From 865f6f91948c66479ad6940a0454c96978afb009 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Wed, 1 Jan 2025 13:10:49 +0100 Subject: [PATCH 01/26] fix scaling bug in spatial plots --- R/visualization.R | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/R/visualization.R b/R/visualization.R index a2ca561..a2eb093 100644 --- a/R/visualization.R +++ b/R/visualization.R @@ -218,7 +218,6 @@ vrSpatialPlotSingle <- function(assay, metadata, group.by = "Sample", plot.segme # plot g <- ggplot() - scale_factors <- 1 # add image and background image <- vrSpatialPlotImage(g, assay, background, scale.image, spatial = spatial, @@ -227,6 +226,7 @@ vrSpatialPlotSingle <- function(assay, metadata, group.by = "Sample", plot.segme info <- image$info background.color <- image$background.color spatial_name <- image$spatial + scale_factors <- image$scale_factors # coords coords <- vrCoordinates(assay, spatial_name = spatial_name, reg = reg) @@ -666,7 +666,6 @@ vrSpatialFeaturePlotSingle <- function(assay, metadata, feature, plot.segments = # plot g <- ggplot() - scale_factors <- 1 # add image and background image <- vrSpatialPlotImage(g, assay, background, scale.image, spatial = spatial, @@ -675,6 +674,7 @@ vrSpatialFeaturePlotSingle <- function(assay, metadata, feature, plot.segments = info <- image$info background.color <- image$background.color spatial_name <- image$spatial + scale_factors <- image$scale_factors # coords coords <- vrCoordinates(assay, spatial_name = spatial_name, reg = reg) @@ -1038,6 +1038,9 @@ vrSpatialFeatureCombinePlot <- function(g, all_data, n.tile, coords, features){ #' @noRd vrSpatialPlotImage <- function(g, assay, background, scale.image, spatial = NULL, channel = NULL, background.color = NULL){ + # parameters + scale_factors <- 1 + # check background, spatial and channel if(!is.null(background)){ warning("The use of 'background' is deprecated, please use 'spatial' to call spatial coordinate systems and 'channel' to get channels of associated images.") @@ -1093,7 +1096,7 @@ vrSpatialPlotImage <- function(g, assay, background, scale.image, spatial = NULL theme(panel.background = element_blank()) } } - list(plot = g, info = info, background.color = background.color, spatial = spatial) + list(plot = g, info = info, background.color = background.color, spatial = spatial, scale_factors = scale_factors) } #' vrSpatialExtent From 40f23329f380ec65d6173f2f5d39fc43b8382336 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Thu, 2 Jan 2025 15:11:47 +0100 Subject: [PATCH 02/26] update niche clustering tutorial and fix a bug in hot spot analysis --- R/spatial.R | 1 + docs/_site.yml | 2 +- docs/conversion.html | 2 +- docs/importingdata.html | 2 +- docs/index.html | 2 +- docs/interactive.Rmd | 2 +- docs/interactive.html | 4 +- docs/moleculeanalysis.html | 2 +- docs/multiomic.html | 2 +- ...{deconvolution.Rmd => nicheclustering.Rmd} | 81 +++++-- ...econvolution.html => nicheclustering.html} | 200 +++++++++++------- docs/ondisk.html | 2 +- docs/pixelanalysis.html | 2 +- docs/registration.html | 2 +- docs/roianalysis.html | 2 +- docs/spotanalysis.html | 2 +- docs/tutorials.Rmd | 2 +- docs/tutorials.html | 4 +- docs/voltronobjects.html | 2 +- 19 files changed, 204 insertions(+), 114 deletions(-) rename docs/{deconvolution.Rmd => nicheclustering.Rmd} (63%) rename docs/{deconvolution.html => nicheclustering.html} (78%) diff --git a/R/spatial.R b/R/spatial.R index 85c1ee4..95a1d1c 100644 --- a/R/spatial.R +++ b/R/spatial.R @@ -355,6 +355,7 @@ getHotSpotAnalysis <- function(object, assay = NULL, method = "Getis-Ord", featu # calculate getis ord n <- length(statistic) + statistic <- statistic - (min(statistic)) ### correct for negative scores, correct for this later getisord_stat <- adj_matrix %*% statistic getisord_stat <- getisord_stat/(sum(statistic) - statistic) getisord[[1]] <- getisord_stat[,1] diff --git a/docs/_site.yml b/docs/_site.yml index e43468c..4e88edf 100644 --- a/docs/_site.yml +++ b/docs/_site.yml @@ -14,7 +14,7 @@ navbar: - text: "Multi-omic Integration" href: multiomic.html - text: "Niche Clustering" - href: deconvolution.html + href: nicheclustering.html - text: "Downstream Analysis" menu: - text: "ROI Analysis" diff --git a/docs/conversion.html b/docs/conversion.html index 29d361e..4736775 100644 --- a/docs/conversion.html +++ b/docs/conversion.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/importingdata.html b/docs/importingdata.html index aefb67d..d1589e6 100644 --- a/docs/importingdata.html +++ b/docs/importingdata.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/index.html b/docs/index.html index 868a921..411881b 100644 --- a/docs/index.html +++ b/docs/index.html @@ -264,7 +264,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/interactive.Rmd b/docs/interactive.Rmd index 365f5db..d0162d7 100644 --- a/docs/interactive.Rmd +++ b/docs/interactive.Rmd @@ -37,7 +37,7 @@ knitr::opts_chunk$set(highlight = TRUE, echo = TRUE) # Interactive Annotation -**VoltRon** includes interactive applications to select and manually label spatial points by drawing polygons and circles. As an example, we will use a Spot-based spatial transcriptomic assay, specifically the **Mouse Brain Serial Section 1/2** datasets, analyzed in the [Niche Clustering](deconvolution.html) tutorial. You can find the already analyzed data stored as a VoltRon object [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/NicheClustering/Visium&Visium_data_decon_analyzed.rds) +**VoltRon** includes interactive applications to select and manually label spatial points by drawing polygons and circles. As an example, we will use a Spot-based spatial transcriptomic assay, specifically the **Mouse Brain Serial Section 1/2** datasets, analyzed in the [Niche Clustering](nicheclustering.html) tutorial. You can find the already analyzed data stored as a VoltRon object [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/NicheClustering/Visium&Visium_data_decon_analyzed.rds) ```{r class.source="watch-out", eval = FALSE} MBrain_Sec <- readRDS("Visium&Visium_data_decon_analyzed.rds") diff --git a/docs/interactive.html b/docs/interactive.html index 21d9c44..e2c0652 100644 --- a/docs/interactive.html +++ b/docs/interactive.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • @@ -464,7 +464,7 @@

    Interactive Annotation

    and manually label spatial points by drawing polygons and circles. As an example, we will use a Spot-based spatial transcriptomic assay, specifically the Mouse Brain Serial Section 1/2 -datasets, analyzed in the Niche +datasets, analyzed in the Niche Clustering tutorial. You can find the already analyzed data stored as a VoltRon object here

    diff --git a/docs/moleculeanalysis.html b/docs/moleculeanalysis.html index ab4e07d..e3e7447 100644 --- a/docs/moleculeanalysis.html +++ b/docs/moleculeanalysis.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/multiomic.html b/docs/multiomic.html index b114bd9..f176827 100644 --- a/docs/multiomic.html +++ b/docs/multiomic.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/deconvolution.Rmd b/docs/nicheclustering.Rmd similarity index 63% rename from docs/deconvolution.Rmd rename to docs/nicheclustering.Rmd index 148104c..10ec0fb 100644 --- a/docs/deconvolution.Rmd +++ b/docs/nicheclustering.Rmd @@ -37,9 +37,9 @@ knitr::opts_chunk$set(highlight = TRUE, echo = TRUE) # Spot-based Niche Clustering -Spot-based spatial transcriptomic assays capture spatially-resolved gene expression profiles that are somewhat closer to single cell resolution. However, each spot still include a few number of cells that are likely from a combination of cell types within the tissue of origin. RNA deconvolution is then incorporated to estimate the percentage/abundance of these cell types within each spot/ROI given a reference scRNAseq dataset. +Spot-based spatial transcriptomic assays capture spatially-resolved gene expression profiles that are somewhat closer to single cell resolution. However, each spot still includes a few number of cells that are likely originated from few number of cell types, hence transcriptomic profile of each spot would likely include markers from multiple cell types. Here, **RNA deconvolution** can be incorporated to estimate the percentage/abundance of cell types for each spot. We use a scRNAseq dataset as a reference to computationally estimate the relative abundance of cell types across across the spots. -VoltRon includes wrapper commands for using popular RNA deconvolution methods such as [RCTD](https://www.nature.com/articles/s41587-021-00830-w) (**spot**) and return estimated abundances as additional assays within each layer. These estimated percentages of cell types each spot could be incorporated to detect **niches** (i.e. small local microenvironments of cells) within the tissue. We can process cell type abundance assays and used them for clustering to detect these niches. +VoltRon includes wrapper commands for using popular spot-level RNA deconvolution methods such as [RCTD](https://www.nature.com/articles/s41587-021-00830-w) and return estimated abundances as additional feature sets within each layer. These estimated percentages of cell types for each spot could be incorporated to detect **niches** (i.e. small local microenvironments of cells) within the tissue. We can process cell type abundance assays and used them for clustering to detect these niches.
    @@ -47,7 +47,7 @@ VoltRon includes wrapper commands for using popular RNA deconvolution methods su For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the [Visium](https://www.10xgenomics.com/products/spatial-gene-expression) instrument. The **Mouse Brain Serial Section 1/2** datasets can be downloaded from [here](https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000) (specifically, please filter for **Species=Mouse**, **AnatomicalEntity=brain**, **Chemistry=v1** and **PipelineVersion=v1.1.0**). -We will now import each of four samples separately and merge them into one VoltRon object. There are four sections in total given two serial anterior and serial posterior sections, hence we have **two tissue blocks each having two layers**. +We will now import each of four samples separately and merge them into one VoltRon object. There are four brain tissue sections in total given two serial anterior and serial posterior sections, hence we have **two tissue blocks each having two layers**. ```{r eval = FALSE, class.source="watch-out"} library(VoltRon) @@ -85,11 +85,16 @@ vrSpatialFeaturePlot(MBrain_Sec, features = "Count", crop = TRUE, alpha = 1, nco ## Import scRNA data -We will now import the scRNA data for reference which can be downloaded from [here](https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1). Specifically, we will use a scRNA reference of Mouse cortical adult brain with 14,000 cells from the Allen Institute, generated with the SMART-Seq2 protocol. This scRNA data is also used by the Spatial Data Analysis tutorial in [Seurat website](https://satijalab.org/seurat/articles/spatial_vignette.html#integration-with-single-cell-data). - -Before deconvoluting Visium spots, we correct cell types labels and drop some cell types with extremely few number of cells (e.g. "CR"). +We will now import the scRNA data for reference which can be downloaded from [here](https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1). Specifically, we will use a scRNA data of Mouse cortical adult brain with 14,000 cells, generated with the SMART-Seq2 protocol, from the Allen Institute. This scRNA data is also used by the Spatial Data Analysis tutorial in [Seurat](https://satijalab.org/seurat/articles/spatial_vignette.html#integration-with-single-cell-data) website. ```{r eval = FALSE, class.source="watch-out"} +# install packages if necessary +if(!requireNamespace("Seurat")) + install.packages("Seurat") +if(!requireNamespace("dplyr")) + install.packages("dplyr") + +# import scRNA data library(Seurat) allen_reference <- readRDS("allen_cortex.rds") @@ -98,7 +103,11 @@ library(dplyr) allen_reference <- SCTransform(allen_reference, ncells = 3000, verbose = FALSE) %>% RunPCA(verbose = FALSE) %>% RunUMAP(dims = 1:30) +``` + +Before deconvoluting Visium spots, we correct cell types labels and drop some cell types with extremely few number of cells (e.g. "CR"). +```{r eval = FALSE, class.source="watch-out"} # update labels and subset allen_reference$subclass <- gsub("L2/3 IT", "L23 IT", allen_reference$subclass) allen_reference <- allen_reference[,colnames(allen_reference)[!allen_reference@meta.data$subclass %in% "CR"]] @@ -117,14 +126,14 @@ gsubclass | gclass ## Spot Deconvolution with RCTD -In order to integrate the scRNA data and the Visium data sets within the VoltRon objects, we will use **RCTD** algorithm which is accessible with the [spacexr](https://github.com/dmcable/spacexr) package. +In order to integrate the scRNA data and the spatial data sets within the VoltRon object and estimate relative cell type abundances for each Visium spot, we will use **RCTD** algorithm which is accessible with the [spacexr](https://github.com/dmcable/spacexr) package. ```{r eval = FALSE, class.source="watch-out"} -if(requireNamespace("spacexr")) +if(!requireNamespace("spacexr")) devtools::install_github("dmcable/spacexr", build_vignettes = FALSE) ``` -After running **getDeconvolution**, an additional assay within the same layer with **_decon** postfix will be created for all layers with a Visium data. +After running **getDeconvolution**, an additional feature set within the same Visium assay with name **Decon** will be created. ```{r eval = FALSE, class.source="watch-out"} library(spacexr) @@ -156,7 +165,7 @@ vrFeatures(MBrain_Sec) [19] "Sncg" "Sst" "Vip" "VLMC" ``` -These features (i.e. cell type abundances) can be visualized like all other feature types. +These features (i.e. cell type abundances) can be visualized like any other feature type. ```{r eval = FALSE, class.source="watch-out"} vrSpatialFeaturePlot(MBrain_Sec, features = c("L4", "L5 PT", "Oligo", "Vip"), @@ -169,17 +178,18 @@ vrSpatialFeaturePlot(MBrain_Sec, features = c("L4", "L5 PT", "Oligo", "Vip"), ## Clustering -Relative cell type abundances that are learned by RCTD and stored within VoltRon can now be used to cluster spots. These groups or clusters of spots can also be realized as **niches**, regions within tissue sections that have a distinct cell type composition compared to the remaining parts of the tissue. +Relative cell type abundances that are learned by RCTD and stored within VoltRon can now be used to cluster spots. These groups or clusters of spots can often be referred to as **niches**. Here, as a definition, a niche is a region or a collection of regions within tissue that have a distinct cell type composition as opposed to the remaining parts of the tissue. -We will now normalize and process relative cell type abundances to learn/detect these niche clusters. We treat cell type abundances as [compositional data](https://en.wikipedia.org/wiki/Compositional_data), hence we incorporate **centred log ratio (CLR)** transformation for normalizing them. +The cell type abundances (which adds up to one for each spot) can be normalized and processed like transcriptomic and proteomic profiles prior to clustering (i.e. niche clustering). We treat cell type abundances as [compositional data](https://en.wikipedia.org/wiki/Compositional_data), hence we incorporate **centred log ratio (CLR)** transformation for normalizing them. ```{r eval = FALSE, class.source="watch-out"} +vrMainFeatureType(MBrain_Sec) <- "Decon" MBrain_Sec <- normalizeData(MBrain_Sec, method = "CLR") ``` -The CLR normalized assay have only 25 features, each representing a cell type from the single cell reference data. Hence, we can **directly calculate UMAP reductions from this feature set** without relying on a prior dimensionality reduction such as PCA. +The CLR normalized assay have only 25 features, each representing a cell type from the single cell reference data. Hence, we can **directly calculate UMAP reductions from this feature abundances** since we dont have much number of features which necessitates dimensionality reduction such as PCA. -VoltRon is also capable of calculating the UMAP reduction from normalized data slots. Hence, we build a UMAP reduction from CLR data. However, UMAP will always be calculated from a PCA reduction by default (if a PCA embedding is found in the object). +However, we may still need to reduce the dimensionality of this space with 25 features using UMAP for visualizing purposes. VoltRon is also capable of calculating the UMAP reduction from normalized data slots. Hence, we build a UMAP reduction from CLR data directly. However, UMAP will always be calculated from a PCA reduction by default (if a PCA embedding is found in the object). ```{r eval = FALSE, class.source="watch-out"} MBrain_Sec <- getUMAP(MBrain_Sec, data.type = "norm") @@ -206,7 +216,7 @@ VoltRon incorporates distinct plotting functions for, e.g. embeddings, coordinat ```{r eval = FALSE, class.source="watch-out"} # visualize g1 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Sample") -g2 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "clusters", label = TRUE) +g2 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "niche_clusters", label = TRUE) g1 | g2 ``` @@ -214,10 +224,10 @@ g1 | g2
    -Mapping clusters on the spatial images and spots would show the niche structure across the tissues and layers. +Mapping clusters on the spatial images and spots would show the niche structure across all four tissue sections. ```{r eval = FALSE, class.source="watch-out"} -vrSpatialPlot(MBrain_Sec, group.by = "clusters", crop = TRUE, alpha = 1) +vrSpatialPlot(MBrain_Sec, group.by = "niche_clusters", crop = TRUE, alpha = 1) ``` @@ -227,8 +237,13 @@ vrSpatialPlot(MBrain_Sec, group.by = "clusters", crop = TRUE, alpha = 1) We use **vrHeatmapPlot** to investigate relative cell type abundances across these niche clusters. You will need to have **ComplexHeatmap** package in your namespace. ```{r eval = FALSE, class.source="watch-out"} +# install packages if necessary +if(!requireNamespace("ComplexHeatmap")) + BiocManager::install("ComplexHeatmap") + +# heatmap of niches library(ComplexHeatmap) -vrHeatmapPlot(MBrain_Sec, features = vrFeatures(MBrain_Sec), group.by = "clusters", +vrHeatmapPlot(MBrain_Sec, features = vrFeatures(MBrain_Sec), group.by = "niche_clusters", show_row_names = T, show_heatmap_legend = T) ``` @@ -237,14 +252,21 @@ vrHeatmapPlot(MBrain_Sec, features = vrFeatures(MBrain_Sec), group.by = "cluster # Cell-based Niche Clustering -Unlike spot-based assays, spatial transcriptomics data in single cell resolution require building niche assays to detect clusters that are equivalent to the niches that each individual cell belongs to. We first detect the mixture of cells around a spatial neighborhood around each of these cells and use that a profile to perform clustering. +Similar to spot-based spatial omics assays, we can build and cluster niche associated to each cell for spatial transcriptomics datasets in single cell resolution. For this, we require building niche assays for the collections of cells where a niche of cell is defined as a region of sets of regions with distinct cell type population that each of these cells belong to. + +Here, we dont require any scRNA reference dataset but we may first need to cluster and annotate cells in the RNA/transcriptome level profiles, and determine cell types. Then, we first detect the mixture of cell types within a spatial neighborhood around all cells and use that as a profile to perform clustering where these clusters will be associated with niches. ## Import Xenium Data -For this, the data has to be already clustered (and annotated if possible). We will use the cluster labels generated at the end of the Xenium analysis section of workflow from [Cell/Spot Analysis](spotanalysis.html). You can download the VoltRon object with clustered and annotated Xenium cells along with the Visium assay from [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/VRBlock_data_clustered.rds). +For this, the data has to be already clustered (and annotated if possible). We will use the cluster labels generated at the end of the Xenium analysis workflow from [Cell/Spot Analysis](spotanalysis.html). You can download the VoltRon object with clustered and annotated Xenium cells along with the Visium assay from [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/VRBlock_data_clustered.rds). ```{r eval = FALSE, class.source="watch-out"} Xen_data <- readRDS("VRBlock_data_clustered.rds") +``` + +We will use all these 18 cell types used for annotating Xenium cells for detecting niches with distinct cellular type mixtures. + +```{r eval = FALSE, class.source="watch-out"} vrMainSpatial(Xen_data, assay = "Assay1") <- "main" vrMainSpatial(Xen_data, assay = "Assay3") <- "main" vrSpatialPlot(Xen_data, group.by = "CellType", pt.size = 0.13, background.color = "black", @@ -257,7 +279,7 @@ vrSpatialPlot(Xen_data, group.by = "CellType", pt.size = 0.13, background.color ## Creating Niche Assay -Here, in order to calculate niche profiles for each cell, we have to first build spatial neighborhoods around cells and capture the local cell type mixtures. Using **getSpatialNeighbors**, we build a spatial neighborhood graph to connect all cells to other cells within at most 15 distance apart +For calculating niche profiles for each cell, we have to first build spatial neighborhoods around cells and capture the local cell type mixtures. Using **getSpatialNeighbors**, we build a spatial neighborhood graph to connect all cells to other cells within at most 15 distance apart. ```{r eval = FALSE, class.source="watch-out"} Xen_data <- getSpatialNeighbors(Xen_data, radius = 15, method = "radius") @@ -294,10 +316,15 @@ vrMainFeatureType(Xen_data) <- "Niche" Xen_data <- normalizeData(Xen_data, method = "CLR") ``` -Default clustering functions could be used to analyze the normalized niche profiles of cells to detect niches associated with each cell. However, we use K-means algorithm to perform the niche clustering. +Default clustering functions could be used to analyze the normalized niche profiles of cells to detect niches associated with each cell. However, we use K-means algorithm to perform the niche clustering. For this exercise, we pick an estimate of 7 clusters which will be the number of niche clusters we get. ```{r eval = FALSE, class.source="watch-out"} Xen_data <- getClusters(Xen_data, nclus = 7, method = "kmeans", label = "niche_clusters") +``` + +After the niche clustering, the metadata is updated and observed later like below. + +```{r eval = FALSE, class.source="watch-out"} head(Metadata(Xen_data)) ``` @@ -313,14 +340,24 @@ head(Metadata(Xen_data)) ## Visualization +After niche clustering, each cell in the Xenium assay will be assigned a niche which is initially a number which indicates the ID of each particular niche. It is up to the user to annotate, filter and visualize these niches moving forward. + ```{r eval = FALSE, class.source="watch-out"} vrSpatialPlot(Xen_data, group.by = "niche_clusters", alpha = 1, legend.loc = "top") ``` +We use **vrHeatmapPlot** to investigate the abundance of each cell type across the niche clusters. You will need to have **ComplexHeatmap** package in your namespace. We see that niche cluster 1 include all invasive tumor subtypes (IT 1-3). We see this for two subtypes of in situ ductal carcinoma (DCIS 1,2) subtypes as well other than a third DCIS subcluster being within proximity to myoepithelial cells. Niche cluster 6 also shows regions within the breast cancer tissue where T cells and B cells are found together abundantly. +
    ```{r eval = FALSE, class.source="watch-out"} +# install packages if necessary +if(!requireNamespace("ComplexHeatmap")) + BiocManager::install("ComplexHeatmap") + +# heatmap of niches +library(ComplexHeatmap) vrHeatmapPlot(Xen_data, features = vrFeatures(Xen_data), group.by = "niche_clusters") ``` diff --git a/docs/deconvolution.html b/docs/nicheclustering.html similarity index 78% rename from docs/deconvolution.html rename to docs/nicheclustering.html index fae6ddd..e6b629c 100644 --- a/docs/deconvolution.html +++ b/docs/nicheclustering.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • @@ -462,19 +462,22 @@

    Niche Clustering

    Spot-based Niche Clustering

    Spot-based spatial transcriptomic assays capture spatially-resolved gene expression profiles that are somewhat closer to single cell -resolution. However, each spot still include a few number of cells that -are likely from a combination of cell types within the tissue of origin. -RNA deconvolution is then incorporated to estimate the -percentage/abundance of these cell types within each spot/ROI given a -reference scRNAseq dataset.

    -

    VoltRon includes wrapper commands for using popular RNA deconvolution -methods such as RCTD -(spot) and return estimated abundances as additional -assays within each layer. These estimated percentages of cell types each -spot could be incorporated to detect niches (i.e. small -local microenvironments of cells) within the tissue. We can process cell -type abundance assays and used them for clustering to detect these +resolution. However, each spot still includes a few number of cells that +are likely originated from few number of cell types, hence +transcriptomic profile of each spot would likely include markers from +multiple cell types. Here, RNA deconvolution can be +incorporated to estimate the percentage/abundance of cell types for each +spot. We use a scRNAseq dataset as a reference to computationally +estimate the relative abundance of cell types across across the +spots.

    +

    VoltRon includes wrapper commands for using popular spot-level RNA +deconvolution methods such as RCTD and +return estimated abundances as additional feature sets within each +layer. These estimated percentages of cell types for each spot could be +incorporated to detect niches (i.e. small local +microenvironments of cells) within the tissue. We can process cell type +abundance assays and used them for clustering to detect these niches.


    @@ -489,9 +492,9 @@

    Import Visium Data

    AnatomicalEntity=brain, Chemistry=v1 and PipelineVersion=v1.1.0).

    We will now import each of four samples separately and merge them -into one VoltRon object. There are four sections in total given two -serial anterior and serial posterior sections, hence we have two -tissue blocks each having two layers.

    +into one VoltRon object. There are four brain tissue sections in total +given two serial anterior and serial posterior sections, hence we have +two tissue blocks each having two layers.

    library(VoltRon)
 Ant_Sec1 <- importVisium("Sagittal_Anterior/Section1/", sample_name = "Anterior1")
 Ant_Sec2 <- importVisium("Sagittal_Anterior/Section2/", sample_name = "Anterior2")
@@ -522,24 +525,30 @@ 
    Import scRNA data
    
 
    We will now import the scRNA data for reference which can be
 downloaded from here.
-Specifically, we will use a scRNA reference of Mouse cortical adult
-brain with 14,000 cells from the Allen Institute, generated with the
-SMART-Seq2 protocol. This scRNA data is also used by the Spatial Data
+Specifically, we will use a scRNA data of Mouse cortical adult brain
+with 14,000 cells, generated with the SMART-Seq2 protocol, from the
+Allen Institute. This scRNA data is also used by the Spatial Data
 Analysis tutorial in Seurat
-website.
    
-
    Before deconvoluting Visium spots, we correct cell types labels and
-drop some cell types with extremely few number of cells (e.g. “CR”).
    
-
    library(Seurat)
+href="https://satijalab.org/seurat/articles/spatial_vignette.html#integration-with-single-cell-data">Seurat
+website.
    
+
    # install packages if necessary
+if(!requireNamespace("Seurat"))
+  install.packages("Seurat")
+if(!requireNamespace("dplyr"))
+  install.packages("dplyr")
+
+# import scRNA data
+library(Seurat)
 allen_reference <- readRDS("allen_cortex.rds")
 
 # process and reduce dimensionality
 library(dplyr)
 allen_reference <- SCTransform(allen_reference, ncells = 3000, verbose = FALSE) %>%
   RunPCA(verbose = FALSE) %>%
-  RunUMAP(dims = 1:30)
-
-# update labels and subset
+  RunUMAP(dims = 1:30)
    
+
    Before deconvoluting Visium spots, we correct cell types labels and
+drop some cell types with extremely few number of cells (e.g. “CR”).
    
+
    # update labels and subset
 allen_reference$subclass <- gsub("L2/3 IT", "L23 IT", allen_reference$subclass)
 allen_reference <- allen_reference[,colnames(allen_reference)[!allen_reference@meta.data$subclass %in% "CR"]]
 
@@ -554,15 +563,16 @@ 
    Import scRNA data

    Spot Deconvolution with RCTD

    -

    In order to integrate the scRNA data and the Visium data sets within -the VoltRon objects, we will use RCTD algorithm which -is accessible with the In order to integrate the scRNA data and the spatial data sets within +the VoltRon object and estimate relative cell type abundances for each +Visium spot, we will use RCTD algorithm which is +accessible with the spacexr package.

    -
    if(requireNamespace("spacexr"))
+
    if(!requireNamespace("spacexr"))
   devtools::install_github("dmcable/spacexr", build_vignettes = FALSE)
    
-
    After running getDeconvolution, an additional assay
-within the same layer with **_decon** postfix will be created for all
-layers with a Visium data.
    
+
    After running getDeconvolution, an additional
+feature set within the same Visium assay with name
+Decon will be created.
    
 
    library(spacexr)
 MBrain_Sec <- getDeconvolution(MBrain_Sec, sc.object = allen_reference, sc.cluster = "subclass", max_cores = 6)
 MBrain_Sec
    
@@ -582,8 +592,8 @@ 
    Spot Deconvolution with RCTD
    
  [7] "L6 CT"      "L6 IT"      "L6b"        "Lamp5"      "Macrophage" "Meis2"     
 [13] "NP"         "Oligo"      "Peri"       "Pvalb"      "Serpinf1"   "SMC"       
 [19] "Sncg"       "Sst"        "Vip"        "VLMC"
    -

    These features (i.e. cell type abundances) can be visualized like all -other feature types.

    +

    These features (i.e. cell type abundances) can be visualized like any +other feature type.

    vrSpatialFeaturePlot(MBrain_Sec, features = c("L4", "L5 PT", "Oligo", "Vip"), 
                      crop = TRUE, ncol = 2, alpha = 1, keep.scale = "all")

    @@ -593,24 +603,30 @@

    Spot Deconvolution with RCTD

    Clustering

    Relative cell type abundances that are learned by RCTD and stored within VoltRon can now be used to cluster spots. These groups or -clusters of spots can also be realized as niches, -regions within tissue sections that have a distinct cell type -composition compared to the remaining parts of the tissue.

    -

    We will now normalize and process relative cell type abundances to -learn/detect these niche clusters. We treat cell type abundances as niches. +Here, as a definition, a niche is a region or a collection of regions +within tissue that have a distinct cell type composition as opposed to +the remaining parts of the tissue.

    +

    The cell type abundances (which adds up to one for each spot) can be +normalized and processed like transcriptomic and proteomic profiles +prior to clustering (i.e. niche clustering). We treat cell type +abundances as compositional data, hence we incorporate centred log ratio (CLR) transformation for normalizing them.

    -
    MBrain_Sec <- normalizeData(MBrain_Sec, method = "CLR")
    +
    vrMainFeatureType(MBrain_Sec) <- "Decon"
+MBrain_Sec <- normalizeData(MBrain_Sec, method = "CLR")

    The CLR normalized assay have only 25 features, each representing a cell type from the single cell reference data. Hence, we can directly calculate UMAP reductions from this feature -set without relying on a prior dimensionality reduction such as -PCA.

    -

    VoltRon is also capable of calculating the UMAP reduction from -normalized data slots. Hence, we build a UMAP reduction from CLR data. -However, UMAP will always be calculated from a PCA reduction by default -(if a PCA embedding is found in the object).

    +abundances since we dont have much number of features which +necessitates dimensionality reduction such as PCA.

    +

    However, we may still need to reduce the dimensionality of this space +with 25 features using UMAP for visualizing purposes. VoltRon is also +capable of calculating the UMAP reduction from normalized data slots. +Hence, we build a UMAP reduction from CLR data directly. However, UMAP +will always be calculated from a PCA reduction by default (if a PCA +embedding is found in the object).

    MBrain_Sec <- getUMAP(MBrain_Sec, data.type = "norm")
 vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Sample")

    @@ -628,20 +644,25 @@

    Visualization

    the clusters we have generated on UMAP embeddings.

    # visualize 
 g1 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Sample")
-g2 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "clusters", label = TRUE)
+g2 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "niche_clusters", label = TRUE)
 g1 | g2


    Mapping clusters on the spatial images and spots would show the niche -structure across the tissues and layers.

    -
    vrSpatialPlot(MBrain_Sec, group.by = "clusters", crop = TRUE, alpha = 1)
    +structure across all four tissue sections.

    +
    vrSpatialPlot(MBrain_Sec, group.by = "niche_clusters", crop = TRUE, alpha = 1)


    We use vrHeatmapPlot to investigate relative cell type abundances across these niche clusters. You will need to have ComplexHeatmap package in your namespace.

    -
    library(ComplexHeatmap)
-vrHeatmapPlot(MBrain_Sec, features = vrFeatures(MBrain_Sec), group.by = "clusters", 
+
    # install packages if necessary
+if(!requireNamespace("ComplexHeatmap"))
+  BiocManager::install("ComplexHeatmap")
+
+# heatmap of niches
+library(ComplexHeatmap)
+vrHeatmapPlot(MBrain_Sec, features = vrFeatures(MBrain_Sec), group.by = "niche_clusters", 
               show_row_names = T, show_heatmap_legend = T)
    
 
    
 
    
@@ -649,22 +670,30 @@ 
    Visualization

    Cell-based Niche Clustering

    -

    Unlike spot-based assays, spatial transcriptomics data in single cell -resolution require building niche assays to detect clusters that are -equivalent to the niches that each individual cell belongs to. We first -detect the mixture of cells around a spatial neighborhood around each of -these cells and use that a profile to perform clustering.

    +

    Similar to spot-based spatial omics assays, we can build and cluster +niche associated to each cell for spatial transcriptomics datasets in +single cell resolution. For this, we require building niche assays for +the collections of cells where a niche of cell is defined as a region of +sets of regions with distinct cell type population that each of these +cells belong to.

    +

    Here, we dont require any scRNA reference dataset but we may first +need to cluster and annotate cells in the RNA/transcriptome level +profiles, and determine cell types. Then, we first detect the mixture of +cell types within a spatial neighborhood around all cells and use that +as a profile to perform clustering where these clusters will be +associated with niches.

    Import Xenium Data

    For this, the data has to be already clustered (and annotated if possible). We will use the cluster labels generated at the end of the -Xenium analysis section of workflow from Cell/Spot Analysis. You can download the -VoltRon object with clustered and annotated Xenium cells along with the -Visium assay from Cell/Spot +Analysis. You can download the VoltRon object with clustered and +annotated Xenium cells along with the Visium assay from here.

    -
    Xen_data <- readRDS("VRBlock_data_clustered.rds")
-vrMainSpatial(Xen_data, assay = "Assay1") <- "main"
+
    Xen_data <- readRDS("VRBlock_data_clustered.rds")
    
+
    We will use all these 18 cell types used for annotating Xenium cells
+for detecting niches with distinct cellular type mixtures.
    
+
    vrMainSpatial(Xen_data, assay = "Assay1") <- "main"
 vrMainSpatial(Xen_data, assay = "Assay3") <- "main"
 vrSpatialPlot(Xen_data, group.by = "CellType", pt.size = 0.13, background.color = "black", 
               legend.loc = "top", n.tile = 500)
    
@@ -673,11 +702,11 @@ 
    Import Xenium Data

    Creating Niche Assay

    -

    Here, in order to calculate niche profiles for each cell, we have to -first build spatial neighborhoods around cells and capture the local -cell type mixtures. Using getSpatialNeighbors, we build -a spatial neighborhood graph to connect all cells to other cells within -at most 15 distance apart

    +

    For calculating niche profiles for each cell, we have to first build +spatial neighborhoods around cells and capture the local cell type +mixtures. Using getSpatialNeighbors, we build a spatial +neighborhood graph to connect all cells to other cells within at most 15 +distance apart.

    Xen_data <- getSpatialNeighbors(Xen_data, radius = 15, method = "radius")
 vrGraphNames(Xen_data)
    [1] "radius"
    @@ -704,9 +733,13 @@

    Clustering

    Xen_data <- normalizeData(Xen_data, method = "CLR")

    Default clustering functions could be used to analyze the normalized niche profiles of cells to detect niches associated with each cell. -However, we use K-means algorithm to perform the niche clustering.

    -
    Xen_data <- getClusters(Xen_data, nclus = 7, method = "kmeans", label = "niche_clusters")
-head(Metadata(Xen_data))
    +However, we use K-means algorithm to perform the niche clustering. For +this exercise, we pick an estimate of 7 clusters which will be the +number of niche clusters we get.

    +
    Xen_data <- getClusters(Xen_data, nclus = 7, method = "kmeans", label = "niche_clusters")
    +

    After the niche clustering, the metadata is updated and observed +later like below.

    +
    head(Metadata(Xen_data))
                   id Count assay_id  Assay    Layer   Sample CellType niche_clusters
 1_Assay1 1_Assay1    28   Assay1 Xenium Section1 10XBlock   DCIS_1              2
@@ -720,10 +753,29 @@ 
    Clustering

    Visualization

    +

    After niche clustering, each cell in the Xenium assay will be +assigned a niche which is initially a number which indicates the ID of +each particular niche. It is up to the user to annotate, filter and +visualize these niches moving forward.

    vrSpatialPlot(Xen_data, group.by = "niche_clusters", alpha = 1, legend.loc = "top")
    +

    We use vrHeatmapPlot to investigate the abundance of +each cell type across the niche clusters. You will need to have +ComplexHeatmap package in your namespace. We see that +niche cluster 1 include all invasive tumor subtypes (IT 1-3). We see +this for two subtypes of in situ ductal carcinoma (DCIS 1,2) subtypes as +well other than a third DCIS subcluster being within proximity to +myoepithelial cells. Niche cluster 6 also shows regions within the +breast cancer tissue where T cells and B cells are found together +abundantly.


    -
    vrHeatmapPlot(Xen_data, features = vrFeatures(Xen_data), group.by = "niche_clusters")
    +
    # install packages if necessary
+if(!requireNamespace("ComplexHeatmap"))
+  BiocManager::install("ComplexHeatmap")
+
+# heatmap of niches
+library(ComplexHeatmap)
+vrHeatmapPlot(Xen_data, features = vrFeatures(Xen_data), group.by = "niche_clusters")


    diff --git a/docs/ondisk.html b/docs/ondisk.html index 85aa4c6..109d087 100644 --- a/docs/ondisk.html +++ b/docs/ondisk.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/pixelanalysis.html b/docs/pixelanalysis.html index b057698..eeadf7d 100644 --- a/docs/pixelanalysis.html +++ b/docs/pixelanalysis.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/registration.html b/docs/registration.html index 7f43bfa..d9be384 100644 --- a/docs/registration.html +++ b/docs/registration.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/roianalysis.html b/docs/roianalysis.html index b1af33b..c8d9bb4 100644 --- a/docs/roianalysis.html +++ b/docs/roianalysis.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/spotanalysis.html b/docs/spotanalysis.html index 31aa299..aa1fe88 100644 --- a/docs/spotanalysis.html +++ b/docs/spotanalysis.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • diff --git a/docs/tutorials.Rmd b/docs/tutorials.Rmd index 5734b1e..1e39899 100644 --- a/docs/tutorials.Rmd +++ b/docs/tutorials.Rmd @@ -72,7 +72,7 @@ In addition, VoltRon provides a number of spatially aware data analysis methods

    Integrating data modalities within or across tissue sections

    - +

    Niche Clustering

    Clustering based on ROI/spot deconvolution and Spatial Neighborhood

    diff --git a/docs/tutorials.html b/docs/tutorials.html index f63198e..d4401ef 100644 --- a/docs/tutorials.html +++ b/docs/tutorials.html @@ -264,7 +264,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • @@ -444,7 +444,7 @@

    Spatially Aware Data Integration and Analysis

    Integrating data modalities within or across tissue sections

    - +

    Niche Clustering

    diff --git a/docs/voltronobjects.html b/docs/voltronobjects.html index 5f4c2b5..ed83e67 100644 --- a/docs/voltronobjects.html +++ b/docs/voltronobjects.html @@ -346,7 +346,7 @@ Multi-omic Integration
  • - Niche Clustering + Niche Clustering
    • From cf440688a08a56cf10bfaf8e4dc1d993b08a9608 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Sun, 5 Jan 2025 00:49:55 +0100 Subject: [PATCH 03/26] fix zarrarray coords bug --- R/io.R | 40 ++++++++++++++++++------------------ tests/testthat/test-ondisk.R | 28 ++++++++++++------------- 2 files changed, 34 insertions(+), 34 deletions(-) diff --git a/R/io.R b/R/io.R index b2e8eca..b25d5e2 100644 --- a/R/io.R +++ b/R/io.R @@ -824,22 +824,21 @@ writeZarrArrayInImage <- function(object, zarr.array <- pizzarr::zarr_open(store = zarr_path) zarr.array$create_group(paste0(name, "/", spat)) - # TODO: check the problem with changing path to zarr arrays - # # write coordinates - # coords <- vrCoordinates(object, spatial_name = spat) - # if(!inherits(coords, c("DelayedArray", "IterableMatrix")) || replace){ - # if(verbose) - # cat(paste0("Writing '", name, "' coordinates \n")) - # coords <- ZarrArray::writeZarrArray(coords, - # zarr_path, - # name = paste0(name, "/", spat, "/coords"), - # chunkdim=chunkdim, - # level=level, - # as.sparse=as.sparse, - # with.dimnames=TRUE, - # verbose=FALSE) - # vrCoordinates(object, spatial_name = spat) <- coords - # } + # write coordinates + coords <- vrCoordinates(object, spatial_name = spat) + if(!inherits(coords, c("DelayedArray", "IterableMatrix")) || replace){ + if(verbose) + cat(paste0("Writing '", name, "' coordinates \n")) + coords <- ZarrArray::writeZarrArray(coords, + zarr_path, + name = paste0(name, "/", spat, "/coords"), + chunkdim=chunkdim, + level=level, + as.sparse=as.sparse, + with.dimnames=TRUE, + verbose=FALSE) + vrCoordinates(object, spatial_name = spat) <- coords + } # for each channel channels <- vrImageChannelNames(object, name = spat) @@ -1094,15 +1093,15 @@ shorten_assay_links_images <- function(object){ spatial_names <- vrSpatialNames(object) for(spat in spatial_names){ - # TODO: check the problem with zarr array paths # coordinates - vrCoordinates(object, spatial_name = spat) <- + # TODO: replace method for vrCoordinates fail with ZarrArray + object@image[[spat]]@coords <- modify_seeds(vrCoordinates(object, spatial_name = spat), function(x) { shorten_assay_links_data(x) - # DelayedArray::path(x) <- basename(DelayedArray::path(x)) }) + # for each channel channels <- vrImageChannelNames(object, name = spat) if(!all(grepl("No Channels", channels))){ @@ -1233,7 +1232,8 @@ restore_absolute_assay_links_images <- function(object, dir){ for(spat in spatial_names){ # coordinates - vrCoordinates(object, spatial_name = spat) <- + # TODO: replace method for vrCoordinates fail with ZarrArray + object@image[[spat]]@coords <- modify_seeds(vrCoordinates(object, spatial_name = spat), function(x) { restore_absolute_links(x, dir) diff --git a/tests/testthat/test-ondisk.R b/tests/testthat/test-ondisk.R index 8163d6b..18aec26 100644 --- a/tests/testthat/test-ondisk.R +++ b/tests/testthat/test-ondisk.R @@ -30,12 +30,12 @@ test_that("save and load on disk", { # TODO: output zarr problem with path # zarr - # xenium_data2 <- saveVoltRon(xenium_data, - # output = output_zarr, - # format = "ZarrVoltRon", - # replace = TRUE, - # verbose = FALSE) - # xenium_data2 <- loadVoltRon(dir = output_zarr) + xenium_data2 <- saveVoltRon(xenium_data, + output = output_zarr, + format = "ZarrVoltRon", + replace = TRUE, + verbose = FALSE) + xenium_data2 <- loadVoltRon(dir = output_zarr) # remove files unlink(output_h5ad, recursive = TRUE) @@ -52,14 +52,14 @@ test_that("double write and merging", { # TODO: output zarr problem with path # write merged data - # xenium_data2 <- xenium_data - # xenium_data2$Sample <- "XeniumR2" - # xenium_data2 <- merge(xenium_data, xenium_data2) - # xenium_data3 <- saveVoltRon(xenium_data2, - # output = output_zarr, - # format = "ZarrVoltRon", - # replace = TRUE, - # verbose = FALSE) + xenium_data2 <- xenium_data + xenium_data2$Sample <- "XeniumR2" + xenium_data2 <- merge(xenium_data, xenium_data2) + xenium_data3 <- saveVoltRon(xenium_data2, + output = output_zarr, + format = "ZarrVoltRon", + replace = TRUE, + verbose = FALSE) # merged ondisk data xenium_data_disk <- saveVoltRon(xenium_data, From e8db9b15a64b7f53699ae5b0489cd1911fe1be1f Mon Sep 17 00:00:00 2001 From: Artur-man Date: Sun, 5 Jan 2025 01:43:12 +0100 Subject: [PATCH 04/26] add github actions for docker image building --- .github/workflows/docker.yml | 44 ++++++++++++++++++++++++++++++++++++ README.md | 1 + 2 files changed, 45 insertions(+) create mode 100644 .github/workflows/docker.yml diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 0000000..0146eae --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,44 @@ +name: Publish Docker image + +on: + push: + branches: + - main + +jobs: + push_to_registry: + name: Push Docker image to Docker Hub + runs-on: ubuntu-latest + if: github.repository == 'BIMSBbioinfo/VoltRon' + + permissions: + packages: write + contents: read + attestations: write + id-token: write + + steps: + - name: Check out the repo + uses: actions/checkout@v4 + + - name: Log in to Docker Hub + uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a + with: + username: ${{ secrets.DOCKER_USERNAME }} + password: ${{ secrets.DOCKER_PASSWORD }} + + - name: Extract metadata (tags, labels) for Docker + id: meta + uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 + with: + images: amanukyan1385/rstudio-voltron + + - name: Build and push Docker image + id: push + uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671 + with: + context: . + file: ./inst/extdata/Dockerfile + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} \ No newline at end of file diff --git a/README.md b/README.md index 2b2516e..8bf2212 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ [![R-CMD-CHECK](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/check.yml/badge.svg)](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/check.yml) [![DOI:10.1101/2023.12.15.571667](https://zenodo.org/badge/DOI/10.1101/2023.12.15.571667-x.svg)](https://doi.org/10.1101/2023.12.15.571667) +[![Publish Docker image](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/docker.yml/badge.svg)](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/docker.yml) ----- From ff879794e0008e7367e4761f96ea1c820dcc8d6d Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 6 Jan 2025 01:01:19 +0100 Subject: [PATCH 05/26] update tutorial page image sizes --- docs/tutorials.Rmd | 57 ++++++++++++++++--------------- docs/tutorials.html | 81 +++++++++++++++++++++++++++++---------------- 2 files changed, 81 insertions(+), 57 deletions(-) diff --git a/docs/tutorials.Rmd b/docs/tutorials.Rmd index 1e39899..a2c653b 100644 --- a/docs/tutorials.Rmd +++ b/docs/tutorials.Rmd @@ -32,6 +32,10 @@ td:hover, td:active{ margin-left: auto; margin-right: auto; } + + + + p.tutorial { text-decoration: none!important; font-size: 1.5em; @@ -64,18 +68,18 @@ In addition, VoltRon provides a number of spatially aware data analysis methods

    Spatial Data Alignment

    - -

    Automated and manual alignment of spatial data assays

    +
    +

    Automated and manual alignment of spatial data assays

    Multi-omic Integration

    - -

    Integrating data modalities within or across tissue sections

    +
    +

    Integrating data modalities within or across tissue sections

    Niche Clustering

    - -

    Clustering based on ROI/spot deconvolution and Spatial Neighborhood

    +
    +

    Clustering based on ROI/spot deconvolution and Spatial Neighborhood

    @@ -94,27 +98,26 @@ In addition, VoltRon provides a number of spatially aware data analysis methods

    Region of Interests (ROIs)

    - -

    Quality control, analysis and visualization of ROI segments

    +
    +

    Quality control, analysis and visualization of ROI segments

    Cells/Spots

    - -

    Quality control, analysis and visualization of Cell/Spot datasets

    +
    +

    Quality control, analysis and visualization of Cell/Spot datasets

    Molecules


    - -
    -

    Analysis and visualization of Molecule datasets
    (Under Development)

    +
    +

    Analysis and visualization of Molecule datasets
    (Under Development)

    Pixels (Image Only)

    - -

    Analysis and visualization of Image datasets
    (Under Development)

    +
    +

    Analysis and visualization of Image datasets
    (Under Development)

    @@ -136,39 +139,35 @@ Here, we provide a group of tutorials to use additional features of the VoltRon

    Interactive Utilities

    - -

    Interactive annotation and visualization

    +
    +
    +

    Interactive annotation and visualization

    Working with VoltRon Objects


    - +


    Manipulating and configuring VoltRon objects

    Importing Spatial Data

    - -

    Importing readouts of spatial technologies

    +
    +

    Importing readouts of spatial technologies

    Converting VoltRon Objects


    - -
    -
    -

    Converting VoltRon objects into Seurat, SpatialExperiment and Squidpy (anndata) etc.

    +
    +

    Converting VoltRon objects into Seurat, SpatialExperiment and Squidpy (anndata) etc.

    OnDisk Analysis Utilities

    -
    - -
    -
    -

    Efficient access to large VoltRon objects

    +
    +

    Efficient access to large VoltRon objects

    diff --git a/docs/tutorials.html b/docs/tutorials.html index d4401ef..59002a9 100644 --- a/docs/tutorials.html +++ b/docs/tutorials.html @@ -387,6 +387,10 @@

    Tutorials

    margin-left: auto; margin-right: auto; } + + + + p.tutorial { text-decoration: none!important; font-size: 1.5em; @@ -430,8 +434,10 @@

    Spatially Aware Data Integration and Analysis

    Spatial Data Alignment

    - -

    +

    + +
    +

    Automated and manual alignment of spatial data assays

    @@ -439,8 +445,10 @@

    Spatially Aware Data Integration and Analysis

    Multi-omic Integration

    - -

    +

    + +
    +

    Integrating data modalities within or across tissue sections

    @@ -448,8 +456,10 @@

    Spatially Aware Data Integration and Analysis

    Niche Clustering

    - -

    +

    + +
    +

    Clustering based on ROI/spot deconvolution and Spatial Neighborhood

    @@ -475,8 +485,10 @@

    Additional Downstream Analysis

    Region of Interests (ROIs)

    - -

    +

    + +
    +

    Quality control, analysis and visualization of ROI segments

    @@ -484,8 +496,10 @@

    Additional Downstream Analysis

    Cells/Spots

    - -

    +

    + +
    +

    Quality control, analysis and visualization of Cell/Spot datasets

    @@ -494,9 +508,10 @@

    Additional Downstream Analysis

    Molecules


    - -
    -

    +

    + +
    +

    Analysis and visualization of Molecule datasets
    (Under Development)

    @@ -507,8 +522,10 @@

    Additional Downstream Analysis

    Pixels (Image Only)

    - -

    +

    + +
    +

    Analysis and visualization of Image datasets
    (Under Development)

    @@ -542,8 +559,11 @@

    Other Utilities

    Interactive Utilities

    - -

    +
    +

    + +
    +

    Interactive annotation and visualization

    @@ -552,7 +572,9 @@

    Other Utilities

    Working with VoltRon Objects


    - +
    + +


    Manipulating and configuring VoltRon objects @@ -562,9 +584,11 @@

    Other Utilities

    Importing Spatial Data

    - -

    -Importing readouts of spatial technologies
    +

    + +
    +

    +Importing readouts of spatial technologies

    @@ -574,9 +598,10 @@

    Other Utilities

    Converting VoltRon Objects


    - -

    -

    +

    + +
    +

    Converting VoltRon objects into Seurat, SpatialExperiment and Squidpy (anndata) etc.

    @@ -585,10 +610,10 @@

    Other Utilities

    OnDisk Analysis Utilities

    -
    - -

    -

    +

    + +
    +

    Efficient access to large VoltRon objects

    From 11a508f77fe9080e85893dd4e8928716988d9499 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 6 Jan 2025 01:31:56 +0100 Subject: [PATCH 06/26] add instructions for docker image and docker desktop --- README.md | 12 ++++++++++++ inst/extdata/docker_desktop_instructions.md | 19 +++++++++++++++++++ 2 files changed, 31 insertions(+) create mode 100644 inst/extdata/docker_desktop_instructions.md diff --git a/README.md b/README.md index 8bf2212..879f73a 100644 --- a/README.md +++ b/README.md @@ -81,6 +81,18 @@ On **Fedora** you may need [`opencv-devel`](https://src.fedoraproject.org/rpms/o yum install opencv-devel ``` +## Docker Hub + +You can also run VoltRon from a container already available in [Docker Hub](https://hub.docker.com/repository/docker/amanukyan1385/rstudio-voltron/general). The docker image is based on the [Rocker Project](https://rocker-project.org/) and can be run from the terminal like below: + +``` +docker run --rm -ti -e PASSWORD= -p 8787:8787 amanukyan1385/rstudio-voltron:main +``` + +Then, start the RStudio session from the browser at `http://localhost:8787/` and enter `rstudio` as username and `` as password. + +See [here](https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/docker_desktop_instructions.md) for more instructions on how to run the container using [Docker Desktop](https://www.docker.com/products/docker-desktop/). + ## Tutorials Please see the [Explore](https://artur-man.github.io/VoltRon/tutorials.html) section in the VoltRon website for tutorials, example scripts and analysis found in the [preprint](https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1). Tutorials include links for accessing necessary data to run scripts across all tutorials. diff --git a/inst/extdata/docker_desktop_instructions.md b/inst/extdata/docker_desktop_instructions.md new file mode 100644 index 0000000..75148a2 --- /dev/null +++ b/inst/extdata/docker_desktop_instructions.md @@ -0,0 +1,19 @@ +## Docker Desktop Instructions + +1. Install Docker Desktop application from [https://www.docker.com/products/docker-desktop/](https://www.docker.com/products/docker-desktop/). + +2. Pull the docker image from Docker Hub using the following command on the terminal: + +``` +docker pull amanukyan1385/rstudio-voltron:main +``` + +3. Open Docker Deskop app and go to `Images` + +4. Find `amanukyan1385/rstudio-voltron:main` and click `Run` on the right. + +5. Enter `8787` to Ports, `PASSWORD` to `Variable` and `yourpassword` (you can also give a custom password) to `Value` under Environment variables. + +6. Click `Run` + +7. Start the RStudio session from the browser at `http://localhost:8787/` and enter `rstudio` as username and `yourpassword` as password. From 1cd0b4c9bc8ec08e83276216039a2238383caa6d Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 6 Jan 2025 10:55:40 +0100 Subject: [PATCH 07/26] update README --- README.md | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 879f73a..66939e3 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ +[![R-CMD-CHECK](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/check.yml/badge.svg)](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/check.yml) +[![DOI:10.1101/2023.12.15.571667](https://zenodo.org/badge/DOI/10.1101/2023.12.15.571667-x.svg)](https://doi.org/10.1101/2023.12.15.571667) +[![Publish Docker image](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/docker.yml/badge.svg)](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/docker.yml) + # VoltRon ### Spatial omic analysis toolbox for multi-resolution and multi-omic integration using image registration -**Citation:** Manukyan, A., Bahry, E., Wyler, E. et al. ... & Akalin, A. (2023). [VoltRon: A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics Integration using Image Registration](https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1). bioRxiv, 2023-12. - -[![R-CMD-CHECK](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/check.yml/badge.svg)](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/check.yml) -[![DOI:10.1101/2023.12.15.571667](https://zenodo.org/badge/DOI/10.1101/2023.12.15.571667-x.svg)](https://doi.org/10.1101/2023.12.15.571667) -[![Publish Docker image](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/docker.yml/badge.svg)](https://github.com/BIMSBbioinfo/VoltRon/actions/workflows/docker.yml) +### [Website](https://bioinformatics.mdc-berlin.de/VoltRon/) | [Tutorials](https://bioinformatics.mdc-berlin.de/VoltRon/tutorials.html) | [Preprint](https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1) ----- @@ -14,8 +14,6 @@
    -**Website and Tutorials**: https://bioinformatics.mdc-berlin.de/VoltRon - **VoltRon** is a spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is also capable of analyzing multiple types of spatially-aware data modalities.
      @@ -34,18 +32,18 @@

    • Spatially Aware Analysis allows detecting spatial patterns across cells, spots, molecules and other entities.

        -
      • +
      • (Niche Clustering: Spots) VoltRon allows integration to single cell RNA datasets using Seurat, SingleCellExperiment and spacexr for spot deconvolution. Estimated cell type abundances are then used to cluster spots into groups of cell type niches which are defined as spots with distinct composition of cell types.
      • (Niche Clustering: Cells) VoltRon creates spatial neighborhoods around cells to cluster local cellular compositions around all cells which in turn informs users on cell types that are likely within proximity to each other.
        • -
      • +
      • (Hot Spot Detection) VoltRon detects region of locally spatial patterns of cells/molecules/spots that are abundant in biological events and/or features.
      • -
    • +

    • Support for Big Data for VoltRon objects enables storing large feature data matrices and large microscopic images of tissues on disk without overloading memory, thus allowing analysis on large datasets with ease. VoltRon stores large images as pyramid structures to speed up visualization and data retrieval.

    • From 7e368b89eb8cc00ae6e697e7252922cd78651a00 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 6 Jan 2025 15:37:32 +0100 Subject: [PATCH 08/26] update README --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 66939e3..e731c37 100644 --- a/README.md +++ b/README.md @@ -91,10 +91,6 @@ Then, start the RStudio session from the browser at `http://localhost:8787/` and See [here](https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/docker_desktop_instructions.md) for more instructions on how to run the container using [Docker Desktop](https://www.docker.com/products/docker-desktop/). -## Tutorials - -Please see the [Explore](https://artur-man.github.io/VoltRon/tutorials.html) section in the VoltRon website for tutorials, example scripts and analysis found in the [preprint](https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1). Tutorials include links for accessing necessary data to run scripts across all tutorials. - ## References Manukyan, A., Bahry, E., Wyler, E., Becher, E., Pascual-Reguant, A., Plumbom, I., ... & Akalin, A. (2023). [VoltRon: A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics Integration using Image Registration](https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1). bioRxiv, 2023-12. From d414a8f60b3a1edb8f7d82173e555a47a377dee7 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Wed, 8 Jan 2025 11:10:01 +0100 Subject: [PATCH 09/26] update tutorials --- docs/interactive.Rmd | 4 ---- docs/interactive.html | 2 -- docs/multiomic.Rmd | 11 ++--------- docs/multiomic.html | 9 ++------- docs/nicheclustering.Rmd | 5 ++++- docs/nicheclustering.html | 7 +++++-- docs/spotanalysis.Rmd | 4 +++- docs/spotanalysis.html | 7 +++++-- 8 files changed, 21 insertions(+), 28 deletions(-) diff --git a/docs/interactive.Rmd b/docs/interactive.Rmd index d0162d7..b384f7c 100644 --- a/docs/interactive.Rmd +++ b/docs/interactive.Rmd @@ -43,8 +43,6 @@ knitr::opts_chunk$set(highlight = TRUE, echo = TRUE) MBrain_Sec <- readRDS("Visium&Visium_data_decon_analyzed.rds") ``` -
      - We can start annotating the spatial assay. By passing arguments used by the **vrSpatialPlot** function to visualize labels (e.g. clusters), we can better select regions within tissue sections for annotation. ```{r class.source="watch-out", eval = FALSE} @@ -181,8 +179,6 @@ To use **TissUUmaps** for interactive investigation of your spatial omic data, w as.AnnData(xenium_data, file = "xenium_data.h5ad", assay = "Xenium", flip_coordinates = TRUE) ``` -
      - To use **TissUUmaps**, you can follow the instructions [here](https://tissuumaps.github.io/installation/). Once installed and executed, simply drag and drop the h5ad file to the main panel of the application diff --git a/docs/interactive.html b/docs/interactive.html index e2c0652..0080f5a 100644 --- a/docs/interactive.html +++ b/docs/interactive.html @@ -469,7 +469,6 @@

      Interactive Annotation

      as a VoltRon object here

      MBrain_Sec <- readRDS("Visium&Visium_data_decon_analyzed.rds")
      -


      We can start annotating the spatial assay. By passing arguments used by the vrSpatialPlot function to visualize labels (e.g. clusters), we can better select regions within tissue sections for @@ -590,7 +589,6 @@

      TissUUmaps

      of cells vertically, hence match it with the top to bottom system of the background DAPI image.

      as.AnnData(xenium_data, file = "xenium_data.h5ad", assay = "Xenium", flip_coordinates = TRUE)
      -


      To use TissUUmaps, you can follow the instructions here. Once installed and executed, simply drag and drop the h5ad file to the main diff --git a/docs/multiomic.Rmd b/docs/multiomic.Rmd index 515d5bc..a7d724b 100644 --- a/docs/multiomic.Rmd +++ b/docs/multiomic.Rmd @@ -86,7 +86,7 @@ We incorporate two open reading frames (ORFs), named **S2_N** and **S2_orf1ab**, ```{r eval = FALSE, class.source="watch-out"} vrSpatialPlot(vr2_merged_acute1, assay = "Xenium_mol", group.by = "gene", - group.ids = c("S2_N", "S2_orf1ab"), n.tile = 300) + group.ids = c("S2_N", "S2_orf1ab"), n.tile = 500) ``` @@ -146,7 +146,7 @@ We now select the feature type and graph name to run an hot spot analysis which ```{r eval = FALSE, class.source="watch-out"} vr2_merged_acute1 <- getHotSpotAnalysis(vr2_merged_acute1, assay = "Xenium_mol", features = "gene", graph.type = "radius") -vrSpatialPlot(vr2_merged_acute1_2, assay = "Xenium_mol", +vrSpatialPlot(vr2_merged_acute1, assay = "Xenium_mol", group.by = "gene_hotspot_flag", group.ids = c("cold", "hot"), alpha = 1) ``` @@ -241,13 +241,6 @@ as.AnnData(vr2_merged_acute1, assay = "Xenium", file = "vr2_merged_acute1.h5ad", flip_coordinates = TRUE, name = "main", channel = "H&E") ``` - - - - - -
      - To run TissUUmaps please follow installation instructions [here](https://tissuumaps.github.io/installation/), then you can simply drag and drop both the h5ad file and png file to the application. diff --git a/docs/multiomic.html b/docs/multiomic.html index f176827..7dee559 100644 --- a/docs/multiomic.html +++ b/docs/multiomic.html @@ -521,7 +521,7 @@

      Single Cells and Molecules

      visualize again tile the count of these virus expressions by specifically selecting these two ORFs.

      vrSpatialPlot(vr2_merged_acute1, assay = "Xenium_mol", group.by = "gene", 
-              group.ids = c("S2_N", "S2_orf1ab"), n.tile = 300)
      + group.ids = c("S2_N", "S2_orf1ab"), n.tile = 500)


      We can even zoom into specific locations at the tissue to investigate @@ -562,7 +562,7 @@

      Hot Spot Analysis

      in other virus molecules.

      vr2_merged_acute1 <- getHotSpotAnalysis(vr2_merged_acute1, assay = "Xenium_mol", 
                                         features = "gene", graph.type = "radius")
-vrSpatialPlot(vr2_merged_acute1_2, assay = "Xenium_mol", 
+vrSpatialPlot(vr2_merged_acute1, assay = "Xenium_mol", 
               group.by = "gene_hotspot_flag", group.ids = c("cold", "hot"), alpha = 1)

    @@ -657,11 +657,6 @@

    Interactive Visualization

    # convert VoltRon object to h5ad
 as.AnnData(vr2_merged_acute1, assay = "Xenium", file = "vr2_merged_acute1.h5ad", 
            flip_coordinates = TRUE, name = "main", channel = "H&E")
    - - - - -


    To run TissUUmaps please follow installation instructions here, then you can simply drag and drop both the h5ad file and png file to the diff --git a/docs/nicheclustering.Rmd b/docs/nicheclustering.Rmd index 10ec0fb..bcdff51 100644 --- a/docs/nicheclustering.Rmd +++ b/docs/nicheclustering.Rmd @@ -45,7 +45,10 @@ VoltRon includes wrapper commands for using popular spot-level RNA deconvolution ## Import Visium Data -For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the [Visium](https://www.10xgenomics.com/products/spatial-gene-expression) instrument. The **Mouse Brain Serial Section 1/2** datasets can be downloaded from [here](https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000) (specifically, please filter for **Species=Mouse**, **AnatomicalEntity=brain**, **Chemistry=v1** and **PipelineVersion=v1.1.0**). +For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the [Visium](https://www.10xgenomics.com/products/spatial-gene-expression) instrument. + + +You can find and download readouts of all four Visium sections [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Visium/MouseBrainSerialSections.zip). The **Mouse Brain Serial Section 1/2** datasets can be also downloaded from [here](https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000) (specifically, please filter for **Species=Mouse**, **AnatomicalEntity=brain**, **Chemistry=v1** and **PipelineVersion=v1.1.0**). We will now import each of four samples separately and merge them into one VoltRon object. There are four brain tissue sections in total given two serial anterior and serial posterior sections, hence we have **two tissue blocks each having two layers**. diff --git a/docs/nicheclustering.html b/docs/nicheclustering.html index e6b629c..058e7e7 100644 --- a/docs/nicheclustering.html +++ b/docs/nicheclustering.html @@ -485,8 +485,11 @@

    Import Visium Data

    For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the Visium -instrument. The Mouse Brain Serial Section 1/2 datasets -can be downloaded from +

    You can find and download readouts of all four Visium sections here. +The Mouse Brain Serial Section 1/2 datasets can be also +downloaded from here (specifically, please filter for Species=Mouse, AnatomicalEntity=brain, Chemistry=v1 diff --git a/docs/spotanalysis.Rmd b/docs/spotanalysis.Rmd index dee3a5e..d6fd440 100644 --- a/docs/spotanalysis.Rmd +++ b/docs/spotanalysis.Rmd @@ -234,7 +234,9 @@ Spot-based spatial transcriptomic assays capture spatially-resolved gene express ## Import ST Data -For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the [Visium](https://www.10xgenomics.com/products/spatial-gene-expression) instrument. The **Mouse Brain Serial Section 1** datasets can be downloaded from [here](https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000) (specifically, please filter for **Species=Mouse**, **AnatomicalEntity=brain**, **Chemistry=v1** and **PipelineVersion=v1.1.0**). +For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the [Visium](https://www.10xgenomics.com/products/spatial-gene-expression) instrument. + +You can find and download readouts of all four Visium sections [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Visium/MouseBrainSerialSections.zip). The **Mouse Brain Serial Section 1** datasets can be downloaded from [here](https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000) (specifically, please filter for **Species=Mouse**, **AnatomicalEntity=brain**, **Chemistry=v1** and **PipelineVersion=v1.1.0**). We will now import each of four samples separately and merge them into one VoltRon object. There are four sections in total given two serial anterior and serial posterior sections, hence we have **two tissue blocks each having two layers**. diff --git a/docs/spotanalysis.html b/docs/spotanalysis.html index aa1fe88..d98beb1 100644 --- a/docs/spotanalysis.html +++ b/docs/spotanalysis.html @@ -671,8 +671,11 @@

    Import ST Data

    For this tutorial we will analyze spot-based transcriptomic assays from Mouse Brain generated by the Visium -instrument. The Mouse Brain Serial Section 1 datasets -can be downloaded from +

    You can find and download readouts of all four Visium sections here. +The Mouse Brain Serial Section 1 datasets can be +downloaded from here (specifically, please filter for Species=Mouse, AnatomicalEntity=brain, Chemistry=v1 From 4ccf8665687439f2013d5401f6a687b938f6a636 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Wed, 8 Jan 2025 16:03:15 +0100 Subject: [PATCH 10/26] optimize new graph replace method --- NAMESPACE | 2 ++ R/objects.R | 37 +++++++++++++++++++++-------------- R/spatial.R | 24 +++++++++++++---------- R/visualization.R | 21 ++++++++++---------- docs/multiomic.Rmd | 3 ++- man/vrGraph.Rd | 2 +- man/vrSpatialPlot.Rd | 3 +++ tests/testthat/test-graph.R | 34 ++++++++++++++++++++++++++++++++ tests/testthat/test-spatial.R | 24 +++++++++++++++++++++++ 9 files changed, 113 insertions(+), 37 deletions(-) create mode 100644 tests/testthat/test-graph.R diff --git a/NAMESPACE b/NAMESPACE index bf5da59..7610aca 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -302,6 +302,7 @@ importFrom(data.table,chmatch) importFrom(data.table,data.table) importFrom(data.table,fread) importFrom(data.table,is.data.table) +importFrom(data.table,melt) importFrom(data.table,set) importFrom(data.table,setkey) importFrom(dplyr,"%>%") @@ -351,6 +352,7 @@ importFrom(igraph,E) importFrom(igraph,V) importFrom(igraph,add_edges) importFrom(igraph,as_adjacency_matrix) +importFrom(igraph,as_data_frame) importFrom(igraph,cluster_leiden) importFrom(igraph,components) importFrom(igraph,disjoint_union) diff --git a/R/objects.R b/R/objects.R index e790d30..525d8c2 100644 --- a/R/objects.R +++ b/R/objects.R @@ -1820,7 +1820,8 @@ flipCoordinates.VoltRon <- function(object, assay = NULL, image_name = NULL, spa #' #' @rdname vrGraph #' -#' @importFrom igraph induced_subgraph +#' @importFrom igraph induced_subgraph V +#' #' @export vrGraph <- function(object, assay = NULL, graph.type = NULL) { @@ -1846,6 +1847,7 @@ vrGraph <- function(object, assay = NULL, graph.type = NULL) { # return graph if(length(vrGraphNames(object)) > 0){ + node_names <- intersect(igraph::V(object@graph[[graph.type]])$name, node_names) returngraph <- igraph::induced_subgraph(object@graph[[graph.type]], node_names) return(returngraph) } else { @@ -1858,32 +1860,37 @@ vrGraph <- function(object, assay = NULL, graph.type = NULL) { #' #' @rdname vrGraph #' -#' @importFrom igraph disjoint_union induced_subgraph +#' @importFrom igraph disjoint_union induced_subgraph V #' @export -"vrGraph<-" <- function(object, graph.type = "kNN", value) { +"vrGraph<-" <- function(object, assay = NULL, graph.type = "kNN", value) { # check value if(!inherits(value, "igraph")) stop("The 'value' should be of an igraph class!") - # all vertices - spobject <- vrSpatialPoints(object) + # get assay names + assay_names <- vrAssayNames(object, assay = assay) + spobject <- vrSpatialPoints(object, assay = assay_names) # check if there exists graphs graph <- object@graph if(length(names(object@graph)) == 0 || !graph.type %in% names(object@graph)){ - graph[[graph.type]] <- make_empty_graph(directed = FALSE) + vertices(spobject) + + # graph[[graph.type]] <- make_empty_graph(directed = FALSE) + vertices(spobject) + graph[[graph.type]] <- value + + } else { + + # vertices + new_vert <- igraph::V(value)$name + + # edges + subg_inv <- igraph::induced_subgraph(graph[[graph.type]], spobject[!spobject%in%new_vert]) + graph[[graph.type]] <- igraph::disjoint_union(value, subg_inv) } - - # vertices - new_vert <- V(value)$name - - # edges - subg_inv <- igraph::induced_subgraph(graph[[graph.type]], spobject[!spobject%in%new_vert]) - graph[[graph.type]] <- igraph::disjoint_union(value, subg_inv) - + # update object - object@graph <- graph + object@graph <- graph # return return(object) diff --git a/R/spatial.R b/R/spatial.R index 95a1d1c..ecc8726 100644 --- a/R/spatial.R +++ b/R/spatial.R @@ -23,7 +23,7 @@ NULL #' @importFrom igraph add_edges simplify make_empty_graph vertices #' @importFrom RCDT delaunay #' @importFrom RANN nn2 -#' @importFrom reshape2 melt +#' @importFrom data.table melt #' @importFrom stats dist #' #' @export @@ -43,11 +43,11 @@ getSpatialNeighbors <- function(object, assay_names <- vrAssayNames(object, assay = assay) # get assay connectivity - assay_names <- getBlockConnectivity(object, assay = assay_names) + assay_names_connected <- getBlockConnectivity(object, assay = assay_names) # get spatial edges spatialedges_list <- list() - for(assy in assay_names){ + for(assy in assay_names_connected){ # get coordinates cur_coords <- as.matrix(vrCoordinates(object, assay = assy)) @@ -100,9 +100,11 @@ getSpatialNeighbors <- function(object, # nnedges <- RANN::nn2(cur_coords, k = k + 1) nnedges <- knn_annoy(cur_coords, k = k + 1) names(nnedges) <- c("nn.index", "nn.dist") - nnedges <- nnedges$nn.index - nnedges <- reshape2::melt(data.frame(nnedges), id.vars = "X1") - nnedges <- subset(nnedges[,c("X1", "value")], value != 0 & X1 != 0) + # nnedges <- nnedges$nn.index + # nnedges <- reshape2::melt(data.frame(nnedges), id.vars = "X1") + # nnedges <- subset(nnedges[,c("X1", "value")], value != 0 & X1 != 0) + nnedges <- data.table::melt(data.table::data.table(nnedges$nn.index), id.vars = "V1") + nnedges <- nnedges[,c("V1", "value")][V1 > 0 & value > 0] nnedges <- as.vector(t(as.matrix(nnedges))) nnedges <- rownames(cur_coords)[nnedges] nnedges @@ -113,9 +115,11 @@ getSpatialNeighbors <- function(object, radius <- ifelse(is.null(spot.radius), 1, spot.radius) } nnedges <- suppressWarnings({RANN::nn2(cur_coords, searchtype = "radius", radius = radius, k = min(300, sqrt(nrow(cur_coords))/2))}) - nnedges <- nnedges$nn.idx - nnedges <- reshape2::melt(data.frame(nnedges), id.vars = "X1") - nnedges <- subset(nnedges[,c("X1", "value")], value != 0 & X1 != 0) + # nnedges <- nnedges$nn.idx + # nnedges <- reshape2::melt(data.frame(nnedges), id.vars = "X1") + nnedges <- data.table::melt(data.table::data.table(nnedges$nn.idx), id.vars = "V1") + # nnedges <- subset(nnedges[,c("X1", "value")], value != 0 & X1 != 0) + nnedges <- nnedges[,c("V1", "value")][V1 > 0 & value > 0] nnedges <- as.vector(t(as.matrix(nnedges))) nnedges <- rownames(cur_coords)[nnedges] nnedges @@ -128,7 +132,7 @@ getSpatialNeighbors <- function(object, graph <- make_empty_graph(directed = FALSE) + vertices(spatialpoints) graph <- add_edges(graph, edges = spatialedges) graph <- simplify(graph, remove.multiple = TRUE, remove.loops = FALSE) - vrGraph(object, graph.type = graph.key) <- graph + vrGraph(object, assay = assay_names, graph.type = graph.key) <- graph # return return(object) diff --git a/R/visualization.R b/R/visualization.R index a2eb093..82b1cd4 100644 --- a/R/visualization.R +++ b/R/visualization.R @@ -22,6 +22,7 @@ NULL #' @param assay assay name (exp: Assay1) or assay class (exp: Visium, Xenium), see \link{SampleMetadata}. #' if NULL, the default assay will be used, see \link{vrMainAssay}. #' @param graph.name if not NULL, the spatial graph is with name \code{graph.name} is visualized as well, see \link{vrGraphNames} +#' @param graph.edge.color the colors of the graph edges, if \code{graph.name} is not NULL. #' @param reduction used by \code{vrSpatialPlotVitessce} to visualize an embedding alongside with the spatial plot. #' @param ncol column wise number of plots, for \link{ggarrange} #' @param nrow row wise number of plots, for \link{ggarrange} @@ -52,7 +53,7 @@ NULL #' #' @export vrSpatialPlot <- function(object, group.by = "Sample", plot.segments = FALSE, group.ids = NULL, colors = NULL, n.tile = 0, - assay = NULL, graph.name = NULL, reduction = NULL, ncol = 2, nrow = NULL, font.size = 2, pt.size = 2, + assay = NULL, graph.name = NULL, graph.edge.color = "orange", reduction = NULL, ncol = 2, nrow = NULL, font.size = 2, pt.size = 2, cell.shape = 21, alpha = 1, label = FALSE, spatial = NULL, channel = NULL, background.color = NULL, background = NULL, reg = FALSE, crop = FALSE, legend.pt.size = 2, legend.text.size = 14, scale.image = TRUE, legend.loc = "right", common.legend = TRUE, collapse.plots = TRUE, interactive = FALSE, @@ -155,8 +156,8 @@ vrSpatialPlot <- function(object, group.by = "Sample", plot.segments = FALSE, gr # visualize p_title <- plot_title[[assy]] gg[[i]] <- vrSpatialPlotSingle(assay = cur_assay, metadata = cur_metadata, - group.by = group.by, plot.segments = plot.segments, group.ids = group.ids, colors = colors, n.tile = n.tile, graph = graph, font.size = font.size, pt.size = pt.size, - alpha = alpha, cell.shape = cell.shape, plot_title = p_title, background = background, spatial = spatial, channel = channel, background.color = background.color, reg = reg, + group.by = group.by, plot.segments = plot.segments, group.ids = group.ids, colors = colors, n.tile = n.tile, graph = graph, graph.edge.color = graph.edge.color, font.size = font.size, + pt.size = pt.size, alpha = alpha, cell.shape = cell.shape, plot_title = p_title, background = background, spatial = spatial, channel = channel, background.color = background.color, reg = reg, crop = crop, legend.pt.size = legend.pt.size, legend.text.size = legend.text.size, scale.image = scale.image) i <- i + 1 } @@ -212,7 +213,7 @@ vrSpatialPlot <- function(object, group.by = "Sample", plot.segments = FALSE, gr #' #' @noRd vrSpatialPlotSingle <- function(assay, metadata, group.by = "Sample", plot.segments = FALSE, group.ids = NULL, colors = NULL, n.tile = 0, - graph = NULL, font.size = 2, pt.size = 2, cell.shape = 16, alpha = 1, plot_title = NULL, spatial = NULL, + graph = NULL, graph.edge.color = "orange", font.size = 2, pt.size = 2, cell.shape = 16, alpha = 1, plot_title = NULL, spatial = NULL, channel = NULL, background.color = NULL, background = NULL, reg = FALSE, crop = FALSE, legend.pt.size = 2, legend.text.size = 14, scale.image = TRUE){ @@ -332,7 +333,7 @@ vrSpatialPlotSingle <- function(assay, metadata, group.by = "Sample", plot.segme # add if a graph exists if(!is.null(graph)){ - g <- g + addGraph(graph, coords, background.color) + g <- g + addGraph(graph = graph, coords = coords, background = graph.edge.color) } } @@ -380,7 +381,7 @@ vrSpatialPlotSingle <- function(assay, metadata, group.by = "Sample", plot.segme # add if a graph exists if(!is.null(graph)){ - g <- g + addGraph(graph, coords, background.color) + g <- g + addGraph(graph = graph, coords = coords, background = graph.edge.color) } } @@ -1083,7 +1084,6 @@ vrSpatialPlotImage <- function(g, assay, background, scale.image, spatial = NULL } # background color - # if(any(background.color %in% c("white","black"))){ if(!is.null(background.color)){ g <- g + theme(panel.background = element_rect(fill = background.color, colour = background.color, linewidth = 0.5, linetype = "solid")) @@ -1222,17 +1222,18 @@ GeomSpot <- ggplot2::ggproto("GeomSpot", #' @param background the background of the plot. Either an image name, see \link{vrImageNames} or a vector of length two with image name #' #' @import ggplot2 +#' @importFrom igraph as_data_frame #' #' @noRd addGraph <- function(graph, coords, background){ - graph.df <- igraph::get.data.frame(graph) + # graph.df <- igraph::get.data.frame(graph) + graph.df <- igraph::as_data_frame(graph) graph.df$from.x <- coords$x[match(graph.df$from, rownames(coords))] graph.df$from.y <- coords$y[match(graph.df$from, rownames(coords))] graph.df$to.x <- coords$x[match(graph.df$to, rownames(coords))] graph.df$to.y <- coords$y[match(graph.df$to, rownames(coords))] geom_segment(data = graph.df, mapping = aes(x=from.x,xend = to.x, y=from.y,yend = to.y), - alpha = 0.5, color = ifelse(background == "black", "grey", "black")) - + alpha = 0.5, color = background) } #### diff --git a/docs/multiomic.Rmd b/docs/multiomic.Rmd index a7d724b..32fca56 100644 --- a/docs/multiomic.Rmd +++ b/docs/multiomic.Rmd @@ -147,7 +147,8 @@ We now select the feature type and graph name to run an hot spot analysis which vr2_merged_acute1 <- getHotSpotAnalysis(vr2_merged_acute1, assay = "Xenium_mol", features = "gene", graph.type = "radius") vrSpatialPlot(vr2_merged_acute1, assay = "Xenium_mol", - group.by = "gene_hotspot_flag", group.ids = c("cold", "hot"), alpha = 1) + group.by = "gene_hotspot_flag", group.ids = c("cold", "hot"), + alpha = 1, background.color = "white") ``` diff --git a/man/vrGraph.Rd b/man/vrGraph.Rd index d642650..75dd774 100644 --- a/man/vrGraph.Rd +++ b/man/vrGraph.Rd @@ -7,7 +7,7 @@ \usage{ vrGraph(object, assay = NULL, graph.type = NULL) -vrGraph(object, graph.type = "kNN") <- value +vrGraph(object, assay = NULL, graph.type = "kNN") <- value } \arguments{ \item{object}{a VoltRon object} diff --git a/man/vrSpatialPlot.Rd b/man/vrSpatialPlot.Rd index 3ea57d7..e649e85 100644 --- a/man/vrSpatialPlot.Rd +++ b/man/vrSpatialPlot.Rd @@ -13,6 +13,7 @@ vrSpatialPlot( n.tile = 0, assay = NULL, graph.name = NULL, + graph.edge.color = "orange", reduction = NULL, ncol = 2, nrow = NULL, @@ -55,6 +56,8 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.} \item{graph.name}{if not NULL, the spatial graph is with name \code{graph.name} is visualized as well, see \link{vrGraphNames}} +\item{graph.edge.color}{the colors of the graph edges, if \code{graph.name} is not NULL.} + \item{reduction}{used by \code{vrSpatialPlotVitessce} to visualize an embedding alongside with the spatial plot.} \item{ncol}{column wise number of plots, for \link{ggarrange}} diff --git a/tests/testthat/test-graph.R b/tests/testthat/test-graph.R new file mode 100644 index 0000000..9eaa543 --- /dev/null +++ b/tests/testthat/test-graph.R @@ -0,0 +1,34 @@ +test_that("spatial graphs", { + + # get data + data("xenium_data") + + # spatial graphs + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + graphs <- vrGraph(xenium_data, graph.type = "delaunay") + expect_true(inherits(graphs,"igraph")) + expect_true(length(igraph::E(graphs)) > 0) +}) + +test_that("overwrite graphs", { + + # spatial graphs + data("xenium_data") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + graphs <- vrGraph(xenium_data, graph.type = "delaunay") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + graphs <- vrGraph(xenium_data, graph.type = "delaunay") + expect_true(inherits(graphs,"igraph")) + expect_true(length(igraph::E(graphs)) > 0) + + # overwrite using subset of metadata groups + data("xenium_data") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", group.by = "clusters", group.ids = c(1,2,3,4)) + graphs <- vrGraph(xenium_data, graph.type = "delaunay") + expect_true(inherits(graphs,"igraph")) + expect_true(length(igraph::E(graphs)) > 0) + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", group.by = "clusters", group.ids = c(3,4,5,6)) + graphs <- vrGraph(xenium_data, graph.type = "delaunay") + expect_true(inherits(graphs,"igraph")) + expect_true(length(igraph::E(graphs)) > 0) +}) \ No newline at end of file diff --git a/tests/testthat/test-spatial.R b/tests/testthat/test-spatial.R index 06ee825..9ae69ce 100644 --- a/tests/testthat/test-spatial.R +++ b/tests/testthat/test-spatial.R @@ -8,18 +8,42 @@ test_that("spatial neighbors", { graphs <- vrGraph(xenium_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) + vrSpatialPlot(xenium_data, graph.name = "delaunay", group.by = "clusters") # spatial neighbors, spatialkNN xenium_data <- getSpatialNeighbors(xenium_data, method = "spatialkNN", k = 5) graphs <- vrGraph(xenium_data, graph.type = "spatialkNN") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) + vrSpatialPlot(xenium_data, graph.name = "spatialkNN", group.by = "clusters") # spatial neighbors, radius xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10) graphs <- vrGraph(xenium_data, graph.type = "radius") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) + vrSpatialPlot(xenium_data, graph.name = "radius", group.by = "clusters") + + # return + expect_equal(1,1L) +}) + +test_that("spatial neighbors for subsets", { + + # get data + data("xenium_data") + + # merge two of same types + xenium_data2 <- xenium_data + xenium_data2$Sample <- "XeniumR2" + merged_data <- merge(xenium_data, xenium_data2) + + # spatial neighbors, delaunay + merged_data <- getSpatialNeighbors(merged_data, assay = "Assay1", method = "delaunay") + graphs <- vrGraph(merged_data, graph.type = "delaunay") + expect_true(inherits(graphs,"igraph")) + expect_true(length(igraph::E(graphs)) > 0) + vrSpatialPlot(merged_data, graph.name = "delaunay", group.by = "clusters") # return expect_equal(1,1L) From df58bf36b063166554a73d910ca61dac2bf39d42 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Thu, 9 Jan 2025 15:24:13 +0100 Subject: [PATCH 11/26] mention more vrGraphNames and channel names in tutorials --- docs/multiomic.Rmd | 15 +++++++++++++-- docs/multiomic.html | 15 +++++++++++---- docs/nicheclustering.Rmd | 8 ++++++++ docs/registration.Rmd | 19 +++++++++++++++---- docs/registration.html | 16 ++++++++++------ docs/roianalysis.Rmd | 38 ++++++++++++++++++++++++++++---------- docs/roianalysis.html | 26 +++++++++++++++++++------- docs/spotanalysis.Rmd | 35 +++++++++++++++++++++++++++++++++-- docs/spotanalysis.html | 23 +++++++++++++++++------ 9 files changed, 154 insertions(+), 41 deletions(-) diff --git a/docs/multiomic.Rmd b/docs/multiomic.Rmd index 32fca56..2d183be 100644 --- a/docs/multiomic.Rmd +++ b/docs/multiomic.Rmd @@ -131,7 +131,7 @@ vr2_merged_acute1 <- getSpatialNeighbors(vr2_merged_acute1, assay = "Xenium_mol" method = "radius", radius = 50) ``` -This will create a new graph with the voltron object +We can now observe the new spatial neighborhood graph saved in the VoltRon object with name **radius**. ```{r eval = FALSE, class.source="watch-out"} vrGraphNames(vr2_merged_acute1) @@ -210,6 +210,18 @@ vrImages(vr2_merged_acute1[["Assay8"]], name = "main", channel = "H&E") <- vrImages(imgdata_reg, assay = "Assay1", name = "main_reg") ``` +We can now observe the new channels available for the both molecule and cell-level assays of Xenium data. + +```{r eval = FALSE, class.source="watch-out"} +vrImageChannelNames(vr2_merged_acute1) +``` + +``` + Assay Spatial Channels +1 Assay7 main DAPI,H&E +2 Assay8 main DAPI,H&E +``` +
    You can also add the VoltRon object of H&E data as an additional assay of the Xenium section such that one layer includes cell, molecules, ROI Annotations and images in the same time. Specifically, we add the ROI annotation to the Xenium VoltRon object using the **addAssay** function where we choose the destination sample/block and the layer of the assay. @@ -253,7 +265,6 @@ To run TissUUmaps please follow installation instructions [here](https://tissuum Now we can transfer ROI annotations as additional metadata features of the molecule assay. We will refer the new metadata column as "Region" which will indicate if the molecule is within any annotated ROI in the same layer. ```{r eval = FALSE, class.source="watch-out"} -# make a new metadata column for ROIAnnotation assay using names vrMainAssay(vr2_merged_acute1) <- "ROIAnnotation" vr2_merged_acute1$Region <- vrSpatialPoints(vr2_merged_acute1) diff --git a/docs/multiomic.html b/docs/multiomic.html index 7dee559..da2ef42 100644 --- a/docs/multiomic.html +++ b/docs/multiomic.html @@ -554,7 +554,8 @@

    Hot Spot Analysis

    vr2_merged_acute1 <- getSpatialNeighbors(vr2_merged_acute1, assay = "Xenium_mol", 
                                          group.by = "gene", group.ids = c("S2_N", "S2_orf1ab"), 
                                          method = "radius", radius = 50)
    -

    This will create a new graph with the voltron object

    +

    We can now observe the new spatial neighborhood graph saved in the +VoltRon object with name radius.

    vrGraphNames(vr2_merged_acute1)
    [1] "radius"

    We now select the feature type and graph name to run an hot spot @@ -563,7 +564,8 @@

    Hot Spot Analysis

    vr2_merged_acute1 <- getHotSpotAnalysis(vr2_merged_acute1, assay = "Xenium_mol", 
                                         features = "gene", graph.type = "radius")
 vrSpatialPlot(vr2_merged_acute1, assay = "Xenium_mol", 
-              group.by = "gene_hotspot_flag", group.ids = c("cold", "hot"), alpha = 1)
    + group.by = "gene_hotspot_flag", group.ids = c("cold", "hot"), + alpha = 1, background.color = "white")

    @@ -629,6 +631,12 @@

    Automated H&E Registration

    vrImages(imgdata_reg, assay = "Assay1", name = "main_reg") vrImages(vr2_merged_acute1[["Assay8"]], name = "main", channel = "H&E") <- vrImages(imgdata_reg, assay = "Assay1", name = "main_reg") +

    We can now observe the new channels available for the both molecule +and cell-level assays of Xenium data.

    +
    vrImageChannelNames(vr2_merged_acute1)
    +
       Assay  Spatial  Channels
+1 Assay7     main  DAPI,H&E
+2 Assay8     main  DAPI,H&E


    You can also add the VoltRon object of H&E data as an additional assay of the Xenium section such that one layer includes cell, @@ -670,8 +678,7 @@

    Label transfer

    of the molecule assay. We will refer the new metadata column as “Region” which will indicate if the molecule is within any annotated ROI in the same layer.

    -
    # make a new metadata column for ROIAnnotation assay using names
-vrMainAssay(vr2_merged_acute1) <- "ROIAnnotation"
+
    vrMainAssay(vr2_merged_acute1) <- "ROIAnnotation"
 vr2_merged_acute1$Region <- vrSpatialPoints(vr2_merged_acute1)
 
 # set the spatial coordinate system of ROI Annotations assay
diff --git a/docs/nicheclustering.Rmd b/docs/nicheclustering.Rmd
index bcdff51..3ab5a2a 100644
--- a/docs/nicheclustering.Rmd
+++ b/docs/nicheclustering.Rmd
@@ -207,6 +207,14 @@ Using normalized cell type abundances, we can now generate k-nearest neighbor gr
 
 ```{r eval = FALSE, class.source="watch-out"}
 MBrain_Sec <- getProfileNeighbors(MBrain_Sec, data.type = "norm", method = "SNN")
+vrGraphNames(MBrain_Sec)
+```
+
+```
+[1] "SNN"
+```
+
+```{r eval = FALSE, class.source="watch-out"}
 MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.6, graph = "SNN")
 ```
 
diff --git a/docs/registration.Rmd b/docs/registration.Rmd
index 2fca64c..ebd4b86 100644
--- a/docs/registration.Rmd
+++ b/docs/registration.Rmd
@@ -438,8 +438,8 @@ Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1")
 
 # import H&E image and build a VoltRon object
 Xen_R1_image <- importImageData("Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif",
-                                sample_name = "XeniumR1image",
-                                image_name = "H&E")
+                                sample_name = "XeniumR1image", 
+                                channel_names = "H&E")
 Xen_R1_image
 ```
 
@@ -478,10 +478,21 @@ Now we create a new channel for the existing coordinate system of the Xenium dat
 
 ```{r eval = FALSE, class.source="watch-out"}
 Xen_R1_image_reg <- xen_reg$registered_spat[[2]]
-vrImages(Xen_R1[["Assay1"]], channel = "H&E") <- vrImages(Xen_R1_image_reg, name = "H&E_reg")
+vrImages(Xen_R1[["Assay1"]], channel = "H&E") <- vrImages(Xenium_reg, name = "main_reg", channel = "H&E")
 ```
 
-Now we can call the registered H&E image of the Xenium data or later put the aligned H&E when calling **vrSpatialPlot** or **vrSpatialFeaturePlot**.
+We can now observe the new channels (H&E) available for the Xenium assay using **vrImageChannelNames**.
+
+```{r eval = FALSE, class.source="watch-out"}
+vrImageChannelNames(Xen_R1)
+```
+
+```
+   Assay Spatial Channels
+1 Assay1    main DAPI,H&E
+```
+
+We can call the registered H&E image of the Xenium data or later put the aligned H&E when calling **vrSpatialPlot** or **vrSpatialFeaturePlot**. 
 
 ```{r eval = FALSE, class.source="watch-out"}
 vrImages(Xen_R1, channel = "H&E", scale.perc = 5)
diff --git a/docs/registration.html b/docs/registration.html
index d9be384..bc9fd2f 100644
--- a/docs/registration.html
+++ b/docs/registration.html
@@ -864,8 +864,8 @@ 
    Alignment of Xenium and H&E
    
 
 # import H&E image and build a VoltRon object
 Xen_R1_image <- importImageData("Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif",
-                                sample_name = "XeniumR1image",
-                                image_name = "H&E")
+                                sample_name = "XeniumR1image", 
+                                channel_names = "H&E")
 Xen_R1_image
    
 
    VoltRon Object 
 XeniumR1image: 
@@ -897,10 +897,14 @@ 
    Automated Image Alignment
    
 we give a name for the new image/channel which is
 H&E.
    
 
    Xen_R1_image_reg <- xen_reg$registered_spat[[2]]
-vrImages(Xen_R1[["Assay1"]], channel = "H&E") <- vrImages(Xen_R1_image_reg, name = "H&E_reg")
    
-
    Now we can call the registered H&E image of the Xenium data or
-later put the aligned H&E when calling
-vrSpatialPlot or
+vrImages(Xen_R1[["Assay1"]], channel = "H&E") <- vrImages(Xenium_reg, name = "main_reg", channel = "H&E")
    
+
    We can now observe the new channels (H&E) available for the
+Xenium assay using vrImageChannelNames.
    
+
    vrImageChannelNames(Xen_R1)
    
+
       Assay Spatial Channels
+1 Assay1    main DAPI,H&E
    
+
    We can call the registered H&E image of the Xenium data or later
+put the aligned H&E when calling vrSpatialPlot or
 vrSpatialFeaturePlot.
    
 
    vrImages(Xen_R1, channel = "H&E", scale.perc = 5)
    
 
    
diff --git a/docs/roianalysis.Rmd b/docs/roianalysis.Rmd
index 6eb1c9c..befc280 100644
--- a/docs/roianalysis.Rmd
+++ b/docs/roianalysis.Rmd
@@ -444,7 +444,7 @@ We first import the H&E image of the prolonged case 4 TMA section using the **im
 We will use the H&E image of TMA section taken from the same block as the Prolonged case 4. You can download the image from [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/prolonged_case4_HE.tif).
 
 ```{r eval = FALSE, class.source="watch-out"}
-vrHEimage <- importImageData("prolonged_case4_HE.tif", image_name = "H&E")
+vrHEimage <- importImageData("prolonged_case4_HE.tif", channel_names = "H&E")
 vrImages(vrHEimage, scale.perc = 40)
 ```
 
@@ -463,33 +463,51 @@ GeoMxReg <- registerSpatialData(reference_spatdata = GeoMxR1_subset, query_spatd
 
 
 
-
-The result of the registration is a list of registered VoltRon objects which we can use for parsing the registered image as well. In this case, the second element of the resulting list would be the registered H&E based VoltRon object. We can also exchange images where the H&E image now is registered to the perspective as the GeoMx channels, and can be defined as a new channel in the original GeoMx object.
+The result of the registration is a list of registered VoltRon objects which we can use for parsing the registered image as well. In this case, the second element of the resulting list would be the registered H&E based VoltRon object. 
 
 ```{r eval = FALSE, class.source="watch-out"}
 vrHEimage_reg <- GeoMxReg$registered_spat[[2]]
-vrImages(GeoMxR1_subset[["Assay1"]], name = "main", channel = "H&E") <- vrImages(vrHEimage_reg, name = "H&E_reg")
 ```
 
-We can now visualize the ROIs and their annotations where the registered H&E visible in the background. We define the spatial key **main** and the channel name **H&E**. 
+We can check the additional coordinate system now available for the registered H&E image. One is the coordinate system with the original image, and the other with the one that is registered.
 
 ```{r eval = FALSE, class.source="watch-out"}
-vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E")
+vrSpatialNames(vrHEimage_reg)
 ```
 
-
+```
+[1] "main"     "main_reg"
+```
 
-Interactive Visualization can also be used to zoom in to ROIs and investigate the pathology associated with GeoMx ROIs labeled as fibrotic. 
+We can also exchange images where the H&E image now is registered to the perspective of the GeoMx channels, and can be defined as a new channel in the original GeoMx object.
 
 ```{r eval = FALSE, class.source="watch-out"}
-vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E", interactive = TRUE)
+vrImages(GeoMxR1_subset[["Assay1"]], name = "main", channel = "H&E") <- vrImages(vrHEimage_reg, name = "main_reg", channel = "H&E")
 ```
 
-
+We can now observe the new channels (H&E) available for the GeoMx assay using **vrImageChannelNames**. H&E is saved as a separate channel along with the originally available antibody channels of the original GeoMx experiment.
+
+```{r eval = FALSE, class.source="watch-out"}
+vrImageChannelNames(GeoMxR1_subset)
+```
 
+```
+   Assay Spatial                                               Channels
+1 Assay1    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
+```
 
+We can now visualize the ROIs and their annotations where the registered H&E visible in the background. We define the spatial key **main** and the channel name **H&E**. 
 
+```{r eval = FALSE, class.source="watch-out"}
+vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E")
+```
 
+
 
+Interactive Visualization can also be used to zoom in to ROIs and investigate the pathology associated with GeoMx ROIs labeled as fibrotic. 
 
+```{r eval = FALSE, class.source="watch-out"}
+vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E", interactive = TRUE)
+```
 
+
diff --git a/docs/roianalysis.html b/docs/roianalysis.html
index c8d9bb4..900b588 100644
--- a/docs/roianalysis.html
+++ b/docs/roianalysis.html
@@ -919,7 +919,7 @@ 
    H&E Image Integration
    
 
    We will use the H&E image of TMA section taken from the same
 block as the Prolonged case 4. You can download the image from here.
    
-
    vrHEimage <- importImageData("prolonged_case4_HE.tif", image_name = "H&E")
+
    vrHEimage <- importImageData("prolonged_case4_HE.tif", channel_names = "H&E")
 vrImages(vrHEimage, scale.perc = 40)
    
 
    
 

    
@@ -946,12 +946,24 @@ 
    H&E Image Integration
    
 
    The result of the registration is a list of registered VoltRon
 objects which we can use for parsing the registered image as well. In
 this case, the second element of the resulting list would be the
-registered H&E based VoltRon object. We can also exchange images
-where the H&E image now is registered to the perspective as the
-GeoMx channels, and can be defined as a new channel in the original
-GeoMx object.
    
-
    vrHEimage_reg <- GeoMxReg$registered_spat[[2]]
-vrImages(GeoMxR1_subset[["Assay1"]], name = "main", channel = "H&E") <- vrImages(vrHEimage_reg, name = "H&E_reg")
    
+registered H&E based VoltRon object.
    
+
    vrHEimage_reg <- GeoMxReg$registered_spat[[2]]
    
+
    We can check the additional coordinate system now available for the
+registered H&E image. One is the coordinate system with the original
+image, and the other with the one that is registered.
    
+
    vrSpatialNames(vrHEimage_reg)
    
+
    [1] "main"     "main_reg"
    
+
    We can also exchange images where the H&E image now is registered
+to the perspective of the GeoMx channels, and can be defined as a new
+channel in the original GeoMx object.
    
+
    vrImages(GeoMxR1_subset[["Assay1"]], name = "main", channel = "H&E") <- vrImages(vrHEimage_reg, name = "main_reg", channel = "H&E")
    
+
    We can now observe the new channels (H&E) available for the GeoMx
+assay using vrImageChannelNames. H&E is saved as a
+separate channel along with the originally available antibody channels
+of the original GeoMx experiment.
    
+
    vrImageChannelNames(GeoMxR1_subset)
    
+
       Assay Spatial                                               Channels
+1 Assay1    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
    
 
    We can now visualize the ROIs and their annotations where the
 registered H&E visible in the background. We define the spatial key
 main and the channel name H&E.
    
diff --git a/docs/spotanalysis.Rmd b/docs/spotanalysis.Rmd
index d6fd440..41b716f 100644
--- a/docs/spotanalysis.Rmd
+++ b/docs/spotanalysis.Rmd
@@ -157,6 +157,11 @@ Next, we build neighborhood graphs with the **shared nearest neighbors (SNN)** o
 
 ```{r eval = FALSE, class.source="watch-out", fig.align='center'}
 Xen_data <- getProfileNeighbors(Xen_data, dims = 1:20, method = "SNN")
+vrGraphNames(Xen_data)
+```
+
+```
+[1] "SNN"
 ```
 
 We can later conduct a clustering of cells using the **leiden's method** from the igraph package, which is utilized with the **getClusters** function.
@@ -334,6 +339,14 @@ vrEmbeddingPlot(MBrain_Sec, embedding = "umap")
 
 ```{r eval = FALSE, class.source="watch-out"}
 MBrain_Sec <- getProfileNeighbors(MBrain_Sec, dims = 1:30, k = 10, method = "SNN")
+vrGraphNames(MBrain_Sec)
+```
+
+```
+[1] "SNN"
+```
+
+```{r eval = FALSE, class.source="watch-out"}
 MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.5, label = "Clusters", graph = "SNN")
 vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Clusters")
 ```
@@ -461,6 +474,14 @@ Now we can visualize the clusters across these sections and perhaps also check f
 ```{r eval = FALSE, class.source="watch-out", fig.align='center'}
 # SNN graph and clusters
 vr_merged <- getProfileNeighbors(vr_merged, dims = 1:10, k = 10, method = "SNN")
+vrGraphNames(vr_merged)
+```
+
+```
+[1] "SNN"
+```
+
+```{r eval = FALSE, class.source="watch-out", fig.align='center'}
 vr_merged <- getClusters(vr_merged, resolution = 0.8, label = "MELC_Clusters", graph = "SNN")
 
 # install patchwork package
@@ -515,7 +536,17 @@ We use the **vrNeighbourhoodEnrichment** function to detect cell type pairs that
 [Delaunay tesselations](https://en.wikipedia.org/wiki/Delaunay_triangulation) or graphs are commonly used to determine neighbors of spatial entities. The function **getSpatialNeighbors** builds a delaunay graph of all assays of a certain type and detects neighbors of cells in a VoltRon object.
 
 ```{r eval = FALSE, class.source="watch-out", fig.align='center'}
-vr_merged_result <- getSpatialNeighbors(vr_merged, method = "delaunay")
+vr_merged <- getSpatialNeighbors(vr_merged, method = "delaunay")
+```
+
+The graph **delaunay**, which we will use for spatially-aware neightborhood analysis, is now the second graph available in the VoltRon object along with **SNN**.
+
+```{r eval = FALSE, class.source="watch-out", fig.align='center'}
+vrGraphNames(vr_merged)
+```
+
+```
+[1] "SNN"      "delaunay"
 ```
 
 Once neighbors are founds, we can apply a **permutation test** that compares the number of cell type occurances with an expected number of these occurances under multiple permutations of labels in the tissue (fixed coordinates but cells are randomly labelled). A similar approach is used to by several spatial analysis frameworks and packages ([Schapiro et. al 2017](https://www.nature.com/articles/nmeth.4391), [Palla et. al 2022](https://www.nature.com/articles/s41592-021-01358-2)).
@@ -523,7 +554,7 @@ Once neighbors are founds, we can apply a **permutation test** that compares the
 Here, we will use the original cell type labels annotated by [Mothes et. al 2023](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922044/).
 
 ```{r eval = FALSE, class.source="watch-out", fig.align='center'}
-neighborhood_results <- vrNeighbourhoodEnrichment(vr_merged_result, group.by = "Clusters")
+neighborhood_results <- vrNeighbourhoodEnrichment(vr_merged, group.by = "Clusters")
 ```
 
 The neighborhood analysis provides the results of:
diff --git a/docs/spotanalysis.html b/docs/spotanalysis.html
index d98beb1..ce315d1 100644
--- a/docs/spotanalysis.html
+++ b/docs/spotanalysis.html
@@ -596,7 +596,9 @@ 
    Clustering
    
 dimensionally reduced gene expression profiles. The function
 getProfileNeighbors also has an option of building
 k-nearest neighbors (kNN) graphs.
    
-
    Xen_data <- getProfileNeighbors(Xen_data, dims = 1:20, method = "SNN")
    
+
    Xen_data <- getProfileNeighbors(Xen_data, dims = 1:20, method = "SNN")
+vrGraphNames(Xen_data)
    
+
    [1] "SNN"
    
 
    We can later conduct a clustering of cells using the leiden’s
 method from the igraph package, which is utilized with the
 getClusters function.
    
@@ -673,7 +675,7 @@ 
    Import ST Data
    
 href="https://www.10xgenomics.com/products/spatial-gene-expression">Visium
 instrument.
    
 
    You can find and download readouts of all four Visium sections here.
+href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Visium/MouseBrainSerialSections.zip">here.
 The Mouse Brain Serial Section 1 datasets can be
 downloaded from here
@@ -746,7 +748,9 @@ 
    Embedding
    
 
    
 
    Clustering
    
 
    MBrain_Sec <- getProfileNeighbors(MBrain_Sec, dims = 1:30, k = 10, method = "SNN")
-MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.5, label = "Clusters", graph = "SNN")
+vrGraphNames(MBrain_Sec)
    
+
    [1] "SNN"
    
+
    MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.5, label = "Clusters", graph = "SNN")
 vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Clusters")
    
 
    
 

    
@@ -867,7 +871,9 @@ 
    Clustering
    
 conditions.
    
 
    # SNN graph and clusters
 vr_merged <- getProfileNeighbors(vr_merged, dims = 1:10, k = 10, method = "SNN")
-vr_merged <- getClusters(vr_merged, resolution = 0.8, label = "MELC_Clusters", graph = "SNN")
+vrGraphNames(vr_merged)
    
+
    [1] "SNN"
    
+
    vr_merged <- getClusters(vr_merged, resolution = 0.8, label = "MELC_Clusters", graph = "SNN")
 
 # install patchwork package
 if (!requireNamespace("patchwork", quietly = TRUE))
@@ -917,7 +923,12 @@ 
    Neighborhood Analysis
    
 spatial entities. The function getSpatialNeighbors
 builds a delaunay graph of all assays of a certain type and detects
 neighbors of cells in a VoltRon object.
    
-
    vr_merged_result <- getSpatialNeighbors(vr_merged, method = "delaunay")
    
+
    vr_merged <- getSpatialNeighbors(vr_merged, method = "delaunay")
    
+
    The graph delaunay, which we will use for
+spatially-aware neightborhood analysis, is now the second graph
+available in the VoltRon object along with SNN.
    
+
    vrGraphNames(vr_merged)
    
+
    [1] "SNN"      "delaunay"
    
 
    Once neighbors are founds, we can apply a permutation
 test that compares the number of cell type occurances with an
 expected number of these occurances under multiple permutations of
@@ -931,7 +942,7 @@ 
    Neighborhood Analysis
    
 
    Here, we will use the original cell type labels annotated by Mothes et.
 al 2023.
    
-
    neighborhood_results <- vrNeighbourhoodEnrichment(vr_merged_result, group.by = "Clusters")
    
+
    neighborhood_results <- vrNeighbourhoodEnrichment(vr_merged, group.by = "Clusters")
    
 
    The neighborhood analysis provides the results of:
    
 
      
 
    • the association tests (whether cell types are

From c26f61affc7e7c346ec02b2c11f32996d7d3b5c8 Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Thu, 9 Jan 2025 15:35:36 +0100
Subject: [PATCH 12/26] update links to .rds files in tutorials

---
 docs/interactive.Rmd   | 19 -------------------
 docs/interactive.html  | 19 -------------------
 docs/registration.Rmd  | 21 +++++++++++++++++++--
 docs/registration.html | 22 +++++++++++++++-------
 4 files changed, 34 insertions(+), 47 deletions(-)

diff --git a/docs/interactive.Rmd b/docs/interactive.Rmd
index b384f7c..3473cb5 100644
--- a/docs/interactive.Rmd
+++ b/docs/interactive.Rmd
@@ -114,25 +114,6 @@ VoltRon incorporates utilities
 * **(i)** to convert VoltRon objects into other spatial data objects and files that could be used platforms, and 
 * **(ii)** to incorporate wrapper functions to call methods from package that achieves interactive visualization
 
-
-
 
      
 
 ## Vitessce 
diff --git a/docs/interactive.html b/docs/interactive.html
index 0080f5a..3dcfe0f 100644
--- a/docs/interactive.html
+++ b/docs/interactive.html
@@ -521,25 +521,6 @@ 
      Interactive Visualization
      
 
    • (ii) to incorporate wrapper functions to call
 methods from package that achieves interactive visualization
      • 
 
    
-
 

    
 
    
 
    Vitessce
    
diff --git a/docs/registration.Rmd b/docs/registration.Rmd
index ebd4b86..72ad012 100644
--- a/docs/registration.Rmd
+++ b/docs/registration.Rmd
@@ -118,7 +118,7 @@ xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2))
 
 
    
 
-You can save and use the same parameters later, and reproduce the alignment without choosing parameters the second time. You can find a presaved set of parameters [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/mapping_parameters.rds).
+You can save and use the same parameters later, and reproduce the alignment without choosing parameters the second time. 
 
 ```{r class.source="watch-out", eval = FALSE} 
 mapping_parameters <- xen_reg$mapping_parameters
@@ -126,6 +126,14 @@ xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2),
                                mapping_parameters = mapping_parameters)
 ```
 
+You can find a presaved set of parameters [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/mapping_parameters.rds).
+
+```{r class.source="watch-out", eval = FALSE} 
+mapping_parameters <- readRDS("mapping_parameters.rds")
+xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 
+                               mapping_parameters = mapping_parameters)
+```
+
 If the pre-saved parameters are available, the registration can also be performed without using the shiny app. By using **interactive = FALSE**, we can register images and VoltRon objects directly. 
 
 ```{r class.source="watch-out", eval = FALSE} 
@@ -155,7 +163,7 @@ xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2))
 
 
    
 
-You can save and use the same keypoints later, and reproduce the manual alignment without choosing keypoints for the second time. You can find a presaved set of parameters with selected manual keypoints  [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/mapping_parameters_manual.rds).
+You can save and use the same keypoints later, and reproduce the manual alignment without choosing keypoints for the second time. 
 
 ```{r class.source="watch-out", eval = FALSE} 
 mapping_parameters <- xen_reg$mapping_parameters
@@ -163,6 +171,14 @@ xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2),
                                mapping_parameters = mapping_parameters)
 ```
 
+You can find a presaved set of parameters with selected manual keypoints  [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/mapping_parameters_manual.rds).
+
+```{r class.source="watch-out", eval = FALSE} 
+mapping_parameters <- readRDS("mapping_parameters_manual.rds")
+xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 
+                               mapping_parameters = mapping_parameters)
+```
+
 If the pre-saved keypoints are available with parameters, the registration can also be performed without using the shiny app. By using **interactive = FALSE**, we can register images and VoltRon objects directly. 
 
 ```{r class.source="watch-out", eval = FALSE} 
@@ -317,6 +333,7 @@ g1 / g2
 The **transferData** function can also transfer **metadata features** across layers and assays. In this case, we will transfer cell type labels that were trained on the Xenium sections onto the Visium sections. We will use the cluster labels generated at the end of the Xenium analysis section of workflow from [Cell/Spot Analysis](spotanalysis.html). You can download the VoltRon object with clustered and annotated Xenium cells along with the Visium assay from [here](https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/VRBlock_data_clustered.rds). 
 
 ```{r class.source="watch-out", eval = FALSE}
+VRBlock <- readRDS("VRBlock_data_clustered.rds")
 vrSpatialPlot(VRBlock, assay = "Xenium", group.by = "CellType", pt.size = 0.4)
 ```
 
diff --git a/docs/registration.html b/docs/registration.html
index bc9fd2f..0ac6264 100644
--- a/docs/registration.html
+++ b/docs/registration.html
@@ -582,10 +582,13 @@ 
    Automated Image Alignment
    
 
    
 

    
 
    You can save and use the same parameters later, and reproduce the
-alignment without choosing parameters the second time. You can find a
-presaved set of parameters here.
    
+alignment without choosing parameters the second time.
    
 
    mapping_parameters <- xen_reg$mapping_parameters
+xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 
+                               mapping_parameters = mapping_parameters)
    
+
    You can find a presaved set of parameters here.
    
+
    mapping_parameters <- readRDS("mapping_parameters.rds")
 xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 
                                mapping_parameters = mapping_parameters)
    
 
    If the pre-saved parameters are available, the registration can also
@@ -616,10 +619,14 @@ 
    Manual Image Alignment
    
 
    
 

    
 
    You can save and use the same keypoints later, and reproduce the
-manual alignment without choosing keypoints for the second time. You can
-find a presaved set of parameters with selected manual keypoints here.
    
+manual alignment without choosing keypoints for the second time.
    
 
    mapping_parameters <- xen_reg$mapping_parameters
+xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 
+                               mapping_parameters = mapping_parameters)
    
+
    You can find a presaved set of parameters with selected manual
+keypoints here.
    
+
    mapping_parameters <- readRDS("mapping_parameters_manual.rds")
 xen_reg <- registerSpatialData(object_list = list(Xen_R1, Vis, Xen_R2), 
                                mapping_parameters = mapping_parameters)
    
 
    If the pre-saved keypoints are available with parameters, the
@@ -769,7 +776,8 @@ 
    Label Transfer (Cells->Spots)
    
 VoltRon object with clustered and annotated Xenium cells along with the
 Visium assay from here.
    
-
    vrSpatialPlot(VRBlock, assay = "Xenium", group.by = "CellType", pt.size = 0.4)
    
+
    VRBlock <- readRDS("VRBlock_data_clustered.rds")
+vrSpatialPlot(VRBlock, assay = "Xenium", group.by = "CellType", pt.size = 0.4)
    
 
    
 
    Here, we can see that both Xenium layers are clustered and annotated
 where we can use these cell annotations and transfer them to the Visium

From 0b93d64b2fc3814d723fba087d08fdadfbbd968d Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Thu, 9 Jan 2025 15:44:40 +0100
Subject: [PATCH 13/26] update github workflows and docker files

---
 .github/workflows/check.yml | 7 +++++++
 inst/extdata/Dockerfile     | 7 +++++++
 2 files changed, 14 insertions(+)

diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml
index 3df4cc8..5f05836 100644
--- a/.github/workflows/check.yml
+++ b/.github/workflows/check.yml
@@ -154,6 +154,13 @@ jobs:
           devtools::install_github('BIMSBbioinfo/ImageArray')
           devtools::install_github('BIMSBbioinfo/HDF5DataFrame')
           devtools::install_github('BIMSBbioinfo/ZarrDataFrame')
+          install.packages("Seurat")
+          BiocManager::install("glmGamPoi")
+          install.packages('hdf5r')
+          BiocManager::install('RBioFormats')
+          BiocManager::install('ComplexHeatmap')
+          devtools::install_github('dmcable/spacexr')
+          devtools::install_github('xuranw/MuSiC')
         shell: Rscript {0}
           
       ## R CMD Check
diff --git a/inst/extdata/Dockerfile b/inst/extdata/Dockerfile
index b95d1bc..33ecdbe 100644
--- a/inst/extdata/Dockerfile
+++ b/inst/extdata/Dockerfile
@@ -32,6 +32,13 @@ RUN R -e "remotes::install_github('bnprks/BPCells/r@v0.3.0')"
 RUN R -e "remotes::install_github('BIMSBbioinfo/ImageArray')"
 RUN R -e "remotes::install_github('BIMSBbioinfo/HDF5DataFrame')"
 RUN R -e "remotes::install_github('BIMSBbioinfo/ZarrDataFrame')"
+RUN R -e "install.packages('Seurat')"
+RUN R -e "BiocManager::install('glmGamPoi')"
+RUN R -e "install.packages('hdf5r')"
+RUN R -e "BiocManager::install('RBioFormats')"
+RUN R -e "BiocManager::install('ComplexHeatmap')"
+RUN R -e "devtools::install_github('dmcable/spacexr')"
+RUN R -e "devtools::install_github('xuranw/MuSiC')"
 
 # Install VoltRon dependencies
 RUN R -e "devtools::install_github('BIMSBbioinfo/VoltRon')"

From 2b9fa972f72c3257c9c84568ac8f978e749ba2f7 Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Thu, 9 Jan 2025 15:49:54 +0100
Subject: [PATCH 14/26] add dependencies to Dockerfile and github workflows

---
 .github/workflows/check.yml | 4 ++++
 inst/extdata/Dockerfile     | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml
index 5f05836..7e6bae8 100644
--- a/.github/workflows/check.yml
+++ b/.github/workflows/check.yml
@@ -161,6 +161,10 @@ jobs:
           BiocManager::install('ComplexHeatmap')
           devtools::install_github('dmcable/spacexr')
           devtools::install_github('xuranw/MuSiC')
+          BiocManager::install('SingleCellExperiment')
+          BiocManager::install('SpatialExperiment')
+          install.packages('dplyr')
+          BiocManager::install('DESeq2')
         shell: Rscript {0}
           
       ## R CMD Check
diff --git a/inst/extdata/Dockerfile b/inst/extdata/Dockerfile
index 33ecdbe..3816ac9 100644
--- a/inst/extdata/Dockerfile
+++ b/inst/extdata/Dockerfile
@@ -39,6 +39,10 @@ RUN R -e "BiocManager::install('RBioFormats')"
 RUN R -e "BiocManager::install('ComplexHeatmap')"
 RUN R -e "devtools::install_github('dmcable/spacexr')"
 RUN R -e "devtools::install_github('xuranw/MuSiC')"
+RUN R -e "BiocManager::install('SingleCellExperiment')"
+RUN R -e "BiocManager::install('SpatialExperiment')"
+RUN R -e "install.packages('dplyr')"
+RUN R -e "BiocManager::install('DESeq2')"
 
 # Install VoltRon dependencies
 RUN R -e "devtools::install_github('BIMSBbioinfo/VoltRon')"

From 007f8419e1cd5d48afe326335c1383e100817e68 Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Fri, 10 Jan 2025 00:03:54 +0100
Subject: [PATCH 15/26] update webpage intro and README

---
 README.md       |  6 ++++++
 docs/index.Rmd  | 30 ++++++++++++++++++++++++++---
 docs/index.html | 51 ++++++++++++++++++++++++++++++++++++++-----------
 3 files changed, 73 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index e731c37..5252ede 100644
--- a/README.md
+++ b/README.md
@@ -79,6 +79,12 @@ On **Fedora** you may need [`opencv-devel`](https://src.fedoraproject.org/rpms/o
 yum install opencv-devel
 ```
 
+## Dependencies
+
+VoltRon incorporates `RBioformats` package to import images from `ome.tiff` files, which requires [Java JDK](https://www.oracle.com/java/technologies/downloads/?er=221886) to be available in your system:
+
+See [https://cran.r-project.org/web/packages/rJava](https://cran.r-project.org/web/packages/rJava) below for more information.
+
 ## Docker Hub
 
 You can also run VoltRon from a container already available in [Docker Hub](https://hub.docker.com/repository/docker/amanukyan1385/rstudio-voltron/general). The docker image is based on the [Rocker Project](https://rocker-project.org/) and can be run from the terminal like below: 
diff --git a/docs/index.Rmd b/docs/index.Rmd
index 1b41b83..5b33ea5 100644
--- a/docs/index.Rmd
+++ b/docs/index.Rmd
@@ -94,6 +94,14 @@ knitr::opts_chunk$set(echo = TRUE)
 
 
    
 
+## Staying up-to-date
+
+To ask questions please use VoltRon discussion forum on google groups.
+
+- https://groups.google.com/forum/#!forum/voltron_discussion
+
+
    
+
 ## Installation
 
 Install from the GitHub repository using devtools (with R version 4.3.0 or higher):
@@ -108,7 +116,7 @@ Depending on the number of required dependencies, installation may be completed
 
 On **Windows** and **MacOS**, OpenCV will be downloaded automatically upon installation. However, [Rtools](https://cran.r-project.org/bin/windows/Rtools/rtools43/rtools.html) may be required to be downloaded too, hence this may take some time!
 
-On **Ubuntu** we provide a set of instructions that may help users to build OpenCV with necessary headers [here](inst/extdata/install_ubuntu.md).
+On **Ubuntu** we provide a set of instructions that may help users to build OpenCV with necessary headers [here](https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/install_ubuntu.md.
 
 On **Fedora** you may need [`opencv-devel`](https://src.fedoraproject.org/rpms/opencv):
 
@@ -118,9 +126,25 @@ yum install opencv-devel
 
 
    
 
-## Tutorials
+## Dependencies
+
+VoltRon incorporates `RBioformats` package to import images from `ome.tiff` files, which requires [Java JDK](https://www.oracle.com/java/technologies/downloads/?er=221886) to be available in your system:
+
+See [https://cran.r-project.org/web/packages/rJava](https://cran.r-project.org/web/packages/rJava) for more information.
+
+
    
+
+## Docker Hub
+
+You can also run VoltRon from a container already available in [Docker Hub](https://hub.docker.com/repository/docker/amanukyan1385/rstudio-voltron/general). The docker image is based on the [Rocker Project](https://rocker-project.org/) and can be run from the terminal like below: 
+
+```
+docker run --rm -ti -e PASSWORD= -p 8787:8787 amanukyan1385/rstudio-voltron:main
+```
+
+Then, start the RStudio session from the browser at `http://localhost:8787/` and enter `rstudio` as username and `` as password. 
 
-Please see the [Explore](https://artur-man.github.io/VoltRon/tutorials.html) section in the VoltRon website for tutorials, example scripts and analysis found in the [preprint](https://www.biorxiv.org/content/10.1101/2023.12.15.571667v1). Tutorials include links for accessing necessary data to run scripts across all tutorials. 
+See [here](https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/docker_desktop_instructions.md) for more instructions on how to run the container using [Docker Desktop](https://www.docker.com/products/docker-desktop/).
 
 
    
 
diff --git a/docs/index.html b/docs/index.html
index 411881b..1fc332b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -479,6 +479,16 @@ 
    VoltRon
    
 
    
 
    
 

    
+
    
+
    Staying up-to-date
    
+
    To ask questions please use VoltRon discussion forum on google
+groups.
    
+
+

    
+
    
 
    
 
    Installation
    
 
    Install from the GitHub repository using devtools (with R version
@@ -493,22 +503,41 @@ 
    Installation
    
 href="https://cran.r-project.org/bin/windows/Rtools/rtools43/rtools.html">Rtools
 may be required to be downloaded too, hence this may take some time!
    
 
    On Ubuntu we provide a set of instructions that may
-help users to build OpenCV with necessary headers here.
    
+help users to build OpenCV with necessary headers [here](https://github.com/BIMSBbioinfo/VoltRon/blob/main/inst/extdata/install_ubuntu.md.
    
 
    On Fedora you may need opencv-devel:
    
 
    yum install opencv-devel
    
 

    
 
    
-
    
-
    Tutorials
    
-
    Please see the Explore
-section in the VoltRon website for tutorials, example scripts and
-analysis found in the preprint.
-Tutorials include links for accessing necessary data to run scripts
-across all tutorials.
    
+
    
+
    Dependencies
    
+
    VoltRon incorporates RBioformats package to import
+images from ome.tiff files, which requires Java
+JDK to be available in your system:
    
+
    See https://cran.r-project.org/web/packages/rJava
+for more information.
    
+

    
+
    
+
    
+
    Docker Hub
    
+
    You can also run VoltRon from a container already available in Docker
+Hub. The docker image is based on the Rocker Project and can be run
+from the terminal like below:
    
+
    docker run --rm -ti -e PASSWORD=<yourpassword> -p 8787:8787 amanukyan1385/rstudio-voltron:main
    
+
    Then, start the RStudio session from the browser at
+http://localhost:8787/ and enter rstudio as
+username and <yourpassword> as password.
    
+
    See here
+for more instructions on how to run the container using Docker
+Desktop.
    
 

    
 
    
 
    

From 6cc74bf68a697cd6701401e3978a29e2222921b3 Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Fri, 10 Jan 2025 01:06:44 +0100
Subject: [PATCH 16/26] fix reticulate-based zarr conversion bug

---
 R/conversion.R | 35 ++++++++++++++++++++---------------
 1 file changed, 20 insertions(+), 15 deletions(-)

diff --git a/R/conversion.R b/R/conversion.R
index 17951f6..452f82e 100644
--- a/R/conversion.R
+++ b/R/conversion.R
@@ -338,11 +338,18 @@ as.AnnData <- function(object,
     images_mgk <- list(images_mgk)
     names(images_mgk) <- vrAssayNames(object, assay = assay)  
   }
-  image_data_list <- lapply(images_mgk, function(img) {
+  image_list <- lapply(images_mgk, function(img) {
     list(images = list(hires = as.numeric(magick::image_data(img, channels = "rgb"))),
          scalefactors = list(tissue_hires_scalef = 1, spot_diameter_fullres = 0.5))
   })
   
+  # obsm
+  # TODO: currently embedding and spatial dimensions should of the same size, but its not 
+  # always the case in VoltRon objects
+  obsm <- c(obsm, list(spatial = coords, 
+                       spatial_AssayID = coords, 
+                       segmentation = segmentations_array))
+  
   # save as zarr
   if(grepl(".zarr[/]?$", file)){
     
@@ -354,8 +361,8 @@ as.AnnData <- function(object,
     proc <- basilisk::basiliskStart(py_env)
     on.exit(basilisk::basiliskStop(proc))
     success <- basilisk::basiliskRun(proc, function(data, metadata, obsm, coords, segments, image_list, file) {
-      anndata <- reticulate::import("anndata")
       zarr <- reticulate::import("zarr")
+      anndata <- reticulate::import("anndata")
       make_numpy_friendly <- function(x) {
         if (DelayedArray::is_sparse(x)) {
           methods::as(x, "dgCMatrix")
@@ -365,18 +372,17 @@ as.AnnData <- function(object,
         }
       }
       X <- make_numpy_friendly(t(data))
-      obsm <- c(obsm, list(spatial = coords, 
-                           spatial_AssayID = coords, 
-                           segmentation = segmentations_array))
+      obsm <- list(spatial = coords, 
+                   spatial_AssayID = coords, 
+                   segmentation = segmentations_array)
       adata <- anndata$AnnData(X = X, 
                                obs = metadata, 
                                obsm = obsm, 
-                               uns = list(spatial = image_data_list))
-      
+                               uns = list(spatial = image_list))
+      adata <- reticulate::r_to_py(adata)
       adata$write_zarr(file)
       return(TRUE)
-    }, data = data, metadata = metadata, obsm = obsm, coords = coords, segments = segmentations_array, image_list = image_data_list, file = file)
-    
+    }, data = data, metadata = metadata, obsm = obsm, coords = coords, segments = segmentations_array, image_list = image_list, file = file)
     if(create.ometiff){
       success2 <- as.OmeTiff(images_mgk[[1]], out_path = gsub("zarr[/]?$", "ome.tiff", file)) 
       success <- success & success2
@@ -401,7 +407,7 @@ as.AnnData <- function(object,
                                  obsm = list(spatial = coords, 
                                              spatial_AssayID = coords, 
                                              segmentation = segmentations_array),
-                                 uns = list(spatial = image_data_list))
+                                 uns = list(spatial = image_list))
       
       # Write to h5ad file using anndataR
       anndataR::write_h5ad(adata, path = file)
@@ -414,10 +420,10 @@ as.AnnData <- function(object,
       # Create anndata using anndata
       adata <- anndata::AnnData(X = t(data), 
                                 obs = metadata, 
-                                obsm = list(spatial = coords, 
-                                            spatial_AssayID = coords, 
+                                obsm = list(spatial = coords,
+                                            spatial_AssayID = coords,
                                             segmentation = segmentations_array),
-                                uns = list(spatial = image_data_list))
+                                uns = list(spatial = image_list))
       
       
       # Write to h5ad file using anndata
@@ -603,8 +609,7 @@ as.OmeZarr <- function (object, out_path, image_id = "image_1"){
   
   # get image and transpose the array
   img_arr <- apply(as.matrix(magick::image_raster(object, tidy = FALSE)), c(1, 2), col2rgb)
-  # img_arr <- aperm(img_arr, c(2,3,1))
-  
+
   # run basilisk
   proc <- basilisk::basiliskStart(py_env)
   on.exit(basilisk::basiliskStop(proc))

From 72c19785284cd8917072ded5084cab526f7238b4 Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Sat, 11 Jan 2025 00:18:31 +0100
Subject: [PATCH 17/26] implement as.VoltRon method for SpatialExperiment
 objects

---
 NAMESPACE         |   1 +
 R/conversion.R    | 137 +++++++++++++++++++++++++++++++++++++++++++---
 R/generics.R      |   2 +-
 man/as.VoltRon.Rd |  17 +++++-
 4 files changed, 146 insertions(+), 11 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 7610aca..0dd2059 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -46,6 +46,7 @@ S3method(addAssay,vrMetadata)
 S3method(addFeature,VoltRon)
 S3method(addFeature,vrAssayV2)
 S3method(as.VoltRon,Seurat)
+S3method(as.VoltRon,SpatialExperiment)
 S3method(as.Zarr,"magick-image")
 S3method(as.Zarr,VoltRon)
 S3method(combineChannels,VoltRon)
diff --git a/R/conversion.R b/R/conversion.R
index 452f82e..f8326b6 100644
--- a/R/conversion.R
+++ b/R/conversion.R
@@ -2,7 +2,6 @@
 # Seurat ####
 ####
 
-#' @param object a Seurat object
 #' @param type the spatial data type of Seurat object: "image" or "spatial"
 #' @param assay_name the assay name
 #' @param ... Additional parameter passed to \link{formVoltRon}
@@ -17,10 +16,9 @@ as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name =
 
   # check Seurat package
   if(!requireNamespace('Seurat'))
-    stop("Please install Seurat package for using Seurat objects")
+    stop("Please install Seurat package for using Seurat objects!: install.packages('Seurat')")
 
   # raw counts
-  # rawdata <- as.matrix(object[[Seurat::DefaultAssay(object)]]@counts)
   rawdata <- SeuratObject::LayerData(object, assay = Seurat::DefaultAssay(object), layer = "counts")
 
   # metadata
@@ -65,7 +63,7 @@ as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name =
       colnames(coords) <- c("x", "y")
       rownames(coords) <- cells_nopostfix
 
-      # from voltron
+      # form voltron
       params <- list()
       assay.type <- "cell"
       assay_name <- "FOV"
@@ -79,8 +77,6 @@ as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name =
         for(embed_name in names(embedding_list)){
           cur_embedding <- embedding_list[[embed_name]][cells,]
           rownames(cur_embedding) <- spatialpoints
-          # embedding_sp <- embedding_list[[embed_name]][spatialpoints_nopostfix[spatialpoints_assay == vrAssayNames(voltron_list[[fn]])],]
-          # rownames(embedding_sp) <- spatialpoints
           vrEmbeddings(voltron_list[[fn]], type = embed_name) <- cur_embedding
         }
       }
@@ -91,7 +87,7 @@ as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name =
     vrobject <- merge(voltron_list[[1]], voltron_list[-1])
   } else{
     image <- NULL
-    warning("There are no spatial objects available in this Seurat object")
+    stop("There are no spatial objects available in this Seurat object")
   }
 
   return(vrobject)
@@ -722,6 +718,122 @@ as.Giotto <- function(object, assay = NULL, reg = FALSE){
 # SpatialExperiment ####
 ####
 
+#' @param type the spatial data type of Seurat object: "image" or "spatial"
+#' @param assay_type one of two types, 'cell' or 'spot' etc.
+#' @param assay_name the assay name of the voltron assays (e.g. Visium, Xenium etc.)
+#' @param image_id select image_id names if needed.
+#' @param ... Additional parameter passed to \link{formVoltRon}
+#'
+#' @rdname as.VoltRon
+#' @method as.VoltRon SpatialExperiment
+#' 
+#' @importFrom magick image_read
+#'
+#' @export
+as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name = NULL, image_id = NULL, ...){
+  
+  # check SpatialExperiment package
+  if(!requireNamespace('SpatialExperiment'))
+    stop("Please install SpatialExperiment package for using SpatialExperiment objects!: BiocManager::install('SpatialExperiment')")
+  
+  # raw counts
+  data <- SummarizedExperiment::assay(object, i = "counts")
+
+  # metadata
+  metadata <- as.data.frame(SummarizedExperiment::colData(object))
+  
+  # embeddings
+  dim_names <- SingleCellExperiment::reducedDimNames(object)
+  if(length(dim_names) > 0){
+    embeddings_flag <- TRUE
+    embedding_list <- sapply(dim_names, function(x) {
+      SingleCellExperiment::reducedDim(object, type = x)
+    }, USE.NAMES = TRUE)
+  } else {
+    embeddings_flag <- FALSE
+  }
+  
+  # coords
+  coords <- SpatialExperiment::spatialCoords(object)
+  colnames(coords) <- c("x", "y")
+  
+  # img data
+  imgdata <- SpatialExperiment::imgData(object)
+  
+  # image
+  voltron_list <- list()
+  sample_names <- unique(metadata$sample_id)
+  for(samp in sample_names){
+    
+    # spatial points
+    sppoints <- rownames(metadata)[metadata$sample_id == samp]
+    sppoints_nopostfix <- gsub("_Assay[0-9]+$", "", sppoints)
+    
+    # metadata 
+    cur_metadata <- metadata[sppoints,]
+    
+    # data
+    cur_data <- data[,sppoints]
+    
+    # coords
+    cur_coords <- coords[sppoints,]
+    
+    # image
+    if(nrow(imgdata) > 0){
+      if(is.null(image_id)){
+        image_names <- imgdata$image_id[imgdata$sample_id == samp]
+      } else {
+        image_names <- image_id
+      }
+      img_list <- sapply(image_names, function(img){ 
+        imgraster <- SpatialExperiment::imgRaster(object, 
+                                                  sample_id = samp, 
+                                                  image_id = img)
+        magick::image_read(imgraster)
+      }, USE.NAMES = TRUE)
+    } else {
+      img_list <- NULL
+    }
+    
+    # get params
+    if(assay_type == "spot"){
+      vis.spot.radius <- 1 
+      spot.radius <- 1
+    } else {
+      params <- list()
+    }
+    
+    # form voltron
+    assay_name <- assay_name
+    assay_type <- assay_type
+    voltron_list[[samp]] <- formVoltRon(data = cur_data, metadata = cur_metadata, coords = cur_coords, 
+                                      main.assay = assay_name, image = img_list, params = params, 
+                                      assay.type = assay_type, sample_name = samp, ...)
+    
+    # add embeddings
+    spatialpoints <- vrSpatialPoints(voltron_list[[samp]])
+    spatialpoints_nopostfix <- stringr::str_replace(spatialpoints, "_Assay[0-9]+$", "")
+    spatialpoints_assay <- stringr::str_extract(spatialpoints, "Assay[0-9]+$")
+    if(embeddings_flag){
+      for(embed_name in names(embedding_list)){
+        cur_embedding <- embedding_list[[embed_name]][cells,]
+        rownames(cur_embedding) <- spatialpoints
+        vrEmbeddings(voltron_list[[samp]], type = embed_name) <- cur_embedding
+      }
+    }
+  }
+  
+  # merge object
+  message("Merging object ...")
+  if(length(voltron_list) > 1){
+    vrobject <- merge(voltron_list[[1]], voltron_list[-1])
+  } else {
+    vrobject <- voltron_list[[1]]
+  }
+
+  return(vrobject)
+}
+
 #' as.SpatialExperiment
 #'
 #' Converting a VoltRon object into a SpatialExperiment object
@@ -773,13 +885,22 @@ as.SpatialExperiment <- function(object, assay = NULL, reg = FALSE){
   assays <- stringr::str_extract(rownames(metadata), pattern = "_Assay[0-9]+$")
   assays <- gsub("^_", "", assays)
   
+  # Embeddings
+  reduceddims <- list()
+  if (length(vrEmbeddingNames(object, assay = assay)) > 0) {
+    for (embed_name in vrEmbeddingNames(object, assay = assay)) {
+      reduceddims[[embed_name]] <- vrEmbeddings(object, assay = assay, type = embed_name)
+    }
+  }
+  
   # coordinates
   coords <- as.matrix(vrCoordinates(flipCoordinates(object, assay = assay), assay = assay, reg = reg))
   coords <- coords[colnames(rawdata),]
   
   # Seurat object
   spe <- SpatialExperiment::SpatialExperiment(assay=list(counts = rawdata),
-                                              colData=metadata,
+                                              colData=metadata, 
+                                              reducedDims = reduceddims,
                                               sample_id=assays,
                                               spatialCoords=coords)
   
diff --git a/R/generics.R b/R/generics.R
index 4db28b1..955415e 100644
--- a/R/generics.R
+++ b/R/generics.R
@@ -615,7 +615,7 @@ combineChannels <- function(object, assay = NULL, name = NULL, reg = FALSE, chan
 #'
 #' Generic methods for conversion into a VoltRon object
 #'
-#' @param object a VoltRon object
+#' @param object a Seurat or SpatialExperiment object
 #' @param ... Arguments passed to other methods
 #'
 #' @rdname as.VoltRon
diff --git a/man/as.VoltRon.Rd b/man/as.VoltRon.Rd
index 713319c..cb4ed70 100644
--- a/man/as.VoltRon.Rd
+++ b/man/as.VoltRon.Rd
@@ -3,20 +3,33 @@
 \name{as.VoltRon}
 \alias{as.VoltRon}
 \alias{as.VoltRon.Seurat}
+\alias{as.VoltRon.SpatialExperiment}
 \title{as.VoltRon}
 \usage{
 as.VoltRon(object, ...)
 
 \method{as.VoltRon}{Seurat}(object, type = c("image", "spatial"), assay_name = NULL, ...)
+
+\method{as.VoltRon}{SpatialExperiment}(
+  object,
+  assay_type = "cell",
+  assay_name = NULL,
+  image_id = NULL,
+  ...
+)
 }
 \arguments{
-\item{object}{a Seurat object}
+\item{object}{a Seurat or SpatialExperiment object}
 
 \item{...}{Additional parameter passed to \link{formVoltRon}}
 
 \item{type}{the spatial data type of Seurat object: "image" or "spatial"}
 
-\item{assay_name}{the assay name}
+\item{assay_name}{the assay name of the voltron assays (e.g. Visium, Xenium etc.)}
+
+\item{assay_type}{one of two types, 'cell' or 'spot' etc.}
+
+\item{image_id}{select image_id names if needed.}
 }
 \description{
 Generic methods for conversion into a VoltRon object

From 239ed99bdd6af1f815c9c71be054eeae36314533 Mon Sep 17 00:00:00 2001
From: Artur-man 
Date: Sun, 12 Jan 2025 01:58:53 +0100
Subject: [PATCH 18/26] update vrspatialnames and vrimagechannelnames reports

---
 R/image.R                 | 12 ++++++++++--
 R/objects.R               |  7 ++++++-
 docs/importingdata.Rmd    |  4 ++--
 docs/importingdata.html   |  4 ++--
 docs/multiomic.Rmd        |  6 +++---
 docs/multiomic.html       |  6 +++---
 docs/nicheclustering.html |  4 +++-
 docs/registration.Rmd     | 16 ++++++++--------
 docs/registration.html    | 18 +++++++++---------
 docs/roianalysis.Rmd      | 16 +++++++++-------
 docs/roianalysis.html     | 16 +++++++++++-----
 docs/voltronobjects.Rmd   |  8 ++++----
 docs/voltronobjects.html  |  8 ++++----
 13 files changed, 74 insertions(+), 51 deletions(-)

diff --git a/R/image.R b/R/image.R
index 5ba9e20..108c75a 100644
--- a/R/image.R
+++ b/R/image.R
@@ -736,6 +736,9 @@ vrMainSpatial.vrAssayV2 <- vrMainSpatial.vrAssay
 #' @export
 vrImageNames.VoltRon <- function(object, assay = NULL){
 
+  # sample metadata
+  sample.metadata <- SampleMetadata(object)
+  
   # get assay names
   assay_names <- vrAssayNames(object, assay = assay)
 
@@ -744,7 +747,8 @@ vrImageNames.VoltRon <- function(object, assay = NULL){
     if(assay == "all"){
       spatial_names <- unlist(lapply(assay_names, function(x) paste(vrSpatialNames(object[[x]]), collapse = ",")))
       main_spatial_names <- unlist(lapply(assay_names, function(x) vrMainSpatial(object[[x]])))
-      spatial_names <- data.frame(Assay = assay_names, Spatial = spatial_names, Main = main_spatial_names)
+      # spatial_names <- data.frame(Assay = assay_names, Spatial = spatial_names, Main = main_spatial_names)
+      spatial_names <- data.frame(sample.metadata[assay_names,], Spatial = spatial_names, Main = main_spatial_names)
       return(spatial_names)
     }
   }
@@ -901,6 +905,9 @@ vrImageChannelNames.VoltRon <- function(object, assay = NULL){
     assay_names <- vrAssayNames(object, assay = assay)
   }
 
+  # sample metadata
+  sample.metadata <- SampleMetadata(object)
+  
   # get image names
   spatial_names <- unlist(lapply(assay_names, function(x) vrMainSpatial(object[[x]])))
 
@@ -908,7 +915,8 @@ vrImageChannelNames.VoltRon <- function(object, assay = NULL){
   image_channels <- unlist(lapply(assay_names, function(x) paste(vrImageChannelNames(object[[x]]), collapse = ",")))
 
   # return data
-  image_data <- data.frame(Assay = assay_names, Spatial = spatial_names, Channels = image_channels)
+  # image_data <- data.frame(Assay = assay_names, Spatial = spatial_names, Channels = image_channels)
+  image_data <- data.frame(sample.metadata[assay_names,], Spatial = spatial_names, Channels = image_channels)
 
   # return
   return(image_data)
diff --git a/R/objects.R b/R/objects.R
index 525d8c2..368453a 100644
--- a/R/objects.R
+++ b/R/objects.R
@@ -1574,7 +1574,12 @@ Metadata.VoltRon <- function(object, assay = NULL, type = NULL) {
 
       # replace data
       if(!inherits(metadata, "DataFrame")){
-        metadata[match(value$id, metadata$id), ] <- value
+        # TODO: is this replace method appropriate for all dataframe types ? 
+        # metadata[match(value$id, metadata$id), ] <- value
+        ind <- match(value$id, metadata$id)
+        for(cur_col in new_columns){
+          metadata[[cur_col]][ind] <- value[[cur_col]]
+        }
       } else {
         ind <- match(as.vector(value$id), as.vector(metadata$id))
         for(cur_col in new_columns){
diff --git a/docs/importingdata.Rmd b/docs/importingdata.Rmd
index 23be8f6..be7208f 100644
--- a/docs/importingdata.Rmd
+++ b/docs/importingdata.Rmd
@@ -630,8 +630,8 @@ vrImageChannelNames(vr_object)
 ```
 
 ```
-   Assay Image  Channels
-1 Assay1  MELC DAPI,CD45
+       Assay    Layer         Sample Spatial  Channels
+Assay1  MELC Section1 control_case_3    MELC DAPI,CD45
 ```
 
 You can extract each of these channels individually.
diff --git a/docs/importingdata.html b/docs/importingdata.html
index d1589e6..236f338 100644
--- a/docs/importingdata.html
+++ b/docs/importingdata.html
@@ -1153,8 +1153,8 @@ 
    Custom VoltRon objects
    
 
    These channels then can be interrogated and used as background images
 for spatial plots and spatial feature plots as well.
    
 
    vrImageChannelNames(vr_object)
    
-
       Assay Image  Channels
-1 Assay1  MELC DAPI,CD45
    
+
           Assay    Layer         Sample Spatial  Channels
+Assay1  MELC Section1 control_case_3    MELC DAPI,CD45
    
 
    You can extract each of these channels individually.
    
 
    vrImages(vr_object, name = "MELC", channel = "DAPI")
 vrImages(vr_object, name = "MELC", channel = "CD45")
    
diff --git a/docs/multiomic.Rmd b/docs/multiomic.Rmd
index 2d183be..bdc62b9 100644
--- a/docs/multiomic.Rmd
+++ b/docs/multiomic.Rmd
@@ -217,9 +217,9 @@ vrImageChannelNames(vr2_merged_acute1)
 ```
 
 ```
-   Assay  Spatial  Channels
-1 Assay7     main  DAPI,H&E
-2 Assay8     main  DAPI,H&E
+               Assay    Layer       Sample  Spatial Channels
+Assay7        Xenium Section1 acute case 1     main DAPI,H&E
+Assay8    Xenium_mol Section1 acute case 1     main DAPI,H&E
 ```
 
 
    
diff --git a/docs/multiomic.html b/docs/multiomic.html
index da2ef42..09e54b1 100644
--- a/docs/multiomic.html
+++ b/docs/multiomic.html
@@ -634,9 +634,9 @@ 
    Automated H&E Registration
    
 
    We can now observe the new channels available for the both molecule
 and cell-level assays of Xenium data.
    
 
    vrImageChannelNames(vr2_merged_acute1)
    
-
       Assay  Spatial  Channels
-1 Assay7     main  DAPI,H&E
-2 Assay8     main  DAPI,H&E
    
+
                   Assay    Layer       Sample  Spatial Channels
+Assay7        Xenium Section1 acute case 1     main DAPI,H&E
+Assay8    Xenium_mol Section1 acute case 1     main DAPI,H&E
    
 

    
 
    You can also add the VoltRon object of H&E data as an additional
 assay of the Xenium section such that one layer includes cell,
diff --git a/docs/nicheclustering.html b/docs/nicheclustering.html
index 058e7e7..dcbfa76 100644
--- a/docs/nicheclustering.html
+++ b/docs/nicheclustering.html
@@ -637,7 +637,9 @@ 
    Clustering
    
 
    Using normalized cell type abundances, we can now generate k-nearest
 neighbor graphs and cluster the graph using leiden method.
    
 
    MBrain_Sec <- getProfileNeighbors(MBrain_Sec, data.type = "norm", method = "SNN")
-MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.6, graph = "SNN")
    
+vrGraphNames(MBrain_Sec)
    
+
    [1] "SNN"
    
+
    MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.6, graph = "SNN")
    
 

    
 
    
 
    
diff --git a/docs/registration.Rmd b/docs/registration.Rmd
index 72ad012..a54fc5e 100644
--- a/docs/registration.Rmd
+++ b/docs/registration.Rmd
@@ -219,10 +219,10 @@ vrSpatialNames(VRBlock, assay = "all")
 ```
 
 ```
-   Assay       Spatial     Main
-1 Assay1 main,main_reg main_reg
-2 Assay2          main     main
-3 Assay3 main,main_reg main_reg
+        Assay    Layer   Sample       Spatial     Main
+Assay1 Xenium Section1 10XBlock main,main_reg main_reg
+Assay2 Visium Section2 10XBlock          main     main
+Assay3 Xenium Section3 10XBlock main,main_reg main_reg
 ```
 
 
    
@@ -481,7 +481,7 @@ vrImages(Xen_R1_image)
 
 We can use the **registerSpatialData** function to warp/align images across multiple VoltRon objects and define these aligned images additional channels of existing coordinate systems of assays in one of these VoltRon objects. 
 
-First we align the H&E image to the DAPI image of the Xenium replicate. Similar to the first use case, we need to negate the DAPI image and change the alignment of the image to match it with the H&E image. We can also adjust the resolution of the H&E image to 9103.71x6768.63.
+First we align the H&E image to the DAPI image of the Xenium replicate. Similar to the first use case, we need to negate the DAPI image and change the alignment of the image to match it with the H&E image. We can also scale the resolution of the H&E image to 9103.71x6768.63.
 
 ```{r eval = FALSE, class.source="watch-out"}
 xen_reg <- registerSpatialData(object_list = list(Xen_R1, Xen_R1_image))
@@ -491,7 +491,7 @@ xen_reg <- registerSpatialData(object_list = list(Xen_R1, Xen_R1_image))
 
 
    
 
-Now we create a new channel for the existing coordinate system of the Xenium data. Here, the key of the registered H&E image will be **H&E_reg**. We choose the destination of the registered image which is the first Assay of the Xenium data (i.e. **Assay1**). The original DAPI coordinate system, and we give a name for the new image/channel which is **H&E**.
+Now we create a new channel for the existing coordinate system of the Xenium data. Here, the spatial key of the registered H&E image will be **main_reg**. We choose the destination of the registered image which is the first Assay of the Xenium data (i.e. **Assay1**). The original DAPI coordinate system, and we give a name for the new image/channel which is **H&E**.
 
 ```{r eval = FALSE, class.source="watch-out"}
 Xen_R1_image_reg <- xen_reg$registered_spat[[2]]
@@ -505,8 +505,8 @@ vrImageChannelNames(Xen_R1)
 ```
 
 ```
-   Assay Spatial Channels
-1 Assay1    main DAPI,H&E
+       Assay    Layer           Sample Spatial                                               Channels
+Assay1 GeoMx Section1 prolonged case 4    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
 ```
 
 We can call the registered H&E image of the Xenium data or later put the aligned H&E when calling **vrSpatialPlot** or **vrSpatialFeaturePlot**. 
diff --git a/docs/registration.html b/docs/registration.html
index 0ac6264..66e4ffc 100644
--- a/docs/registration.html
+++ b/docs/registration.html
@@ -667,10 +667,10 @@ 
    Combine VoltRon object
    
 where the registered system main_reg is the default
 one.
    
 
    vrSpatialNames(VRBlock, assay = "all")
    
-
       Assay       Spatial     Main
-1 Assay1 main,main_reg main_reg
-2 Assay2          main     main
-3 Assay3 main,main_reg main_reg
    
+
            Assay    Layer   Sample       Spatial     Main
+Assay1 Xenium Section1 10XBlock main,main_reg main_reg
+Assay2 Visium Section2 10XBlock          main     main
+Assay3 Xenium Section3 10XBlock main,main_reg main_reg
    
 

    
 
    
 
    
@@ -892,14 +892,14 @@ 
    Automated Image Alignment
    
 
    First we align the H&E image to the DAPI image of the Xenium
 replicate. Similar to the first use case, we need to negate the DAPI
 image and change the alignment of the image to match it with the H&E
-image. We can also adjust the resolution of the H&E image to
+image. We can also scale the resolution of the H&E image to
 9103.71x6768.63.
    
 
    xen_reg <- registerSpatialData(object_list = list(Xen_R1, Xen_R1_image))
    
 
    
 

    
 
    Now we create a new channel for the existing coordinate system of the
-Xenium data. Here, the key of the registered H&E image will be
-H&E_reg. We choose the destination of the
+Xenium data. Here, the spatial key of the registered H&E image will
+be main_reg. We choose the destination of the
 registered image which is the first Assay of the Xenium data
 (i.e. Assay1). The original DAPI coordinate system, and
 we give a name for the new image/channel which is
@@ -909,8 +909,8 @@ 
    Automated Image Alignment
    
 
    We can now observe the new channels (H&E) available for the
 Xenium assay using vrImageChannelNames.
    
 
    vrImageChannelNames(Xen_R1)
    
-
       Assay Spatial Channels
-1 Assay1    main DAPI,H&E
    
+
           Assay    Layer           Sample Spatial                                               Channels
+Assay1 GeoMx Section1 prolonged case 4    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
    
 
    We can call the registered H&E image of the Xenium data or later
 put the aligned H&E when calling vrSpatialPlot or
 vrSpatialFeaturePlot.
    
diff --git a/docs/roianalysis.Rmd b/docs/roianalysis.Rmd
index befc280..1a487a7 100644
--- a/docs/roianalysis.Rmd
+++ b/docs/roianalysis.Rmd
@@ -458,7 +458,8 @@ VoltRon provides multiple registration methods to align images. Here, after runn
 
 ```{r eval = FALSE, class.source="watch-out"}
 GeoMxR1_subset <- subset(GeoMxR1, sample = c("prolonged case 4"))
-GeoMxReg <- registerSpatialData(reference_spatdata = GeoMxR1_subset, query_spatdata = vrHEimage)
+GeoMxReg <- registerSpatialData(reference_spatdata = GeoMxR1_subset, 
+                                query_spatdata = vrHEimage)
 ```
 
 
@@ -491,15 +492,14 @@ We can now observe the new channels (H&E) available for the GeoMx assay using **
 vrImageChannelNames(GeoMxR1_subset)
 ```
 
-```
-   Assay Spatial                                               Channels
-1 Assay1    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
-```
+
           Assay    Layer           Sample Spatial                                               Channels
+Assay1 GeoMx Section1 prolonged case 4    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
    
 
 We can now visualize the ROIs and their annotations where the registered H&E visible in the background. We define the spatial key **main** and the channel name **H&E**. 
 
 ```{r eval = FALSE, class.source="watch-out"}
-vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E")
+vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, 
+              spatial = "main", channel = "H&E")
 ```
 
 
@@ -507,7 +507,9 @@ vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "mai
 Interactive Visualization can also be used to zoom in to ROIs and investigate the pathology associated with GeoMx ROIs labeled as fibrotic. 
 
 ```{r eval = FALSE, class.source="watch-out"}
-vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E", interactive = TRUE)
+vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, 
+              spatial = "main", channel = "H&E", 
+              interactive = TRUE)
 ```
 
 
diff --git a/docs/roianalysis.html b/docs/roianalysis.html
index 900b588..18c2e9c 100644
--- a/docs/roianalysis.html
+++ b/docs/roianalysis.html
@@ -941,7 +941,8 @@ 
    H&E Image Integration
    
 method allows correct local deformations between the perspective
 transformed H&E image and the GeoMx image for a more accurate
    
 
    GeoMxR1_subset <- subset(GeoMxR1, sample = c("prolonged case 4"))
-GeoMxReg <- registerSpatialData(reference_spatdata = GeoMxR1_subset, query_spatdata = vrHEimage)
    
+GeoMxReg <- registerSpatialData(reference_spatdata = GeoMxR1_subset, 
+                                query_spatdata = vrHEimage)
    
 
    
 
    The result of the registration is a list of registered VoltRon
 objects which we can use for parsing the registered image as well. In
@@ -962,17 +963,22 @@ 
    H&E Image Integration
    
 separate channel along with the originally available antibody channels
 of the original GeoMx experiment.
    
 
    vrImageChannelNames(GeoMxR1_subset)
    
-
       Assay Spatial                                               Channels
-1 Assay1    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
    
+
    
+
           Assay    Layer           Sample Spatial                                               Channels
+Assay1 GeoMx Section1 prolonged case 4    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E
    
+
    
 
    We can now visualize the ROIs and their annotations where the
 registered H&E visible in the background. We define the spatial key
 main and the channel name H&E.
    
-
    vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E")
    
+
    vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, 
+              spatial = "main", channel = "H&E")
    
 
    
 
    Interactive Visualization can also be used to zoom in to ROIs and
 investigate the pathology associated with GeoMx ROIs labeled as
 fibrotic.
    
-
    vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, spatial = "main", channel = "H&E", interactive = TRUE)
    
+
    vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7, 
+              spatial = "main", channel = "H&E", 
+              interactive = TRUE)
    diff --git a/docs/voltronobjects.Rmd b/docs/voltronobjects.Rmd index a6c72e1..c5af598 100644 --- a/docs/voltronobjects.Rmd +++ b/docs/voltronobjects.Rmd @@ -380,8 +380,8 @@ vrImageChannelNames(melc_data, assay = "Assay1") ``` ``` - Assay Spatial Channels -1 Assay1 MELC DAPI,CD45 + Assay Layer Sample Spatial Channels +Assay1 MELC Section1 control_case_3 MELC DAPI,CD45 ```
    @@ -446,8 +446,8 @@ vrImageChannelNames(melc_data) ``` ``` - Assay Spatial Channels -1 Assay1 MELC DAPI,CD45,combined + Assay Layer Sample Spatial Channels +Assay1 MELC Section1 control_case_3 MELC DAPI,CD45,combined ``` ```{r eval = FALSE, class.source="watch-out"} diff --git a/docs/voltronobjects.html b/docs/voltronobjects.html index ed83e67..ce579b2 100644 --- a/docs/voltronobjects.html +++ b/docs/voltronobjects.html @@ -690,8 +690,8 @@

    Channel Names

    vrImageChannelNames(melc_data)
 vrImageChannelNames(melc_data, assay = "MELC")
 vrImageChannelNames(melc_data, assay = "Assay1")
    -
       Assay Spatial  Channels
-1 Assay1    MELC DAPI,CD45
    +
           Assay    Layer         Sample Spatial  Channels
+Assay1  MELC Section1 control_case_3    MELC DAPI,CD45


    @@ -733,8 +733,8 @@

    Combining Image Channels

    These new images can be stored as new channels within the same image object, and called later again

    vrImageChannelNames(melc_data)
    -
       Assay Spatial           Channels
-1 Assay1    MELC DAPI,CD45,combined
    +
           Assay    Layer         Sample Spatial           Channels
+Assay1  MELC Section1 control_case_3    MELC DAPI,CD45,combined
    vrImages(melc_data, channel = "combined")


    From ff59e0fe1aff1c11e4479dc2e91484cef0fd00ac Mon Sep 17 00:00:00 2001 From: Artur-man Date: Sun, 12 Jan 2025 12:20:53 +0100 Subject: [PATCH 19/26] remove reshape2 as a dependancy --- DESCRIPTION | 14 +++++++------- NAMESPACE | 2 -- R/conversion.R | 5 ++--- R/import.R | 41 ++++++++++++++++++++--------------------- R/integration.R | 3 ++- R/spatial.R | 2 +- R/visualization.R | 21 +++++++++++---------- R/zzz.R | 2 +- man/VoltRon-package.Rd | 2 +- 9 files changed, 45 insertions(+), 47 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 0e5e8ba..453a12e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,20 +1,19 @@ Package: VoltRon Type: Package -Title: VoltRon for Spatial Data Integration and Analysis +Title: VoltRon for Spatial Omics Data Integration and Analysis Version: 0.2.0 Depends: R (>= 4.3.0) Author: Artür Manukyan, Ella Bahry, Raj Prateek Rai, Wei-Che Ko, Deborah Schmidt, Markus Landthaler, Altuna Akalin Maintainer: Artür Manukyan -Description: VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells and even molecules. +Description: VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells, molecules and event tiles. License: MIT + file LICENSE SystemRequirements: OpenCV 4.8 (or higher): libopencv-dev (Debian, Ubuntu) or opencv-devel (Fedora) Encoding: UTF-8 RoxygenNote: 7.3.2 biocViews: -Imports: methods, S4Arrays, grDevices, data.table, EBImage, RcppAnnoy, RANN, - Matrix, dplyr, ggplot2, ggrepel, igraph, irlba, rjson, magick, ids, - sp, reshape2, rlang, ggpubr, shiny, shinyjs, stringr, uwot, RCDT, - basilisk, reticulate +Imports: methods, grDevices, data.table, Matrix, S4Arrays, ids, RcppAnnoy, RANN, igraph, + dplyr, ggplot2, ggrepel, ggpubr, rjson, magick, EBImage, + sp, rlang, shiny, shinyjs, stringr, irlba, uwot, RCDT, basilisk, reticulate LinkingTo: Rcpp, RcppArmadillo (>= 0.4) Collate: 'RcppExports.R' @@ -41,6 +40,7 @@ Collate: 'spatial.R' 'visualization.R' Suggests: + testthat (>= 3.0.0), SpatialExperiment, SingleCellExperiment, Seurat, @@ -52,11 +52,11 @@ Suggests: XML, ComplexHeatmap, xlsx, + reshape2, tiledb, tiledbsc, arrow, vitessceR, - testthat (>= 3.0.0), geojsonR, circlize, rstudioapi, diff --git a/NAMESPACE b/NAMESPACE index 0dd2059..e3976b9 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -393,8 +393,6 @@ importFrom(methods,is) importFrom(methods,new) importFrom(methods,slot) importFrom(methods,slotNames) -importFrom(reshape2,acast) -importFrom(reshape2,melt) importFrom(reticulate,import) importFrom(rjson,fromJSON) importFrom(rlang,"%||%") diff --git a/R/conversion.R b/R/conversion.R index f8326b6..2099823 100644 --- a/R/conversion.R +++ b/R/conversion.R @@ -767,8 +767,7 @@ as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name # spatial points sppoints <- rownames(metadata)[metadata$sample_id == samp] - sppoints_nopostfix <- gsub("_Assay[0-9]+$", "", sppoints) - + # metadata cur_metadata <- metadata[sppoints,] @@ -816,7 +815,7 @@ as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name spatialpoints_assay <- stringr::str_extract(spatialpoints, "Assay[0-9]+$") if(embeddings_flag){ for(embed_name in names(embedding_list)){ - cur_embedding <- embedding_list[[embed_name]][cells,] + cur_embedding <- embedding_list[[embed_name]][sppoints,] rownames(cur_embedding) <- spatialpoints vrEmbeddings(voltron_list[[samp]], type = embed_name) <- cur_embedding } diff --git a/R/import.R b/R/import.R index 31de778..ea873c6 100644 --- a/R/import.R +++ b/R/import.R @@ -201,9 +201,6 @@ importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_na #' generateXeniumImage <- function(dir.path, increase.contrast = TRUE, resolution_level = 7, overwrite_resolution = FALSE, output.path = NULL, file.name = "morphology_lowres.tif", ...) { - if (!requireNamespace('RBioFormats')) - stop("Please install RBioFormats package to read the ome.tiff file!") - # file path to either Xenium output folder or specified folder file.path <- paste0(dir.path, "/", file.name) output.file <- paste0(output.path, "/", file.name) @@ -213,7 +210,7 @@ generateXeniumImage <- function(dir.path, increase.contrast = TRUE, resolution_l message(paste0(file.name, " already exists!")) } else { if (!requireNamespace('RBioFormats')) - stop("Please install RBioFormats package to read the ome.tiff file!") + stop("Please install RBioFormats package to extract xml from the ome.tiff file!: BiocManager::install('RBioFormats')") if(dir.exists(paste0(dir.path, "/morphology_focus"))){ message("Loading morphology_focus_0000.ome.tif ...") morphology_image_lowres <- RBioFormats::read.image(paste0(dir.path, "/morphology_focus/morphology_focus_0000.ome.tif"), @@ -402,7 +399,7 @@ importVisiumHD <- function(dir.path, bin.size = "8", selected_assay = "Gene Expr # coordinates if(!requireNamespace("arrow")) - stop("Please install arrow package to import VisiumHD!") + stop("Please install arrow package to import VisiumHD!: install.packages('arrow')") coords_file <- list.files(paste0(dir.path, "/spatial/"), full.names = TRUE) coords_file <- coords_file[grepl("tissue_positions",coords_file)] if(length(coords_file) == 1){ @@ -461,7 +458,7 @@ import10Xh5 <- function(filename){ # check package if(!requireNamespace("hdf5r")){ - stop("You have to install the hdf5r package!: install.packages('hdf5r'')") + stop("You have to install the hdf5r package!: install.packages('hdf5r')") } # check file @@ -588,7 +585,7 @@ importGeoMx <- function(dcc.path, pkc.file, summarySegment, summarySegmentSheetN if(file.exists(summarySegment)){ if(grepl(".xls$|.xlsx$", summarySegment)){ if (!requireNamespace('xlsx')) - stop("Please install xlsx package for using the read.xlsx function!") + stop("Please install xlsx package for using the read.xlsx function!: install.packages('xlsx')") if(!is.null(summarySegmentSheetName)){ segmentsummary <- xlsx::read.xlsx(summarySegment, sheetName = summarySegmentSheetName) } else { @@ -872,7 +869,7 @@ importGeoMxSegments <- function(ome.tiff, summary, imageinfo){ if(file.exists(ome.tiff)){ if(grepl(".ome.tiff$|.ome.tif$", ome.tiff)){ if (!requireNamespace('RBioFormats')) - stop("Please install RBioFormats package to extract xml from the ome.tiff file!") + stop("Please install RBioFormats package to extract xml from the ome.tiff file!: BiocManager::install('RBioFormats')") if (!requireNamespace('XML')) stop("Please install XML package to extract xml from the ome.tiff file!") omexml <- RBioFormats::read.omexml(ome.tiff) @@ -964,7 +961,7 @@ importGeoMxChannels <- function(ome.tiff, summary, imageinfo, resolution_level){ if(file.exists(ome.tiff)){ if(grepl(".ome.tiff$|.ome.tif$", ome.tiff)){ if (!requireNamespace('RBioFormats')) - stop("Please install RBioFormats package to extract xml from the ome.tiff file!") + stop("Please install RBioFormats package to extract xml from the ome.tiff file!: BiocManager::install('RBioFormats')") if (!requireNamespace('XML')) stop("Please install XML package to extract xml from the ome.tiff file!") omexml <- RBioFormats::read.omexml(ome.tiff) @@ -1161,12 +1158,15 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", #' #' @importFrom magick image_read image_contrast #' @importFrom EBImage writeImage -#' @importFrom reshape2 acast #' #' @export -#' generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = NULL, ...) { + # check package + if(!requireNamespace("reshape2")){ + stop("You have to install the reshape2 package!: install.packages('reshape2')") + } + # file path to either Xenium output folder or specified folder file.path <- paste0(dir.path, "/CellComposite_lowres.tif") output.file <- paste0(output.path, "/CellComposite_lowres.tif") @@ -1285,7 +1285,7 @@ importGenePS <- function (dir.path, assay_name = "GenePS", sample_name = NULL, u # check RBioFormats if (!requireNamespace('RBioFormats')) - stop("Please install RBioFormats package to extract xml from the ome.tiff file!") + stop("Please install RBioFormats package to extract xml from the ome.tiff file!: BiocManager::install('RBioFormats')") # check image image_file <- paste0(dir.path, "/images/DAPI.tiff") @@ -1415,9 +1415,9 @@ importGenePS <- function (dir.path, assay_name = "GenePS", sample_name = NULL, u #' importSTOmics <- function(h5ad.path, assay_name = "STOmics", sample_name = NULL, image_name = "main", channel_name = "H&E", ...) { - # check Seurat package + # check package if(!requireNamespace('anndataR')) - stop("Please install anndataR package") + stop("Please install anndataR package!: devtools::install_github('scverse/anndataR')") # get h5ad data stdata <- anndataR::read_h5ad(h5ad.path, to = "HDF5AnnData") @@ -1567,9 +1567,6 @@ readPhenoCyclerMat <- function( filter = 'DAPI|Blank|Empty', inform.quant = c('mean', 'total', 'min', 'max', 'std') ) { - if (!requireNamespace("data.table", quietly = TRUE)) { - stop("Please install 'data.table' for this function") - } # Check arguments if (!file.exists(filename)) { stop(paste("Can't file file:", filename)) @@ -1788,14 +1785,14 @@ readPhenoCyclerMat <- function( #' @param ... additional parameters passed to \link{formVoltRon} #' #' @importFrom methods as -#' @importFrom reshape2 melt +#' @importFrom Matrix t #' #' @export importOpenST <- function(h5ad.path, assay_name = "OpenST", sample_name = NULL, image_name = "main", channel_name = "H&E", ...) { - # check Seurat package + # check package if(!requireNamespace('anndataR')) - stop("Please install anndataR package") + stop("Please install anndataR package!: devtools::install_github('scverse/anndataR')") # get h5ad data stdata <- anndataR::read_h5ad(h5ad.path, to = "HDF5AnnData") @@ -1822,7 +1819,9 @@ importOpenST <- function(h5ad.path, assay_name = "OpenST", sample_name = NULL, i # get individual sections as voltron data sections <- unique(metadata$n_section) zlocation <- zlocation[order(sections)] - connectivity <- reshape2::melt(matrix(rep(1, length(sections)^2), nrow = length(sections)))[,1:2] + # connectivity <- reshape2::melt(matrix(rep(1, length(sections)^2), nrow = length(sections)))[,1:2] + connectivity <- data.frame(Var1 = rep(1:length(sections), length(sections)), + Var2 = rep(1:length(sections), each = length(sections))) sections <- sections[order(sections)] vr_data_list <- list() message("Creating Layers ...") diff --git a/R/integration.R b/R/integration.R index 50a7e7d..c76fe1a 100644 --- a/R/integration.R +++ b/R/integration.R @@ -404,7 +404,8 @@ getCellsFromTiles <- function(from_object, from_metadata = NULL, to_object, feat tile_to_cell <- knn_annoy(coords_tiles, coords_cells, k = k) names(tile_to_cell) <- c("nn.index", "nn.dist") tile_to_cell_nnid <- data.frame(id = rownames(coords_cells), tile_to_cell$nn.index) - tile_to_cell_nnid <- reshape2::melt(tile_to_cell_nnid, id.vars = "id") + # tile_to_cell_nnid <- reshape2::melt(tile_to_cell_nnid, id.vars = "id") + tile_to_cell_nnid <- data.table::melt(tile_to_cell_nnid, id.vars = "id") tile_id <- vrSpatialPoints(from_object)[tile_to_cell_nnid$value] tile_to_cell_nnid <- tile_to_cell_nnid$id diff --git a/R/spatial.R b/R/spatial.R index ecc8726..77bb601 100644 --- a/R/spatial.R +++ b/R/spatial.R @@ -23,7 +23,7 @@ NULL #' @importFrom igraph add_edges simplify make_empty_graph vertices #' @importFrom RCDT delaunay #' @importFrom RANN nn2 -#' @importFrom data.table melt +#' @importFrom data.table data.table melt #' @importFrom stats dist #' #' @export diff --git a/R/visualization.R b/R/visualization.R index 82b1cd4..66fc8ef 100644 --- a/R/visualization.R +++ b/R/visualization.R @@ -1939,7 +1939,7 @@ vrHeatmapPlot <- function(object, assay = NULL, features = NULL, group.by = "clu #' @param nrow row wise number of plots, for \link{ggarrange} #' #' @import ggplot2 -#' @importFrom reshape2 melt +#' @importFrom data.table data.table melt #' #' @export #' @@ -1988,8 +1988,9 @@ vrViolinPlot <- function(object, features = NULL, assay = NULL, group.by = "Samp group.by = as.factor(metadata[[group.by]]), assay_title = assay_title, spatialpoints = rownames(metadata)) - ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("group.by", "assay_title", "spatialpoints")) - + # ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("group.by", "assay_title", "spatialpoints")) + ggplotdatax <- data.table::melt(data.table::data.table(ggplotdatax), id.var = c("group.by", "assay_title", "spatialpoints")) + # visualize points on violin if(plot.points){ gg <- ggplot(ggplotdatax, aes(x = group.by, y = value, color = group.by)) + @@ -2036,7 +2037,7 @@ vrViolinPlot <- function(object, features = NULL, assay = NULL, group.by = "Samp #' @param nrow row wise number of plots, for \link{ggarrange} #' #' @import ggplot2 -#' @importFrom reshape2 melt +#' @importFrom data.table data.table melt #' #' @export #' @@ -2120,7 +2121,7 @@ vrBarPlot <- function(object, features = NULL, assay = NULL, x.label = NULL, gro group.by = group.by.col, assay_title = assay_title, spatialpoints = spatialpoints) - ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("x.label", "assay_title", "group.by", "spatialpoints")) + ggplotdatax <- data.table::melt(data.table::data.table(ggplotdatax), id.var = c("x.label", "assay_title", "group.by", "spatialpoints")) gg <- ggplot(ggplotdatax, aes(x = x.label, y = value, fill = factor(group.by, levels = unique(group.by)))) + geom_bar(stat = "identity") + @@ -2152,7 +2153,7 @@ vrBarPlot <- function(object, features = NULL, assay = NULL, x.label = NULL, gro split.by = split.by.col, assay_title = assay_title, spatialpoints = spatialpoints) - ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("x.label", "assay_title", "group.by", "split.by", "spatialpoints")) + ggplotdatax <- data.table::melt(data.table::data.table(ggplotdatax), id.var = c("x.label", "assay_title", "group.by", "split.by", "spatialpoints")) gg <- ggplot(ggplotdatax, aes(x = x.label, y = value, fill = factor(group.by, levels = unique(group.by)))) + geom_bar(stat = "identity") + @@ -2183,7 +2184,7 @@ vrBarPlot <- function(object, features = NULL, assay = NULL, x.label = NULL, gro #' @param nrow row wise number of plots, for \link{ggarrange} #' #' @import ggplot2 -#' @importFrom reshape2 melt +#' @importFrom data.table data.table melt #' #' @export #' @@ -2241,7 +2242,7 @@ vrProportionPlot <- function(object, assay = NULL, x.label = NULL, x.label = x.label, assay_title = assay_title, spatialpoints = spatialpoints) - ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("x.label", "assay_title", "spatialpoints")) + ggplotdatax <- data.table::melt(data.table::data.table(ggplotdatax), id.var = c("x.label", "assay_title", "spatialpoints")) ggplotdatax <- ggplotdatax[ggplotdatax$value > 0,] gg <- ggplot(ggplotdatax, aes(x = x.label, y = value, fill = variable)) + geom_bar(stat = "identity") + @@ -2254,7 +2255,7 @@ vrProportionPlot <- function(object, assay = NULL, x.label = NULL, x.label = x.label, assay_title = assay_title, spatialpoints = rownames(metadata)) - ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("x.label", "assay_title", "spatialpoints")) + ggplotdatax <- data.table::melt(data.table::data.table(ggplotdatax), id.var = c("x.label", "assay_title", "spatialpoints")) ggplotdatax <- ggplotdatax[ggplotdatax$value > 0,] gg <- ggplot(ggplotdatax, aes(x = x.label, y = value, fill = variable)) + geom_bar(stat = "identity") + @@ -2274,7 +2275,7 @@ vrProportionPlot <- function(object, assay = NULL, x.label = NULL, assay_title = assay_title, split.by = split.by.col, spatialpoints = spatialpoints) - ggplotdatax <- reshape2::melt(ggplotdatax, id.var = c("x.label", "assay_title", "split.by", "spatialpoints")) + ggplotdatax <- data.table::melt(data.table::data.table(ggplotdatax), id.var = c("x.label", "assay_title", "split.by", "spatialpoints")) ggplotdatax <- ggplotdatax[ggplotdatax$value > 0,] gg <- ggplot(ggplotdatax, aes(x = x.label, y = value, fill = variable)) + geom_bar(stat = "identity") + diff --git a/R/zzz.R b/R/zzz.R index 89fcdae..47c68cb 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -9,5 +9,5 @@ utils::globalVariables(c("Assay", "KeyPoint", "NewSample", "Sample", "rx", "ry", "RTS_ID", "Module", "assoc_test", "from_value", "mean_value", "n", "p.adjust", "p_assoc", "p_segreg", "segreg_test", "to_value", "type", "region", "sample.metadata", "cell_id", "from.x", "from.y", "to.x", "to.y", ".SD", ".hasSlot", ":=", "assay_id", "id", "slideID", "J", "majortype", "addImg", - "X1", "in_tissue", "fill", "colour", "color", "color_group", "group"), + "X1", "V1", "in_tissue", "fill", "colour", "color", "color_group", "group"), package = "VoltRon", add = FALSE) diff --git a/man/VoltRon-package.Rd b/man/VoltRon-package.Rd index a1c0279..6c939e1 100644 --- a/man/VoltRon-package.Rd +++ b/man/VoltRon-package.Rd @@ -5,5 +5,5 @@ \alias{VoltRon-package} \title{VoltRon: VoltRon for Spatial Data Integration and Analysis} \description{ -VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells and even molecules. +VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells, molecules and event tiles. } From a39d58eaf787a861ff7b11e2b639e5a0fa4bf25f Mon Sep 17 00:00:00 2001 From: Artur-man Date: Sun, 12 Jan 2025 13:21:30 +0100 Subject: [PATCH 20/26] remove reticulate and basilisk from imports --- .github/workflows/check.yml | 2 ++ DESCRIPTION | 8 +++--- NAMESPACE | 5 ---- R/auxiliary.R | 29 +++++++++++++-------- R/conversion.R | 43 ++++++++++++++++++++++++-------- inst/extdata/Dockerfile | 6 +++-- man/VoltRon-package.Rd | 2 +- tests/testthat/test_conversion.R | 5 ++-- 8 files changed, 67 insertions(+), 33 deletions(-) diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml index 7e6bae8..9e368fc 100644 --- a/.github/workflows/check.yml +++ b/.github/workflows/check.yml @@ -148,6 +148,8 @@ jobs: # All Other dependencies - name: Install Suggested dependencies run: | + install.packages(c('reticulate')) + BiocManager::install(c('basilisk')) BiocManager::install(c('DelayedArray')) BiocManager::install(c('HDF5Array')) remotes::install_github('bnprks/BPCells/r@v0.3.0') diff --git a/DESCRIPTION b/DESCRIPTION index 453a12e..eebf566 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,9 +11,9 @@ SystemRequirements: OpenCV 4.8 (or higher): libopencv-dev (Debian, Ubuntu) or op Encoding: UTF-8 RoxygenNote: 7.3.2 biocViews: -Imports: methods, grDevices, data.table, Matrix, S4Arrays, ids, RcppAnnoy, RANN, igraph, - dplyr, ggplot2, ggrepel, ggpubr, rjson, magick, EBImage, - sp, rlang, shiny, shinyjs, stringr, irlba, uwot, RCDT, basilisk, reticulate +Imports: methods, grDevices, data.table, Matrix, S4Arrays, ids, RcppAnnoy, + RANN, igraph, dplyr, ggplot2, ggrepel, ggpubr, rjson, magick, + EBImage, sp, rlang, shiny, shinyjs, stringr, irlba, uwot, RCDT LinkingTo: Rcpp, RcppArmadillo (>= 0.4) Collate: 'RcppExports.R' @@ -51,6 +51,8 @@ Suggests: RBioFormats, XML, ComplexHeatmap, + basilisk, + reticulate, xlsx, reshape2, tiledb, diff --git a/NAMESPACE b/NAMESPACE index e3976b9..4b6f136 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -293,10 +293,6 @@ importFrom(Matrix,t) importFrom(RANN,nn2) importFrom(RCDT,delaunay) importFrom(RcppAnnoy,AnnoyEuclidean) -importFrom(basilisk,BasiliskEnvironment) -importFrom(basilisk,basiliskRun) -importFrom(basilisk,basiliskStart) -importFrom(basilisk,basiliskStop) importFrom(data.table,alloc.col) importFrom(data.table,as.data.table) importFrom(data.table,chmatch) @@ -393,7 +389,6 @@ importFrom(methods,is) importFrom(methods,new) importFrom(methods,slot) importFrom(methods,slotNames) -importFrom(reticulate,import) importFrom(rjson,fromJSON) importFrom(rlang,"%||%") importFrom(rlang,'!!') diff --git a/R/auxiliary.R b/R/auxiliary.R index d01d769..60e86e9 100644 --- a/R/auxiliary.R +++ b/R/auxiliary.R @@ -194,19 +194,19 @@ rownames(.dccMetadata[["protocolData"]])[rownames(.dccMetadata[["protocolData"]] # Basilisk Environment #### #### -#' The Python Basilisk environment +#' get the Python Basilisk environment #' #' Defines a conda environment via Basilisk, which is used to convert R objects to Zarr stores. #' -#' @importFrom basilisk BasiliskEnvironment -#' #' @keywords internal #' #' @noRd -py_env <- basilisk::BasiliskEnvironment( - envname="VoltRon_basilisk_env", - pkgname="VoltRon", - packages=c( +getBasilisk <- function(){ + + if(!requireNamespace('basilisk')) + stop("Please install basilisk package!: BiocManager::install('basilisk')") + + basilisk.packages=c( "numpy==1.*", "pandas==1.*", "anndata==0.7.*", @@ -219,11 +219,20 @@ py_env <- basilisk::BasiliskEnvironment( "zarr==2.*", "numcodecs==0.*", "tifffile==2024.2.12" - ), - pip=c( + ) + basilisk.pip=c( "ome-zarr==0.2.1" ) -) + + py_env <- basilisk::BasiliskEnvironment( + envname="VoltRon_basilisk_env", + pkgname="VoltRon", + packages=basilisk.packages, + pip=basilisk.pip + ) + + py_env +} #### # Other Auxiliary tools #### diff --git a/R/conversion.R b/R/conversion.R index 2099823..66c2ede 100644 --- a/R/conversion.R +++ b/R/conversion.R @@ -201,9 +201,9 @@ as.Seurat <- function(object, cell.assay = NULL, molecule.assay = NULL, image_ke #' convertAnnDataToVoltRon <- function(file, AssayID = NULL, ...){ - # check Seurat package + # check anndata package if(!requireNamespace('anndata')) - stop("Please install anndata package") + stop("Please install anndata package!: install.packages('anndata')") # read anndata adata <- anndata::read_h5ad(file) @@ -350,10 +350,15 @@ as.AnnData <- function(object, if(grepl(".zarr[/]?$", file)){ # check packages + if(!requireNamespace('basilisk')) + stop("Please install basilisk package!: BiocManager::install('basilisk')") + if(!requireNamespace('reticulate')) + stop("Please install reticulate package!: install.packages('reticulate')") if(!requireNamespace('DelayedArray')) stop("Please install DelayedArray package for using DelayedArray functions") # run basilisk to call zarr methods + py_env <- getBasilisk() proc <- basilisk::basiliskStart(py_env) on.exit(basilisk::basiliskStop(proc)) success <- basilisk::basiliskRun(proc, function(data, metadata, obsm, coords, segments, image_list, file) { @@ -440,8 +445,6 @@ as.AnnData <- function(object, #' @rdname as.Zarr #' -#' @importFrom basilisk basiliskStart basiliskStop basiliskRun -#' @importFrom reticulate import #' @importFrom magick image_raster #' @importFrom grDevices col2rgb #' @@ -450,6 +453,10 @@ as.Zarr.VoltRon <- function (object, out_path, image_id = "image_1") { # check packages + if(!requireNamespace('basilisk')) + stop("Please install basilisk package!: BiocManager::install('basilisk')") + if(!requireNamespace('reticulate')) + stop("Please install reticulate package!: install.packages('reticulate')") if(!requireNamespace('DelayedArray')) stop("Please install DelayedArray package for using DelayedArray functions") @@ -468,6 +475,7 @@ as.Zarr.VoltRon <- function (object, out_path, image_id = "image_1") } } + py_env <- getBasilisk() proc <- basilisk::basiliskStart(py_env) on.exit(basilisk::basiliskStop(proc)) success <- basilisk::basiliskRun(proc, function(datax, metadata, obsm, out_path) { @@ -498,15 +506,20 @@ as.Zarr.VoltRon <- function (object, out_path, image_id = "image_1") #' @rdname as.Zarr #' -#' @importFrom basilisk basiliskStart basiliskStop basiliskRun -#' @importFrom reticulate import #' @importFrom magick image_raster #' @importFrom grDevices col2rgb #' #' @export "as.Zarr.magick-image" <- function (object, out_path, image_id = "image_1") { + # check packages + if(!requireNamespace('basilisk')) + stop("Please install basilisk package!: BiocManager::install('basilisk')") + if(!requireNamespace('reticulate')) + stop("Please install reticulate package!: install.packages('reticulate')") + img_arr <- apply(as.matrix(magick::image_raster(object, tidy = FALSE)), c(1, 2), col2rgb) + py_env <- getBasilisk() proc <- basilisk::basiliskStart(py_env) on.exit(basilisk::basiliskStop(proc)) success <- basilisk::basiliskRun(proc, function(img_arr, image_id, out_path) { @@ -547,19 +560,24 @@ as.Zarr.VoltRon <- function (object, out_path, image_id = "image_1") #' @param out_path output path to ome.tiff file #' @param image_id image name #' -#' @importFrom basilisk basiliskStart basiliskStop basiliskRun -#' @importFrom reticulate import #' @importFrom magick image_raster #' @importFrom grDevices col2rgb #' #' @export as.OmeTiff <- function (object, out_path, image_id = "image_1"){ + # check packages + if(!requireNamespace('basilisk')) + stop("Please install basilisk package!: BiocManager::install('basilisk')") + if(!requireNamespace('reticulate')) + stop("Please install reticulate package!: install.packages('reticulate')") + # get image and transpose the array img_arr <- apply(as.matrix(magick::image_raster(object, tidy = FALSE)), c(1, 2), col2rgb) img_arr <- aperm(img_arr, c(2,3,1)) # run basilisk + py_env <- getBasilisk() proc <- basilisk::basiliskStart(py_env) on.exit(basilisk::basiliskStop(proc)) success <- basilisk::basiliskRun(proc, function(img_arr, image_id, out_path, e) { @@ -595,18 +613,23 @@ with tifffile.TiffWriter('", out_path, "') as tif: tif.write(tifimage, photometr #' @param out_path output path to ome.tiff file #' @param image_id image name #' -#' @importFrom basilisk basiliskStart basiliskStop basiliskRun -#' @importFrom reticulate import #' @importFrom magick image_raster #' @importFrom grDevices col2rgb #' #' @export as.OmeZarr <- function (object, out_path, image_id = "image_1"){ + # check packages + if(!requireNamespace('basilisk')) + stop("Please install basilisk package!: BiocManager::install('basilisk')") + if(!requireNamespace('reticulate')) + stop("Please install reticulate package!: install.packages('reticulate')") + # get image and transpose the array img_arr <- apply(as.matrix(magick::image_raster(object, tidy = FALSE)), c(1, 2), col2rgb) # run basilisk + py_env <- getBasilisk() proc <- basilisk::basiliskStart(py_env) on.exit(basilisk::basiliskStop(proc)) success <- basilisk::basiliskRun(proc, function(img_arr, image_id, out_path) { diff --git a/inst/extdata/Dockerfile b/inst/extdata/Dockerfile index 3816ac9..6f48072 100644 --- a/inst/extdata/Dockerfile +++ b/inst/extdata/Dockerfile @@ -22,10 +22,12 @@ RUN R -e "install.packages(c('shiny', 'devtools', 'BiocManager'), repos='http:// # Install VoltRon dependencies RUN R -e "install.packages(c('grDevices', 'data.table', 'RcppAnnoy', 'RANN', 'Matrix', 'dplyr', 'ggplot2', 'ggrepel', 'igraph', 'irlba', 'rjson', 'magick', 'ids', 'sp', 'reshape2', 'rlang', 'ggpubr', 'shinyjs'), repos='http://cran.rstudio.com/')" -RUN R -e "install.packages(c('stringr', 'uwot', 'RCDT', 'reticulate'), repos='http://cran.rstudio.com/')" -RUN R -e "BiocManager::install(c('EBImage', 'basilisk', 'S4Arrays'))" +RUN R -e "install.packages(c('stringr', 'uwot', 'RCDT'), repos='http://cran.rstudio.com/')" +RUN R -e "BiocManager::install(c('EBImage', 'S4Arrays'))" # Install Suggested dependencies +RUN R -e "install.packages(c('reticulate'))" +RUN R -e "BiocManager::install(c('basilisk'))" RUN R -e "BiocManager::install(c('DelayedArray'))" RUN R -e "BiocManager::install(c('HDF5Array'))" RUN R -e "remotes::install_github('bnprks/BPCells/r@v0.3.0')" diff --git a/man/VoltRon-package.Rd b/man/VoltRon-package.Rd index 6c939e1..e188a43 100644 --- a/man/VoltRon-package.Rd +++ b/man/VoltRon-package.Rd @@ -3,7 +3,7 @@ \docType{package} \name{VoltRon-package} \alias{VoltRon-package} -\title{VoltRon: VoltRon for Spatial Data Integration and Analysis} +\title{VoltRon: VoltRon for Spatial Omics Data Integration and Analysis} \description{ VoltRon is a novel spatial omic analysis toolbox for multi-omics integration using spatial image registration. VoltRon is capable of analyzing multiple types and modalities of spatially-aware datasets. VoltRon visualizes and analyzes regions of interests (ROIs), spots, cells, molecules and event tiles. } diff --git a/tests/testthat/test_conversion.R b/tests/testthat/test_conversion.R index 80febb7..ac8b149 100644 --- a/tests/testthat/test_conversion.R +++ b/tests/testthat/test_conversion.R @@ -1,9 +1,10 @@ # test_that("as.AnnData", { # # # library -# skip_if_not_installed("DelayedArray") -# skip_if_not_installed("anndata") +# skip_if_not_installed("basilisk") # skip_if_not_installed("reticulate") +# skip_if_not_installed("anndata") +# skip_if_not_installed("DelayedArray") # # # get data # data("visium_data") From 14582de38f04cb0aa6260cdb2cd9edf17eafc7de Mon Sep 17 00:00:00 2001 From: Artur-man Date: Sun, 12 Jan 2025 22:40:38 +0100 Subject: [PATCH 21/26] update geojson import and export functions from segments --- NAMESPACE | 4 +- R/import.R | 40 +++++++++---------- docs/multiomic.Rmd | 13 +++++- docs/multiomic.html | 22 +++++++--- man/generateGeoJSON.Rd | 16 ++++++++ man/generateGeoJSONFromSegments.Rd | 16 -------- ...entsFromGeoJSON.Rd => generateSegments.Rd} | 10 ++--- 7 files changed, 70 insertions(+), 51 deletions(-) create mode 100644 man/generateGeoJSON.Rd delete mode 100644 man/generateGeoJSONFromSegments.Rd rename man/{generateSegmentsFromGeoJSON.Rd => generateSegments.Rd} (50%) diff --git a/NAMESPACE b/NAMESPACE index 4b6f136..16a3979 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -192,8 +192,8 @@ export(formAssay) export(formImage) export(formVoltRon) export(generateCosMxImage) -export(generateGeoJSONFromSegments) -export(generateSegmentsFromGeoJSON) +export(generateGeoJSON) +export(generateSegments) export(generateTileData) export(generateXeniumImage) export(getClusters) diff --git a/R/import.R b/R/import.R index ea873c6..1711ca8 100644 --- a/R/import.R +++ b/R/import.R @@ -2025,7 +2025,7 @@ importImageData <- function(image, tile.size = 10, segments = NULL, image_name = # check if segments are paths if(inherits(segments, "character")){ if(grepl(".geojson$", segments)){ - segments <- generateSegmentsFromGeoJSON(geojson.file = segments) + segments <- generateSegments(geojson.file = segments) } else { stop("Only lists or GeoJSON files are accepted as segments input!") } @@ -2055,9 +2055,9 @@ importImageData <- function(image, tile.size = 10, segments = NULL, image_name = } -#' generateSegmentsFromGeoJSON +#' generateSegments #' -#' The function to import segments from a json data +#' The function to import segments from a geojson file #' #' @param geojson.file the GeoJSON file, typically generated by QuPath software #' @@ -2065,16 +2065,12 @@ importImageData <- function(image, tile.size = 10, segments = NULL, image_name = #' @importFrom dplyr tibble #' #' @export -generateSegmentsFromGeoJSON <- function(geojson.file){ - - # if(!requireNamespace('geojsonR')) - # stop("Please install geojsonR package for using geojsonR functions") +generateSegments <- function(geojson.file){ # get segments if(inherits(geojson.file, "character")){ if(file.exists(geojson.file)){ segments <- rjson::fromJSON(file = geojson.file) - # segments <- geojsonR::FROM_GeoJson(geojson.file) } else { stop("geojson.file doesn't exist!") } @@ -2105,40 +2101,42 @@ generateSegmentsFromGeoJSON <- function(geojson.file){ return(segments) } -#' generateSegmentsFromGeoJSON +#' generateGeoJSON #' -#' The function to import segments from a json data +#' generating geojson files from segments #' #' @param segments the segments, typically from \link{vrSegments}. -#' @param geojson.file the GeoJSON file, typically to be used by QuPath software. +#' @param file the GeoJSON file, typically to be used by QuPath software. #' #' @importFrom rjson fromJSON #' #' @export -generateGeoJSONFromSegments <- function(segments, geojson.file){ +generateGeoJSON <- function(segments, file){ if(!requireNamespace('geojsonR')) stop("Please install geojsonR package for using geojsonR functions") # get segments - if(!inherits(geojson.file, "character")){ - stop("geojson.file should be the path to the GeoJSON file!") + if(!inherits(file, "character")){ + stop("file should be the path to the GeoJSON file!") } # reshape segments segments <- mapply(function(id, sgt){ - poly <- as.list(data.frame(t(as.matrix(sgt[,c("x", "y")])))) + poly <- na.omit(as.matrix(sgt[,c("x", "y")])) + poly <- rbind(poly, poly[1,,drop = FALSE]) + poly <- as.list(data.frame(t(poly))) names(poly) <- NULL init <- geojsonR::TO_GeoJson$new() - geometry <- init$Polygon(list(poly), stringify = FALSE) - feature <- list(id = id, geometry = geometry, properties = list(objectType = "annotation")) + geometry <- init$Polygon(list(poly), stringify = TRUE) + feature <- list(type = "Feature", + id = id, + geometry = geometry[!names(geometry) %in% "json_dump"], + properties = list(objectType = "annotation")) feature }, names(segments), segments, SIMPLIFY = FALSE, USE.NAMES = FALSE) # save as json segments <- rjson::toJSON(segments) - write(segments, file = geojson.file) - - # return - return(segments) + write(segments, file = file) } \ No newline at end of file diff --git a/docs/multiomic.Rmd b/docs/multiomic.Rmd index bdc62b9..0055d9d 100644 --- a/docs/multiomic.Rmd +++ b/docs/multiomic.Rmd @@ -189,10 +189,21 @@ vrSpatialPlot(imgdata, assay = "ROIAnnotation", group.by = "Sample",
    -We now register the H&E image and annotations to the DAPI image of Xenium section using the **registerSpatialData** function. See [Spatial Data Alignment](registration.html) tutorial for more information. We select FLANN automated registration mode, negate the DAPI image of the Xenium slide, turn 90 degrees to the left and set the scale parameter of both images to width = 1859. +At a first glance, although the Xenium (DAPI) and H&E images are associated with the same TMA core, they were captured in a different perspective; that is, one image is almost the 90 degree rotated version of the other. We will account for this rotation when we (automatically) align the Xenium data with the H&E image. ```{r eval = FALSE, class.source="watch-out"} vr2_merged_acute1 <- modulateImage(vr2_merged_acute1, brightness = 300, channel = "DAPI") +vrImages(vr2_merged_acute1, assay = "Assay7", scale.perc = 20) +vrImages(imgdata, assay = "Assay1", scale.perc = 20) +``` + + + +
    + +We now register the H&E image and annotations to the DAPI image of Xenium section using the **registerSpatialData** function. We select FLANN (with "Homography" method) automated registration mode, negate the DAPI image of the Xenium slide, turn 90 degrees to the left and set the scale parameter of both images to **width = 1859**. See [Spatial Data Alignment](registration.html) tutorial for more information. + +```{r eval = FALSE, class.source="watch-out"} xen_reg <- registerSpatialData(object_list = list(vr2_merged_acute1, imgdata)) ``` diff --git a/docs/multiomic.html b/docs/multiomic.html index 09e54b1..7ed8640 100644 --- a/docs/multiomic.html +++ b/docs/multiomic.html @@ -611,14 +611,24 @@

    Automated H&E Registration

    alpha = 0.7, interactive = TRUE)


    +

    At a first glance, although the Xenium (DAPI) and H&E images are +associated with the same TMA core, they were captured in a different +perspective; that is, one image is almost the 90 degree rotated version +of the other. We will account for this rotation when we (automatically) +align the Xenium data with the H&E image.

    +
    vr2_merged_acute1 <- modulateImage(vr2_merged_acute1, brightness = 300, channel = "DAPI")
+vrImages(vr2_merged_acute1, assay = "Assay7", scale.perc = 20)
+vrImages(imgdata, assay = "Assay1", scale.perc = 20)
    +

    +


    We now register the H&E image and annotations to the DAPI image of Xenium section using the registerSpatialData -function. See Spatial Data Alignment -tutorial for more information. We select FLANN automated registration -mode, negate the DAPI image of the Xenium slide, turn 90 degrees to the -left and set the scale parameter of both images to width = 1859.

    -
    vr2_merged_acute1 <- modulateImage(vr2_merged_acute1, brightness = 300, channel = "DAPI")
-xen_reg <- registerSpatialData(object_list = list(vr2_merged_acute1, imgdata))
    +function. We select FLANN (with “Homography” method) automated +registration mode, negate the DAPI image of the Xenium slide, turn 90 +degrees to the left and set the scale parameter of both images to +width = 1859. See Spatial +Data Alignment tutorial for more information.

    +
    xen_reg <- registerSpatialData(object_list = list(vr2_merged_acute1, imgdata))


    Once registered, we can isolate the registered H&E data and use diff --git a/man/generateGeoJSON.Rd b/man/generateGeoJSON.Rd new file mode 100644 index 0000000..d3c6abc --- /dev/null +++ b/man/generateGeoJSON.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/import.R +\name{generateGeoJSON} +\alias{generateGeoJSON} +\title{generateGeoJSON} +\usage{ +generateGeoJSON(segments, file) +} +\arguments{ +\item{segments}{the segments, typically from \link{vrSegments}.} + +\item{file}{the GeoJSON file, typically to be used by QuPath software.} +} +\description{ +generating geojson files from segments +} diff --git a/man/generateGeoJSONFromSegments.Rd b/man/generateGeoJSONFromSegments.Rd deleted file mode 100644 index 97be0bf..0000000 --- a/man/generateGeoJSONFromSegments.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/import.R -\name{generateGeoJSONFromSegments} -\alias{generateGeoJSONFromSegments} -\title{generateSegmentsFromGeoJSON} -\usage{ -generateGeoJSONFromSegments(segments, geojson.file) -} -\arguments{ -\item{segments}{the segments, typically from \link{vrSegments}.} - -\item{geojson.file}{the GeoJSON file, typically to be used by QuPath software.} -} -\description{ -The function to import segments from a json data -} diff --git a/man/generateSegmentsFromGeoJSON.Rd b/man/generateSegments.Rd similarity index 50% rename from man/generateSegmentsFromGeoJSON.Rd rename to man/generateSegments.Rd index 91a89ea..1829bba 100644 --- a/man/generateSegmentsFromGeoJSON.Rd +++ b/man/generateSegments.Rd @@ -1,14 +1,14 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/import.R -\name{generateSegmentsFromGeoJSON} -\alias{generateSegmentsFromGeoJSON} -\title{generateSegmentsFromGeoJSON} +\name{generateSegments} +\alias{generateSegments} +\title{generateSegments} \usage{ -generateSegmentsFromGeoJSON(geojson.file) +generateSegments(geojson.file) } \arguments{ \item{geojson.file}{the GeoJSON file, typically generated by QuPath software} } \description{ -The function to import segments from a json data +The function to import segments from a geojson file } From 41b4de316039f5307618ffdfd03c8a25ee8187d3 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 13 Jan 2025 10:50:08 +0100 Subject: [PATCH 22/26] replace cat calls with message --- R/annotation.R | 2 - R/auxiliary.R | 2 +- R/deconvolution.R | 14 +++--- R/image.R | 2 - R/import.R | 12 ++--- R/integration.R | 35 ++++---------- R/interactive.R | 2 - R/io.R | 32 ++++++------- R/objects.R | 1 - R/registration.R | 2 - R/sample.R | 1 - tests/testthat/test_conversion.R | 82 ++++++++++++++++---------------- 12 files changed, 81 insertions(+), 106 deletions(-) diff --git a/R/annotation.R b/R/annotation.R index e3ae180..d0eed8f 100644 --- a/R/annotation.R +++ b/R/annotation.R @@ -439,9 +439,7 @@ annotateSpatialData <- function(object, label = "annotation", assay = NULL, anno shiny::runApp( shiny::shinyApp(ui, server, options = list(host = shiny.options[["host"]], port = shiny.options[["port"]], launch.browser = shiny.options[["launch.browser"]]), onStart = function() { - cat("Doing application setup\n") onStop(function() { - cat("Doing application cleanup\n") }) }) ) diff --git a/R/auxiliary.R b/R/auxiliary.R index 60e86e9..8919968 100644 --- a/R/auxiliary.R +++ b/R/auxiliary.R @@ -330,7 +330,7 @@ make_css <- function (..., file = NULL) css_string } else { - cat(css_string, file = file) + message(css_string, file = file) invisible(NULL) } } diff --git a/R/deconvolution.R b/R/deconvolution.R index 9665075..1086d8b 100644 --- a/R/deconvolution.R +++ b/R/deconvolution.R @@ -83,7 +83,7 @@ getDeconReference <- function(sc.object, sc.assay = "RNA", sc.cluster = "seurat_ if (!requireNamespace('Seurat')) stop("Please install Seurat package for using Seurat objects") - cat("Configuring Single Cell Assay (reference) ...\n") + message("Configuring Single Cell Assay (reference) ...\n") sccounts <- Seurat::GetAssayData(sc.object[[sc.assay]], slot = "counts") # sccounts <- as.matrix(apply(sccounts,2,ceiling)) rownames(sccounts) <- rownames(sc.object[[sc.assay]]) @@ -106,7 +106,7 @@ getDeconReference <- function(sc.object, sc.assay = "RNA", sc.cluster = "seurat_ # deconvolution with MuSiC if(method == "MuSiC"){ - cat("Configuring Single Cell Assay (reference) ...\n") + message("Configuring Single Cell Assay (reference) ...\n") if(inherits(sc.object, "SingleCellExperiment")){ sc.object$music_decon_clusters <- sc.object[[sc.cluster]] reference <- sc.object @@ -152,7 +152,7 @@ getDeconSingle <- function(object, features = features, reference, method = "RCT } if(method == "RCTD"){ - cat("Running RCTD for spot deconvolution ...\n") + message("Running RCTD for spot deconvolution ...\n") rawdata <- getRCTD(object = object, features = features, reference = reference, ...) } @@ -165,7 +165,7 @@ getDeconSingle <- function(object, features = features, reference, method = "RCT } if(method == "MuSiC"){ - cat("Running MuSiC for ROI deconvolution ...\n") + message("Running MuSiC for ROI deconvolution ...\n") rawdata <- getMuSiC(object = object, features = features, reference = reference, ...) } @@ -193,7 +193,7 @@ getRCTD <- function(object, features = NULL, reference, ...){ stop("Please install Seurat package for using Seurat objects: install.packages('Seurat')") # create spatial data - cat("Configuring Spatial Assay ...\n") + message("Configuring Spatial Assay ...\n") spatialcounts <- vrData(object, norm = FALSE) coords <- as.data.frame(as(vrCoordinates(object), "dgCMatrix"))[,c("x", "y")] spatialnUMI <- colSums(spatialcounts) @@ -201,7 +201,7 @@ getRCTD <- function(object, features = NULL, reference, ...){ # Run RCTD myRCTD <- spacexr::create.RCTD(spatialdata, reference, ...) - cat("Calculating Cell Type Compositions of spots with RCTD ...\n") + message("Calculating Cell Type Compositions of spots with RCTD ...\n") myRCTD <- quiet(spacexr::run.RCTD(myRCTD, doublet_mode = 'full')) results <- as.matrix(myRCTD@results$weights) norm_weights <- t(sweep(results, 1, rowSums(results), "/")) @@ -251,7 +251,7 @@ getMuSiC <- function(object, features = NULL, reference, sc.samples = NULL){ datax <- datax[common_features,] # deconvolute - cat("Calculating Cell Type Compositions of ROIs with MuSiC ...\n") + message("Calculating Cell Type Compositions of ROIs with MuSiC ...\n") results <- MuSiC::music_prop(bulk.mtx = datax, sc.sce = reference, clusters = "music_decon_clusters", diff --git a/R/image.R b/R/image.R index 108c75a..a4c6cd7 100644 --- a/R/image.R +++ b/R/image.R @@ -1806,9 +1806,7 @@ demuxVoltRon <- function(object, max.pixel.size = 1200, use.points.only = FALSE, shiny::runApp( shiny::shinyApp(ui, server, options = list(host = shiny.options[["host"]], port = shiny.options[["port"]], launch.browser = shiny.options[["launch.browser"]]), onStart = function() { - cat("Doing application setup\n") onStop(function() { - cat("Doing application cleanup\n") }) }) ) diff --git a/R/import.R b/R/import.R index 1711ca8..2730afc 100644 --- a/R/import.R +++ b/R/import.R @@ -1173,7 +1173,7 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = # check if the file exists in either Xenium output folder, or the specified location if(file.exists(file.path) | file.exists(paste0(output.file))){ - cat("CellComposite_lowres.tif already exists! \n") + message("CellComposite_lowres.tif already exists! \n") return(NULL) } @@ -1183,7 +1183,7 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = fov_positions <- read.csv(fov_positions_path) # manipulate fov positions matrix - cat("Getting FOV Positions \n") + message("Getting FOV Positions \n") relative_fov_positions <- fov_positions x_min <- min(relative_fov_positions$x_global_px) y_min <- min(relative_fov_positions$y_global_px) @@ -1195,7 +1195,7 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = relative_fov_positions <- relative_fov_positions[order(relative_fov_positions$y_global_px, decreasing = TRUE),] # Combine Images of the FOV grid - cat("Loading FOV tif files \n") + message("Loading FOV tif files \n") image.dir.path <- paste0(dir.path,"/CellComposite/") morphology_image_data <- NULL for(i in relative_fov_positions$fov){ @@ -1213,16 +1213,16 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = # pick a resolution level morphology_image_info <- image_info(morphology_image) - cat(paste0("Image Resolution (X:", morphology_image_info$width, " Y:", morphology_image_info$height, ") \n")) + message("Image Resolution (X:", morphology_image_info$width, " Y:", morphology_image_info$height, ") \n") # increase contrast using EBImage if(increase.contrast) { - cat("Increasing Contrast \n") + message("Increasing Contrast \n") morphology_image <- magick::image_contrast(morphology_image, sharpen = 1) } # write to the same folder - cat("Writing Tiff File \n") + message("Writing Tiff File \n") if(is.null(output.path)){ EBImage::writeImage(magick::as_EBImage(morphology_image), file = file.path, ...) } else { diff --git a/R/integration.R b/R/integration.R index c76fe1a..5c8ce50 100644 --- a/R/integration.R +++ b/R/integration.R @@ -156,7 +156,7 @@ getSpotsFromCells <- function(from_object, from_metadata = NULL, to_object, feat Vis_spotradius <- vrAssayParams(to_object, param = "spot.radius") # get cell and spot coordinates - cat("Cell to Spot Distances \n") + message("Cell to Spot Distances \n") coords_spots <- vrCoordinates(to_object) coords_cells <- vrCoordinates(from_object) @@ -172,7 +172,7 @@ getSpotsFromCells <- function(from_object, from_metadata = NULL, to_object, feat cell_to_spot_nnid <- cell_to_spot_nnid[cell_to_spot_nndist < Vis_spotradius] # find associated spot for each cell - cat("Find associated spots for each cell \n") + message("Find associated spots for each cell \n") cell_to_spot_id <- names(cell_to_spot_nnid) # get data @@ -208,7 +208,7 @@ getSpotsFromCells <- function(from_object, from_metadata = NULL, to_object, feat raw_counts <- raw_counts[,cell_to_spot_id, drop = FALSE] # pool cell counts to Spots - cat("Aggregating cell profiles in spots \n") + message("Aggregating cell profiles in spots \n") aggregate_raw_counts <- stats::aggregate(t(as.matrix(raw_counts)), list(cell_to_spot_nnid), sum) aggregate_raw_counts <- data.frame(barcodes = vrSpatialPoints(to_object)) %>% dplyr::right_join(aggregate_raw_counts, by = c("barcodes" = "Group.1")) rownames(aggregate_raw_counts) <- aggregate_raw_counts$barcodes @@ -240,7 +240,7 @@ getCellsFromSpots <- function(from_object, from_metadata = NULL, to_object, feat radius <- vrAssayParams(from_object, param = "nearestpost.distance")/2 # get cell and spot coordinates - cat("Spot to Cell Distances \n") + message("Spot to Cell Distances \n") coords_spots <- vrCoordinates(from_object) coords_cells <- vrCoordinates(to_object) @@ -255,7 +255,7 @@ getCellsFromSpots <- function(from_object, from_metadata = NULL, to_object, feat nnindex <- nnindex[nndist < radius] # find associated spot for each cell - cat("Find associated spot for each cell \n") + message("Find associated spot for each cell \n") # get data if(is.null(features)){ @@ -312,12 +312,12 @@ getCellsFromSpots <- function(from_object, from_metadata = NULL, to_object, feat getROIsFromCells <- function(from_object, from_metadata = NULL, to_object, features = NULL) { # get cell and ROIs coordinates - cat("Cell to ROI Distances \n") + message("Cell to ROI Distances \n") segments_rois <- vrSegments(to_object) coords_cells <- vrCoordinates(from_object) # find associated spot for each cell - cat("Find associated ROIs for each cell \n") + message("Find associated ROIs for each cell \n") cell_to_roi_id <- NULL cell_to_roi_labelid <- NULL names_segments_rois <- names(segments_rois) @@ -366,36 +366,21 @@ getROIsFromCells <- function(from_object, from_metadata = NULL, to_object, featu raw_counts <- raw_counts[,cell_to_roi_id, drop = FALSE] # pool cell counts to Spots - cat("Aggregating cell profiles in spots \n") + message("Aggregating cell profiles in spots \n") aggregate_raw_counts <- stats::aggregate(t(as.matrix(raw_counts)), list(cell_to_roi_labelid), sum) aggregate_raw_counts <- data.frame(barcodes = vrSpatialPoints(to_object)) %>% dplyr::right_join(aggregate_raw_counts, by = c("barcodes" = "Group.1")) rownames(aggregate_raw_counts) <- aggregate_raw_counts$barcodes aggregate_raw_counts <- t(aggregate_raw_counts[,-1]) aggregate_raw_counts[is.na(aggregate_raw_counts)] <- 0 - # create new assay - # images <- list() - # for(img in vrImageNames(to_object)){ - # images[[img]] <- magick::image_data(vrImages(to_object, name = img)) - # } - # new_assay <- formAssay(data = aggregate_raw_counts, - # coords = vrCoordinates(to_object)[colnames(aggregate_raw_counts),], - # image = vrImages(to_object), - # type = vrAssayTypes(to_object), - # main_image = to_object@main_image, - # params = to_object@params) - # new_assay@image <- to_object@image - # new_assay <- subset(new_assay, spatialpoints = colnames(aggregate_raw_counts)) - # return - # return(new_assay) return(aggregate_raw_counts) } getCellsFromTiles <- function(from_object, from_metadata = NULL, to_object, features = NULL, k = 5) { # get cell and spot coordinates - cat("Tile to Cell Distances \n") + message("Tile to Cell Distances \n") coords_cells <- vrCoordinates(to_object) coords_tiles <- vrCoordinates(from_object) @@ -414,7 +399,7 @@ getCellsFromTiles <- function(from_object, from_metadata = NULL, to_object, feat raw_counts <- raw_counts[,tile_id] # pool cell counts to Spots - cat("Aggregating tile profiles in cells \n") + message("Aggregating tile profiles in cells \n") aggregate_raw_counts <- stats::aggregate(t(as.matrix(raw_counts)), list(tile_to_cell_nnid), mean) aggregate_raw_counts <- data.frame(barcodes = vrSpatialPoints(to_object)) %>% dplyr::right_join(aggregate_raw_counts, by = c("barcodes" = "Group.1")) rownames(aggregate_raw_counts) <- aggregate_raw_counts$barcodes diff --git a/R/interactive.R b/R/interactive.R index e2740b6..3211f5e 100644 --- a/R/interactive.R +++ b/R/interactive.R @@ -39,9 +39,7 @@ vrSpatialPlotInteractive <- function(plot_g = NULL, shiny::runApp( shiny::shinyApp(ui, server, options = list(host = shiny.options[["host"]], port = shiny.options[["port"]], launch.browser = shiny.options[["launch.browser"]]), onStart = function() { - cat("Doing application setup\n") onStop(function() { - cat("Doing application cleanup\n") }) }) ) diff --git a/R/io.R b/R/io.R index b25d5e2..c77a1ef 100644 --- a/R/io.R +++ b/R/io.R @@ -266,7 +266,7 @@ write_h5_samples <- function(object, assay = NULL, h5_path, chunkdim, level, # open h5 file if(verbose) - cat(paste0("HDF5 file: ", h5_path, "\n")) + message("HDF5 file: ", h5_path, "\n") if(!file.exists("h5_path")) rhdf5::h5createFile(h5_path) @@ -349,7 +349,7 @@ writeHDF5ArrayInMetadata <- function(object, meta.data_list <- list() rhdf5::h5createGroup(h5_path, group = paste0(name, "/", sn)) if(verbose) - cat(paste0("Writing ", sn, " Metadata \n")) + message("Writing ", sn, " Metadata \n") # write rownames first if they exist, and there is no id column if(!is.null(rownames(meta.data)) && !("id" %in% colnames(meta.data))){ @@ -424,7 +424,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' ", feat, " data \n")) + message("Writing '", vrAssayNames(object), "' ", feat, " data \n") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/", feat), @@ -440,7 +440,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' normalized ", feat, " data \n")) + message("Writing '", vrAssayNames(object), "' normalized ", feat, " data \n") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/", feat, "_norm"), @@ -459,7 +459,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' data \n")) + message("Writing '", vrAssayNames(object), "' data \n") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/rawdata"), @@ -473,7 +473,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' normalized data \n")) + message("Writing '", vrAssayNames(object), "' normalized data \n") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/normdata"), @@ -516,7 +516,7 @@ writeHDF5ArrayInImage <- function(object, if(!inherits(coords, "dgCMatrix")) coords <- as(coords, "dgCMatrix") if(verbose) - cat(paste0("Writing '", name, "' coordinates \n")) + message("Writing '", name, "' coordinates \n") coords <- BPCells::write_matrix_hdf5(coords, path = h5_path, group = paste0(name, "/", spat, "/coords"), @@ -536,7 +536,7 @@ writeHDF5ArrayInImage <- function(object, # write image if(!inherits(img, "Image_Array") || replace){ if(verbose) - cat(paste0("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"' \n")) + message("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"' \n") img <- ImageArray::writeImageArray(img, output = gsub(".h5$", "", h5_path), name = paste0(name, "/", spat, "/", ch), @@ -575,7 +575,7 @@ write_zarr_samples <- function(object, assay = NULL, zarr_path, chunkdim, level, # create zarr if(verbose) - cat(paste0("Zarr store: ", zarr_path, "\n")) + message("Zarr store: ", zarr_path, "\n") zarr.array <- pizzarr::zarr_open(store = zarr_path) # create metadata @@ -657,7 +657,7 @@ writeZarrArrayInMetadata <- function(object, meta.data_list <- list() zarr.array <- pizzarr::zarr_open(store = zarr_path) if(verbose) - cat(paste0("Writing ", sn, " Metadata \n")) + message("Writing ", sn, " Metadata \n") zarr.array$create_group(paste0(name, "/", sn)) # write rownames first if they exist, and there is no id column @@ -731,7 +731,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, feat_type = feat, norm = FALSE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' data \n")) + message("Writing '", vrAssayNames(object), "' data \n") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/", feat), @@ -747,7 +747,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, feat_type = feat, norm = TRUE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' normalized data \n")) + message("Writing '", vrAssayNames(object), "' normalized data \n") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/", feat, "_norm"), @@ -766,7 +766,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, norm = FALSE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' data \n")) + message("Writing '", vrAssayNames(object), "' data \n") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/rawdata"), @@ -782,7 +782,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, norm = TRUE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - cat(paste0("Writing '", vrAssayNames(object), "' normalized data \n")) + message("Writing '", vrAssayNames(object), "' normalized data \n") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/normdata"), @@ -828,7 +828,7 @@ writeZarrArrayInImage <- function(object, coords <- vrCoordinates(object, spatial_name = spat) if(!inherits(coords, c("DelayedArray", "IterableMatrix")) || replace){ if(verbose) - cat(paste0("Writing '", name, "' coordinates \n")) + message("Writing '", name, "' coordinates \n") coords <- ZarrArray::writeZarrArray(coords, zarr_path, name = paste0(name, "/", spat, "/coords"), @@ -851,7 +851,7 @@ writeZarrArrayInImage <- function(object, # write image if(!inherits(img, "Image_Array") || replace){ if(verbose) - cat(paste0("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"' \n")) + message("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"' \n") img <- ImageArray::writeImageArray(img, output = gsub(".zarr$", "", zarr_path), name = paste0(name, "/", spat, "/", ch), diff --git a/R/objects.R b/R/objects.R index 368453a..266c97f 100644 --- a/R/objects.R +++ b/R/objects.R @@ -53,7 +53,6 @@ setMethod( for(samp in sample_names[1:show_length]){ cat(samp, ": \n", sep = "") layers <- unique(sample.metadata$Layer[sample.metadata$Sample == samp]) - # cat(" Layers:", paste(layers, collapse = " "), "\n") layers <- split(layers, ceiling(seq_along(layers)/5)) cat(" Layers:", paste(layers[[1]], collapse = " "), "\n") if(length(layers) > 1){ diff --git a/R/registration.R b/R/registration.R index cea023a..59c5bc6 100644 --- a/R/registration.R +++ b/R/registration.R @@ -230,9 +230,7 @@ registerSpatialData <- function(object_list = NULL, reference_spatdata = NULL, q shiny::runApp( shiny::shinyApp(ui, server, options = list(host = shiny.options[["host"]], port = shiny.options[["port"]], launch.browser = shiny.options[["launch.browser"]]), onStart = function() { - cat("Doing application setup\n") onStop(function() { - cat("Doing application cleanup\n") }) }) ) diff --git a/R/sample.R b/R/sample.R index b435b5e..a16e595 100644 --- a/R/sample.R +++ b/R/sample.R @@ -201,7 +201,6 @@ setMethod( signature = 'vrLayer', definition = function(object) { cat(class(x = object), "(VoltRon Layer) Object \n") - # cat("This object includes", length(object@assay), "assays \n") layers <- names(unlist(object@assay)) cat("Assay(s):", paste(layers, collapse = " "), "\n") return(invisible(x = NULL)) diff --git a/tests/testthat/test_conversion.R b/tests/testthat/test_conversion.R index ac8b149..a45344a 100644 --- a/tests/testthat/test_conversion.R +++ b/tests/testthat/test_conversion.R @@ -1,41 +1,41 @@ -# test_that("as.AnnData", { -# -# # library -# skip_if_not_installed("basilisk") -# skip_if_not_installed("reticulate") -# skip_if_not_installed("anndata") -# skip_if_not_installed("DelayedArray") -# -# # get data -# data("visium_data") -# data("xenium_data") -# -# # file -# h5ad_file <- tempfile(fileext = ".h5ad") -# zarr_file <- tempfile(fileext = ".zarr") -# -# # xenium to anndata -# as.AnnData(xenium_data, file = h5ad_file) -# as.AnnData(xenium_data, file = h5ad_file, assay = "Assay1") -# as.AnnData(xenium_data, file = h5ad_file, flip_coordinates = TRUE) -# -# # visium to anndata -# as.AnnData(visium_data, file = h5ad_file) -# as.AnnData(visium_data, file = h5ad_file, assay = "Assay1") -# as.AnnData(visium_data, file = h5ad_file, flip_coordinates = TRUE) -# -# # xenium to anndata -# as.AnnData(xenium_data, file = zarr_file) -# as.AnnData(xenium_data, file = zarr_file, assay = "Assay1") -# as.AnnData(xenium_data, file = zarr_file, flip_coordinates = TRUE) -# -# # visium to anndata -# as.AnnData(visium_data, file = zarr_file) -# as.AnnData(visium_data, file = zarr_file, assay = "Assay1") -# as.AnnData(visium_data, file = zarr_file, flip_coordinates = TRUE) -# -# # clean file -# file.remove(h5ad_file) -# unlink(zarr_file, recursive = TRUE) -# expect_equal(1,1L) -# }) \ No newline at end of file +test_that("as.AnnData", { + + # library + skip_if_not_installed("basilisk") + skip_if_not_installed("reticulate") + skip_if_not_installed("anndata") + skip_if_not_installed("DelayedArray") + + # get data + data("visium_data") + data("xenium_data") + + # file + h5ad_file <- tempfile(fileext = ".h5ad") + zarr_file <- tempfile(fileext = ".zarr") + + # xenium to anndata + as.AnnData(xenium_data, file = h5ad_file) + as.AnnData(xenium_data, file = h5ad_file, assay = "Assay1") + as.AnnData(xenium_data, file = h5ad_file, flip_coordinates = TRUE) + + # visium to anndata + as.AnnData(visium_data, file = h5ad_file) + as.AnnData(visium_data, file = h5ad_file, assay = "Assay1") + as.AnnData(visium_data, file = h5ad_file, flip_coordinates = TRUE) + + # xenium to anndata + as.AnnData(xenium_data, file = zarr_file) + as.AnnData(xenium_data, file = zarr_file, assay = "Assay1") + as.AnnData(xenium_data, file = zarr_file, flip_coordinates = TRUE) + + # visium to anndata + as.AnnData(visium_data, file = zarr_file) + as.AnnData(visium_data, file = zarr_file, assay = "Assay1") + as.AnnData(visium_data, file = zarr_file, flip_coordinates = TRUE) + + # clean file + file.remove(h5ad_file) + unlink(zarr_file, recursive = TRUE) + expect_equal(1,1L) +}) \ No newline at end of file From 57a9b890f2781fba2f417e66d45dd57f90427798 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 13 Jan 2025 12:00:01 +0100 Subject: [PATCH 23/26] add verbose arguements to some functions --- R/conversion.R | 15 ++-- R/import.R | 111 ++++++++++++++++++--------- R/objects.R | 9 ++- R/spatial.R | 19 +++-- man/as.VoltRon.Rd | 11 ++- man/generateCosMxImage.Rd | 10 ++- man/generateXeniumImage.Rd | 3 + man/getHotSpotAnalysis.Rd | 5 +- man/getSpatialNeighbors.Rd | 5 +- man/importCosMx.Rd | 3 + man/importGenePS.Rd | 3 + man/importOpenST.Rd | 3 + man/importPhenoCycler.Rd | 3 + man/importXenium.Rd | 3 + man/merge.Rd | 4 +- man/vrNeighbourhoodEnrichment.Rd | 5 +- tests/testthat/test-coordsystem.R | 2 +- tests/testthat/test-graph.R | 10 +-- tests/testthat/test-ondisk.R | 4 +- tests/testthat/test-spatial.R | 32 ++++---- tests/testthat/test-voltronobjects.R | 6 +- 21 files changed, 181 insertions(+), 85 deletions(-) diff --git a/R/conversion.R b/R/conversion.R index 66c2ede..a666e16 100644 --- a/R/conversion.R +++ b/R/conversion.R @@ -4,6 +4,7 @@ #' @param type the spatial data type of Seurat object: "image" or "spatial" #' @param assay_name the assay name +#' @param verbose verbose #' @param ... Additional parameter passed to \link{formVoltRon} #' #' @rdname as.VoltRon @@ -12,7 +13,7 @@ #' @importFrom stringr str_replace str_extract #' @export #' -as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name = NULL, ...){ +as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name = NULL, verbose = TRUE, ...){ # check Seurat package if(!requireNamespace('Seurat')) @@ -40,7 +41,8 @@ as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name = for(fn in fov_names){ # message - message("Converting FOV: ", fn, " ...") + if(verbose) + message("Converting FOV: ", fn, " ...") # image object spatialobject <- spatialobjectlist[[fn]] @@ -83,7 +85,8 @@ as.VoltRon.Seurat <- function(object, type = c("image", "spatial"), assay_name = } # merge object - message("Merging object ...") + if(verbose) + message("Merging object ...") vrobject <- merge(voltron_list[[1]], voltron_list[-1]) } else{ image <- NULL @@ -745,6 +748,7 @@ as.Giotto <- function(object, assay = NULL, reg = FALSE){ #' @param assay_type one of two types, 'cell' or 'spot' etc. #' @param assay_name the assay name of the voltron assays (e.g. Visium, Xenium etc.) #' @param image_id select image_id names if needed. +#' @param verbose verbose #' @param ... Additional parameter passed to \link{formVoltRon} #' #' @rdname as.VoltRon @@ -753,7 +757,7 @@ as.Giotto <- function(object, assay = NULL, reg = FALSE){ #' @importFrom magick image_read #' #' @export -as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name = NULL, image_id = NULL, ...){ +as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name = NULL, image_id = NULL, verbose = TRUE, ...){ # check SpatialExperiment package if(!requireNamespace('SpatialExperiment')) @@ -846,7 +850,8 @@ as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name } # merge object - message("Merging object ...") + if(verbose) + message("Merging object ...") if(length(voltron_list) > 1){ vrobject <- merge(voltron_list[[1]], voltron_list[-1]) } else { diff --git a/R/import.R b/R/import.R index 2730afc..0f06177 100644 --- a/R/import.R +++ b/R/import.R @@ -21,6 +21,7 @@ #' @param image_name the image name of the Xenium assay, Default: main #' @param channel_name the channel name of the image of the Xenium assay, Default: DAPI #' @param import_molecules if TRUE, molecule assay will be created along with cell assay. +#' @param verbose verbose #' @param ... additional parameters passed to \link{formVoltRon} #' #' @importFrom magick image_read image_info @@ -30,10 +31,13 @@ #' #' @export #' -importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_name = "Xenium", sample_name = NULL, use_image = TRUE, morphology_image = "morphology_lowres.tif", resolution_level = 7, overwrite_resolution = FALSE, image_name = "main", channel_name = "DAPI", import_molecules = FALSE, ...) +importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_name = "Xenium", sample_name = NULL, use_image = TRUE, + morphology_image = "morphology_lowres.tif", resolution_level = 7, overwrite_resolution = FALSE, + image_name = "main", channel_name = "DAPI", import_molecules = FALSE, verbose = TRUE, ...) { # cell assay - message("Creating cell level assay ...") + if(verbose) + message("Creating cell level assay ...") # raw counts datafile <- paste0(dir.path, "/cell_feature_matrix.h5") @@ -50,8 +54,8 @@ importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_na # image if(use_image){ - # suppressMessages(generateXeniumImage(dir.path, file.name = morphology_image, resolution_level = resolution_level, overwrite_resolution = overwrite_resolution)) - generateXeniumImage(dir.path, file.name = morphology_image, resolution_level = resolution_level, overwrite_resolution = overwrite_resolution) + generateXeniumImage(dir.path, file.name = morphology_image, resolution_level = resolution_level, overwrite_resolution = overwrite_resolution, + verbose = verbose) image_file <- paste0(dir.path, "/", morphology_image) if(file.exists(image_file)){ image <- image_read(image_file) @@ -108,7 +112,8 @@ importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_na if(!import_molecules){ return(cell_object) } else { - message("Creating molecule level assay ...") + if(verbose) + message("Creating molecule level assay ...") # transcripts transcripts_file <- paste0(dir.path, "/transcripts.csv.gz") if(!file.exists(transcripts_file)){ @@ -144,7 +149,8 @@ importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_na mol_assay <- formAssay(coords = coords, image = image, type = "molecule", main_image = image_name, main_channel = channel_name) # merge assays in one section - message("Merging assays ...") + if(verbose) + message("Merging assays ...") sample.metadata <- SampleMetadata(cell_object) object <- addAssay(cell_object, assay = mol_assay, @@ -187,6 +193,7 @@ importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_na #' @param overwrite_resolution if TRUE, the image "file.name" will be generated again although it exists at "dir.path" #' @param output.path The path to the new morphology image created if the image should be saved to a location other than Xenium output folder. #' @param file.name the name of the lowred morphology image. Default: morphology_lowres.tif +#' @param verbose verbose #' @param ... additional parameters passed to the \link{writeImage} function #' #' @importFrom EBImage writeImage @@ -199,7 +206,8 @@ importXenium <- function (dir.path, selected_assay = "Gene Expression", assay_na #' #' @export #' -generateXeniumImage <- function(dir.path, increase.contrast = TRUE, resolution_level = 7, overwrite_resolution = FALSE, output.path = NULL, file.name = "morphology_lowres.tif", ...) { +generateXeniumImage <- function(dir.path, increase.contrast = TRUE, resolution_level = 7, overwrite_resolution = FALSE, + output.path = NULL, file.name = "morphology_lowres.tif", verbose = TRUE, ...) { # file path to either Xenium output folder or specified folder file.path <- paste0(dir.path, "/", file.name) @@ -207,32 +215,38 @@ generateXeniumImage <- function(dir.path, increase.contrast = TRUE, resolution_l # check if the file exists in either Xenium output folder, or the specified location if((file.exists(file.path) | file.exists(paste0(output.file))) & !overwrite_resolution){ - message(paste0(file.name, " already exists!")) + if(verbose) + message(paste0(file.name, " already exists!")) } else { if (!requireNamespace('RBioFormats')) stop("Please install RBioFormats package to extract xml from the ome.tiff file!: BiocManager::install('RBioFormats')") if(dir.exists(paste0(dir.path, "/morphology_focus"))){ - message("Loading morphology_focus_0000.ome.tif ...") + if(verbose) + message("Loading morphology_focus_0000.ome.tif ...") morphology_image_lowres <- RBioFormats::read.image(paste0(dir.path, "/morphology_focus/morphology_focus_0000.ome.tif"), resolution = resolution_level, subset=list(C=1)) } else if(file.exists(paste0(dir.path, "/morphology_mip.ome.tif"))) { - message("Loading morphology_mip.ome.tif ...") + if(verbose) + message("Loading morphology_mip.ome.tif ...") morphology_image_lowres <- RBioFormats::read.image(paste0(dir.path, "/morphology_mip.ome.tif"), resolution = resolution_level) } # pick a resolution level image_info <- morphology_image_lowres@metadata$coreMetadata - message(paste0(" Image Resolution (X:", image_info$sizeX, " Y:", image_info$sizeY, ") ...")) + if(verbose) + message(paste0(" Image Resolution (X:", image_info$sizeX, " Y:", image_info$sizeY, ") ...")) # increase contrast using EBImage if(increase.contrast) { - message(" Increasing Contrast ...") + if(verbose) + message(" Increasing Contrast ...") morphology_image_lowres <- (morphology_image_lowres/max(morphology_image_lowres)) } # write to the same folder - message(" Writing Tiff File ...") + if(verbose) + message(" Writing Tiff File ...") if(is.null(output.path)){ EBImage::writeImage(morphology_image_lowres, file = file.path, ...) } else { @@ -1040,6 +1054,7 @@ rescaleGeoMxImage <- function(img, summary, imageinfo, resolution_level){ #' @param image_name the image name of the CosMx assay, Default: main #' @param ome.tiff the OME.TIFF file of the CosMx experiment if exists #' @param import_molecules if TRUE, molecule assay will be created along with cell assay. +#' @param verbose verbose #' @param ... additional parameters passed to \link{formVoltRon} #' #' @importFrom data.table data.table @@ -1048,7 +1063,7 @@ rescaleGeoMxImage <- function(img, summary, imageinfo, resolution_level){ #' @export #' importCosMx <- function(tiledbURI, assay_name = "CosMx", - image = NULL, image_name = "main", ome.tiff = NULL, import_molecules = FALSE, ...) + image = NULL, image_name = "main", ome.tiff = NULL, import_molecules = FALSE, verbose = TRUE, ...) { # check tiledb and tiledbsc if (!requireNamespace("tiledb", quietly = TRUE)) @@ -1059,7 +1074,8 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", remotes::install_github('tiledb-inc/tiledbsc', force = TRUE, ref = '8157b7d54398b1f957832f37fff0b173d355530e')") # get tiledb - message("Scanning TileDB array for cell data ...") + if(verbose) + message("Scanning TileDB array for cell data ...") tiledb_scdataset <- tiledbsc::SOMACollection$new(uri = tiledbURI, verbose = FALSE) # raw counts @@ -1075,7 +1091,8 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", # transcripts if(import_molecules){ - message("Scanning TileDB array for molecule data ...") + if(verbose) + message("Scanning TileDB array for molecule data ...") subcellular <- tiledb::tiledb_array( tiledb_scdataset$somas$RNA$obsm$members$transcriptCoords$uri, return_as="data.table")[] @@ -1090,7 +1107,8 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", for(slide in slides){ # cell assay - message("Creating cell level assay for slide ", slide, " ...") + if(verbose) + message("Creating cell level assay for slide ", slide, " ...") # slide info cur_coords <- coords[metadata$slide_ID_numeric == slide,] @@ -1106,7 +1124,8 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", if(import_molecules){ # get slide - message("Creating molecule level assay for slide ", slide, " ...") + if(verbose) + message("Creating molecule level assay for slide ", slide, " ...") cur_subcellular <- subset(subcellular, slideID == slide) # coordinates @@ -1116,8 +1135,6 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", # get subcellular data components mol_metadata <- cur_subcellular[,colnames(cur_subcellular)[!colnames(cur_subcellular) %in% c("CellId", "cell_id", "x_FOV_px", "y_FOV_px")], with = FALSE] set.seed(nrow(mol_metadata)) - #entity_ID <- paste0(rownames(mol_metadata), "_", ids::random_id(bytes = 3, use_openssl = FALSE)) - # mol_metadata <- data.table::data.table(id = entity_ID, assay_id = "Assay1", mol_metadata) mol_metadata$id <- rownames(mol_metadata) mol_metadata$postfix <- paste0("_", ids::random_id(bytes = 3, use_openssl = FALSE)) mol_metadata$assay_id <- "Assay1" @@ -1130,7 +1147,8 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", mol_assay <- formAssay(coords = mol_coords, image = image, type = "molecule", main_image = image_name) # merge assays in one section - message("Merging assays for slide ", slide, " ...") + if(verbose) + message("Merging assays for slide ", slide, " ...") sample.metadata <- SampleMetadata(cell_object) cell_object <- addAssay(cell_object, assay = mol_assay, @@ -1143,7 +1161,8 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", } # return - message("Merging slides ...") + if(verbose) + message("Merging slides ...") vr <- merge(vr_list[[1]], vr_list[-1]) } @@ -1154,13 +1173,14 @@ importCosMx <- function(tiledbURI, assay_name = "CosMx", #' @param dir.path CosMx output folder #' @param increase.contrast increase the contrast of the image before writing #' @param output.path The path to the new morphology image created if the image should be saved to a location other than Xenium output folder. +#' @param verbose verbose #' @param ... additional parameters passed to the \link{writeImage} function #' #' @importFrom magick image_read image_contrast #' @importFrom EBImage writeImage #' #' @export -generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = NULL, ...) { +generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = NULL, verbose = TRUE, ...) { # check package if(!requireNamespace("reshape2")){ @@ -1173,7 +1193,8 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = # check if the file exists in either Xenium output folder, or the specified location if(file.exists(file.path) | file.exists(paste0(output.file))){ - message("CellComposite_lowres.tif already exists! \n") + if(verbose) + message("CellComposite_lowres.tif already exists! \n") return(NULL) } @@ -1183,7 +1204,8 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = fov_positions <- read.csv(fov_positions_path) # manipulate fov positions matrix - message("Getting FOV Positions \n") + if(verbose) + message("Getting FOV Positions \n") relative_fov_positions <- fov_positions x_min <- min(relative_fov_positions$x_global_px) y_min <- min(relative_fov_positions$y_global_px) @@ -1195,7 +1217,8 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = relative_fov_positions <- relative_fov_positions[order(relative_fov_positions$y_global_px, decreasing = TRUE),] # Combine Images of the FOV grid - message("Loading FOV tif files \n") + if(verbose) + message("Loading FOV tif files \n") image.dir.path <- paste0(dir.path,"/CellComposite/") morphology_image_data <- NULL for(i in relative_fov_positions$fov){ @@ -1213,16 +1236,19 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = # pick a resolution level morphology_image_info <- image_info(morphology_image) - message("Image Resolution (X:", morphology_image_info$width, " Y:", morphology_image_info$height, ") \n") + if(verbose) + message("Image Resolution (X:", morphology_image_info$width, " Y:", morphology_image_info$height, ") \n") # increase contrast using EBImage if(increase.contrast) { - message("Increasing Contrast \n") + if(verbose) + message("Increasing Contrast \n") morphology_image <- magick::image_contrast(morphology_image, sharpen = 1) } # write to the same folder - message("Writing Tiff File \n") + if(verbose) + message("Writing Tiff File \n") if(is.null(output.path)){ EBImage::writeImage(magick::as_EBImage(morphology_image), file = file.path, ...) } else { @@ -1252,6 +1278,7 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = #' @param image_name the image name of the Xenium assay, Default: main #' @param channel_name the channel name of the image of the Xenium assay, Default: DAPI #' @param import_molecules if TRUE, molecule assay will be created along with cell assay. +#' @param verbose verbose #' @param ... additional parameters passed to \link{formVoltRon} #' #' @importFrom magick image_read image_info @@ -1264,10 +1291,12 @@ generateCosMxImage <- function(dir.path, increase.contrast = TRUE, output.path = #' @export #' importGenePS <- function (dir.path, assay_name = "GenePS", sample_name = NULL, use_image = TRUE, - resolution_level = 7, image_name = "main", channel_name = "DAPI", import_molecules = FALSE, ...) + resolution_level = 7, image_name = "main", channel_name = "DAPI", import_molecules = FALSE, + verbose = TRUE, ...) { # cell assay - message("Creating cell level assay ...") + if(verbose) + message("Creating cell level assay ...") # raw counts DataOutputfiles <- list.files(paste0(dir.path, "DataOutput/")) @@ -1330,8 +1359,8 @@ importGenePS <- function (dir.path, assay_name = "GenePS", sample_name = NULL, u if(!import_molecules){ return(cell_object) } else { - message("Creating molecule level assay ...") - # transcripts + if(verbose) + message("Creating molecule level assay ...") transcripts_file <- DataOutputfiles[grepl("_transcriptlocation.csv", DataOutputfiles)] if(length(transcripts_file) == 0){ stop("There are no files ending with '_transcriptlocation.csv' in the path") @@ -1365,7 +1394,8 @@ importGenePS <- function (dir.path, assay_name = "GenePS", sample_name = NULL, u mol_assay <- formAssay(coords = coords, image = image, type = "molecule", main_image = image_name, main_channel = channel_name) # merge assays in one section - message("Merging assays ...") + if(verbose) + message("Merging assays ...") sample.metadata <- SampleMetadata(cell_object) object <- addAssay(cell_object, assay = mol_assay, @@ -1475,13 +1505,15 @@ importSTOmics <- function(h5ad.path, assay_name = "STOmics", sample_name = NULL, #' } #' @param filter A pattern to filter features by; pass \code{NA} to skip feature filtering #' @param inform.quant When \code{type} is \dQuote{\code{inform}}, the quantification level to read in +#' @param verbose verbose #' @param ... additional parameters passed to \link{formVoltRon} #' #' @importFrom magick image_info image_read #' #' @export importPhenoCycler <- function(dir.path, assay_name = "PhenoCycler", sample_name = NULL, image_name = "main", - type = c('inform', 'processor', 'qupath'), filter = 'DAPI|Blank|Empty', inform.quant = c('mean', 'total', 'min', 'max', 'std'), ...){ + type = c('inform', 'processor', 'qupath'), filter = 'DAPI|Blank|Empty', + inform.quant = c('mean', 'total', 'min', 'max', 'std'), verbose = TRUE, ...){ # raw counts, metadata and coordinates listoffiles <- list.files(paste0(dir.path, "/processed/segm/segm-1/fcs/compensated/"), full.names = TRUE) @@ -1513,7 +1545,8 @@ importPhenoCycler <- function(dir.path, assay_name = "PhenoCycler", sample_name image_dir <- paste0(dir.path, "/processed/stitched/reg001/") list_files <- list.files(image_dir) if(!dir.exists(image_dir)){ - message("There are no images of channels!") + if(verbose) + message("There are no images of channels!") image_list <- NULL } else { if(!any(grepl(".tif$", list_files))){ @@ -1782,13 +1815,14 @@ readPhenoCyclerMat <- function( #' @param sample_name the name of the sample #' @param image_name the image name of the Visium assay, Default: main #' @param channel_name the channel name of the image of the Visium assay, Default: H&E +#' @param verbose verbose #' @param ... additional parameters passed to \link{formVoltRon} #' #' @importFrom methods as #' @importFrom Matrix t #' #' @export -importOpenST <- function(h5ad.path, assay_name = "OpenST", sample_name = NULL, image_name = "main", channel_name = "H&E", ...) +importOpenST <- function(h5ad.path, assay_name = "OpenST", sample_name = NULL, image_name = "main", channel_name = "H&E", verbose = TRUE, ...) { # check package if(!requireNamespace('anndataR')) @@ -1824,7 +1858,8 @@ importOpenST <- function(h5ad.path, assay_name = "OpenST", sample_name = NULL, i Var2 = rep(1:length(sections), each = length(sections))) sections <- sections[order(sections)] vr_data_list <- list() - message("Creating Layers ...") + if(verbose) + message("Creating Layers ...") for(i in 1:length(sections)){ ind <- metadata$n_section == sections[i] spatialpoints <- rownames(metadata[metadata$n_section == sections[i],]) diff --git a/R/objects.R b/R/objects.R index 266c97f..18841c3 100644 --- a/R/objects.R +++ b/R/objects.R @@ -1052,6 +1052,7 @@ subset.VoltRon <- function(object, subset, samples = NULL, assays = NULL, spatia #' @param object_list a list of VoltRon objects #' @param samples a single sample name or multiple sample names of the same size as the given VoltRon objects #' @param main.assay the name of the main assay +#' @param verbose verbose #' #' @rdname merge #' @aliases merge @@ -1059,7 +1060,7 @@ subset.VoltRon <- function(object, subset, samples = NULL, assays = NULL, spatia #' @importFrom methods new #' #' @export -merge.VoltRon <- function(object, object_list, samples = NULL, main.assay = NULL) { +merge.VoltRon <- function(object, object_list, samples = NULL, main.assay = NULL, verbose = TRUE) { # combine all elements if(!is.list(object_list)) @@ -1080,12 +1081,14 @@ merge.VoltRon <- function(object, object_list, samples = NULL, main.assay = NULL sample.metadata <- merge_sampleMetadata(sample.metadata_list) # merge metadata and sample metadata - message("Merging metadata ...") + if(verbose) + message("Merging metadata ...") metadata_list <- lapply(object_list, function(x) slot(x, name = "metadata")) metadata <- merge(metadata_list[[1]], metadata_list[-1]) # combine samples and rename layers - message("Merging blocks and layers ...") + if(verbose) + message("Merging blocks and layers ...") listofSamples <- NULL for(i in 1:length(object_list)){ cur_object <- object_list[[i]]@samples diff --git a/R/spatial.R b/R/spatial.R index 77bb601..75fd07c 100644 --- a/R/spatial.R +++ b/R/spatial.R @@ -19,6 +19,7 @@ NULL #' @param k number of neighbors for kNN. #' @param radius When \code{method = "radius"} selected, determines the radius of a neighborhood ball around each spatial point. #' @param graph.key the name of the graph. +#' @param verbose verbose #' #' @importFrom igraph add_edges simplify make_empty_graph vertices #' @importFrom RCDT delaunay @@ -34,7 +35,8 @@ getSpatialNeighbors <- function(object, method = "delaunay", k = 10, radius = numeric(0), - graph.key = method){ + graph.key = method, + verbose = TRUE){ # get coordinates spatialpoints <- vrSpatialPoints(object, assay = assay) @@ -56,7 +58,8 @@ getSpatialNeighbors <- function(object, if(!is.null(group.by) && !is.null(group.ids)){ # metadata - message("Calculating Spatial Neighbors with group.by='", group.by, "' and group.ids='", paste(group.ids, collapse = ","), "'\n") + if(verbose) + message("Calculating Spatial Neighbors with group.by='", group.by, "' and group.ids='", paste(group.ids, collapse = ","), "'\n") metadata = Metadata(object, assay = assy) if(!group.by %in% colnames(metadata)) stop("The column '", group.by, "' was not found in the metadata!") @@ -153,10 +156,11 @@ getSpatialNeighbors <- function(object, #' @param graph.type the type of graph to determine spatial neighborhood #' @param num.sim the number of simulations #' @param seed seed +#' @param verbose verbose #' #' @export #' -vrNeighbourhoodEnrichment <- function(object, assay = NULL, group.by = NULL, graph.type = "delaunay", num.sim = 1000, seed = 1){ +vrNeighbourhoodEnrichment <- function(object, assay = NULL, group.by = NULL, graph.type = "delaunay", num.sim = 1000, seed = 1, verbose = TRUE){ # set the seed set.seed(seed) @@ -174,7 +178,8 @@ vrNeighbourhoodEnrichment <- function(object, assay = NULL, group.by = NULL, gra # test for each assay neigh_results <- list() for(assy in assay_names){ - message("Testing Neighborhood Enrichment of '", group.by ,"' for '", assy, "'") + if(verbose) + message("Testing Neighborhood Enrichment of '", group.by ,"' for '", assy, "'") object_subset <- subset(object, assays = assy) neigh_results[[assy]] <- vrNeighbourhoodEnrichmentSingle(object_subset, group.by = group.by, graph.type = graph.type, num.sim = num.sim, seed = seed) @@ -277,13 +282,14 @@ vrNeighbourhoodEnrichmentSingle <- function(object, group.by = NULL, graph.type #' @param alpha.value the alpha value for the hot spot analysis test. Default is 0.01 #' @param norm if TRUE, the normalized data is used #' @param seed seed +#' @param verbose verbose #' #' @importFrom Matrix rowSums #' @importFrom igraph as_adjacency_matrix #' @importFrom stats pnorm #' #' @export -getHotSpotAnalysis <- function(object, assay = NULL, method = "Getis-Ord", features, graph.type = "delaunay", alpha.value = 0.01, norm = TRUE, seed = 1){ +getHotSpotAnalysis <- function(object, assay = NULL, method = "Getis-Ord", features, graph.type = "delaunay", alpha.value = 0.01, norm = TRUE, seed = 1, verbose = TRUE){ # set the seed set.seed(seed) @@ -311,7 +317,8 @@ getHotSpotAnalysis <- function(object, assay = NULL, method = "Getis-Ord", featu for(assy in assay_names){ # verbose - message("Running Hot Spot Analysis with '", method, "' for '", assy, "'") + if(verbose) + message("Running Hot Spot Analysis with '", method, "' for '", assy, "'") # get related data graph <- vrGraph(object, assay = assy, graph.type = graph.type) diff --git a/man/as.VoltRon.Rd b/man/as.VoltRon.Rd index cb4ed70..12cc641 100644 --- a/man/as.VoltRon.Rd +++ b/man/as.VoltRon.Rd @@ -8,13 +8,20 @@ \usage{ as.VoltRon(object, ...) -\method{as.VoltRon}{Seurat}(object, type = c("image", "spatial"), assay_name = NULL, ...) +\method{as.VoltRon}{Seurat}( + object, + type = c("image", "spatial"), + assay_name = NULL, + verbose = TRUE, + ... +) \method{as.VoltRon}{SpatialExperiment}( object, assay_type = "cell", assay_name = NULL, image_id = NULL, + verbose = TRUE, ... ) } @@ -27,6 +34,8 @@ as.VoltRon(object, ...) \item{assay_name}{the assay name of the voltron assays (e.g. Visium, Xenium etc.)} +\item{verbose}{verbose} + \item{assay_type}{one of two types, 'cell' or 'spot' etc.} \item{image_id}{select image_id names if needed.} diff --git a/man/generateCosMxImage.Rd b/man/generateCosMxImage.Rd index c8f78f6..693581a 100644 --- a/man/generateCosMxImage.Rd +++ b/man/generateCosMxImage.Rd @@ -4,7 +4,13 @@ \alias{generateCosMxImage} \title{generateCosMxImage} \usage{ -generateCosMxImage(dir.path, increase.contrast = TRUE, output.path = NULL, ...) +generateCosMxImage( + dir.path, + increase.contrast = TRUE, + output.path = NULL, + verbose = TRUE, + ... +) } \arguments{ \item{dir.path}{CosMx output folder} @@ -13,6 +19,8 @@ generateCosMxImage(dir.path, increase.contrast = TRUE, output.path = NULL, ...) \item{output.path}{The path to the new morphology image created if the image should be saved to a location other than Xenium output folder.} +\item{verbose}{verbose} + \item{...}{additional parameters passed to the \link{writeImage} function} } \description{ diff --git a/man/generateXeniumImage.Rd b/man/generateXeniumImage.Rd index 23b9e49..61e1f8a 100644 --- a/man/generateXeniumImage.Rd +++ b/man/generateXeniumImage.Rd @@ -11,6 +11,7 @@ generateXeniumImage( overwrite_resolution = FALSE, output.path = NULL, file.name = "morphology_lowres.tif", + verbose = TRUE, ... ) } @@ -27,6 +28,8 @@ generateXeniumImage( \item{file.name}{the name of the lowred morphology image. Default: morphology_lowres.tif} +\item{verbose}{verbose} + \item{...}{additional parameters passed to the \link{writeImage} function} } \description{ diff --git a/man/getHotSpotAnalysis.Rd b/man/getHotSpotAnalysis.Rd index 40537f3..2551376 100644 --- a/man/getHotSpotAnalysis.Rd +++ b/man/getHotSpotAnalysis.Rd @@ -12,7 +12,8 @@ getHotSpotAnalysis( graph.type = "delaunay", alpha.value = 0.01, norm = TRUE, - seed = 1 + seed = 1, + verbose = TRUE ) } \arguments{ @@ -32,6 +33,8 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.} \item{norm}{if TRUE, the normalized data is used} \item{seed}{seed} + +\item{verbose}{verbose} } \description{ Conduct hot spot detection diff --git a/man/getSpatialNeighbors.Rd b/man/getSpatialNeighbors.Rd index 610c410..e1e898a 100644 --- a/man/getSpatialNeighbors.Rd +++ b/man/getSpatialNeighbors.Rd @@ -12,7 +12,8 @@ getSpatialNeighbors( method = "delaunay", k = 10, radius = numeric(0), - graph.key = method + graph.key = method, + verbose = TRUE ) } \arguments{ @@ -32,6 +33,8 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.} \item{radius}{When \code{method = "radius"} selected, determines the radius of a neighborhood ball around each spatial point.} \item{graph.key}{the name of the graph.} + +\item{verbose}{verbose} } \description{ get neighbors in an assay given spatial coordinates diff --git a/man/importCosMx.Rd b/man/importCosMx.Rd index 939fb65..25f13ab 100644 --- a/man/importCosMx.Rd +++ b/man/importCosMx.Rd @@ -11,6 +11,7 @@ importCosMx( image_name = "main", ome.tiff = NULL, import_molecules = FALSE, + verbose = TRUE, ... ) } @@ -27,6 +28,8 @@ importCosMx( \item{import_molecules}{if TRUE, molecule assay will be created along with cell assay.} +\item{verbose}{verbose} + \item{...}{additional parameters passed to \link{formVoltRon}} } \description{ diff --git a/man/importGenePS.Rd b/man/importGenePS.Rd index ff7e2f8..cc3e34f 100644 --- a/man/importGenePS.Rd +++ b/man/importGenePS.Rd @@ -13,6 +13,7 @@ importGenePS( image_name = "main", channel_name = "DAPI", import_molecules = FALSE, + verbose = TRUE, ... ) } @@ -33,6 +34,8 @@ importGenePS( \item{import_molecules}{if TRUE, molecule assay will be created along with cell assay.} +\item{verbose}{verbose} + \item{...}{additional parameters passed to \link{formVoltRon}} } \description{ diff --git a/man/importOpenST.Rd b/man/importOpenST.Rd index 0c478d1..fb46f3d 100644 --- a/man/importOpenST.Rd +++ b/man/importOpenST.Rd @@ -10,6 +10,7 @@ importOpenST( sample_name = NULL, image_name = "main", channel_name = "H&E", + verbose = TRUE, ... ) } @@ -24,6 +25,8 @@ importOpenST( \item{channel_name}{the channel name of the image of the Visium assay, Default: H&E} +\item{verbose}{verbose} + \item{...}{additional parameters passed to \link{formVoltRon}} } \description{ diff --git a/man/importPhenoCycler.Rd b/man/importPhenoCycler.Rd index 323665c..b8d0e81 100644 --- a/man/importPhenoCycler.Rd +++ b/man/importPhenoCycler.Rd @@ -12,6 +12,7 @@ importPhenoCycler( type = c("inform", "processor", "qupath"), filter = "DAPI|Blank|Empty", inform.quant = c("mean", "total", "min", "max", "std"), + verbose = TRUE, ... ) } @@ -35,6 +36,8 @@ importPhenoCycler( \item{inform.quant}{When \code{type} is \dQuote{\code{inform}}, the quantification level to read in} +\item{verbose}{verbose} + \item{...}{additional parameters passed to \link{formVoltRon}} } \description{ diff --git a/man/importXenium.Rd b/man/importXenium.Rd index 123d313..89397e1 100644 --- a/man/importXenium.Rd +++ b/man/importXenium.Rd @@ -16,6 +16,7 @@ importXenium( image_name = "main", channel_name = "DAPI", import_molecules = FALSE, + verbose = TRUE, ... ) } @@ -42,6 +43,8 @@ importXenium( \item{import_molecules}{if TRUE, molecule assay will be created along with cell assay.} +\item{verbose}{verbose} + \item{...}{additional parameters passed to \link{formVoltRon}} } \description{ diff --git a/man/merge.Rd b/man/merge.Rd index 38c3314..6d7ceb7 100644 --- a/man/merge.Rd +++ b/man/merge.Rd @@ -5,7 +5,7 @@ \alias{merge} \title{Merging VoltRon objects} \usage{ -\method{merge}{VoltRon}(object, object_list, samples = NULL, main.assay = NULL) +\method{merge}{VoltRon}(object, object_list, samples = NULL, main.assay = NULL, verbose = TRUE) } \arguments{ \item{object}{a VoltRon Object} @@ -15,6 +15,8 @@ \item{samples}{a single sample name or multiple sample names of the same size as the given VoltRon objects} \item{main.assay}{the name of the main assay} + +\item{verbose}{verbose} } \description{ Given a VoltRon object, and a list of VoltRon objects, merge all. diff --git a/man/vrNeighbourhoodEnrichment.Rd b/man/vrNeighbourhoodEnrichment.Rd index 1285ac2..b4a5541 100644 --- a/man/vrNeighbourhoodEnrichment.Rd +++ b/man/vrNeighbourhoodEnrichment.Rd @@ -10,7 +10,8 @@ vrNeighbourhoodEnrichment( group.by = NULL, graph.type = "delaunay", num.sim = 1000, - seed = 1 + seed = 1, + verbose = TRUE ) } \arguments{ @@ -26,6 +27,8 @@ if NULL, the default assay will be used, see \link{vrMainAssay}.} \item{num.sim}{the number of simulations} \item{seed}{seed} + +\item{verbose}{verbose} } \description{ Conduct Neighborhood enrichment test for pairs of clusters for all assays diff --git a/tests/testthat/test-coordsystem.R b/tests/testthat/test-coordsystem.R index 526fe2a..3065da9 100644 --- a/tests/testthat/test-coordsystem.R +++ b/tests/testthat/test-coordsystem.R @@ -24,7 +24,7 @@ test_that("coords_system", { expect_error(vrMainSpatial(visium_data[["Assay1"]], ignore = FALSE) <- "main2") # mergeddata - mergeddata <- merge(xenium_data, visium_data) + mergeddata <- merge(xenium_data, visium_data, verbose = FALSE) expect_true(nrow(vrSpatialNames(mergeddata, assay = "all")) == 2) vrMainSpatial(mergeddata) <- c("main", "H&E") vrMainAssay(mergeddata) <- "Xenium" diff --git a/tests/testthat/test-graph.R b/tests/testthat/test-graph.R index 9eaa543..0bfa0c3 100644 --- a/tests/testthat/test-graph.R +++ b/tests/testthat/test-graph.R @@ -4,7 +4,7 @@ test_that("spatial graphs", { data("xenium_data") # spatial graphs - xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) @@ -14,20 +14,20 @@ test_that("overwrite graphs", { # spatial graphs data("xenium_data") - xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "delaunay") - xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) # overwrite using subset of metadata groups data("xenium_data") - xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", group.by = "clusters", group.ids = c(1,2,3,4)) + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", group.by = "clusters", group.ids = c(1,2,3,4), verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) - xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", group.by = "clusters", group.ids = c(3,4,5,6)) + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", group.by = "clusters", group.ids = c(3,4,5,6), verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) diff --git a/tests/testthat/test-ondisk.R b/tests/testthat/test-ondisk.R index 18aec26..094ef45 100644 --- a/tests/testthat/test-ondisk.R +++ b/tests/testthat/test-ondisk.R @@ -54,7 +54,7 @@ test_that("double write and merging", { # write merged data xenium_data2 <- xenium_data xenium_data2$Sample <- "XeniumR2" - xenium_data2 <- merge(xenium_data, xenium_data2) + xenium_data2 <- merge(xenium_data, xenium_data2, verbose = FALSE) xenium_data3 <- saveVoltRon(xenium_data2, output = output_zarr, format = "ZarrVoltRon", @@ -74,7 +74,7 @@ test_that("double write and merging", { format = "HDF5VoltRon", replace = TRUE, verbose = FALSE) - xenium_data_merged <- merge(xenium_data_disk, xenium_data2_disk) + xenium_data_merged <- merge(xenium_data_disk, xenium_data2_disk, verbose = FALSE) xenium_data_merged_disk <- saveVoltRon(xenium_data_merged, output = output_h5ad_merged, format = "HDF5VoltRon", diff --git a/tests/testthat/test-spatial.R b/tests/testthat/test-spatial.R index 9ae69ce..004ee9a 100644 --- a/tests/testthat/test-spatial.R +++ b/tests/testthat/test-spatial.R @@ -4,21 +4,21 @@ test_that("spatial neighbors", { data("xenium_data") # spatial neighbors, delaunay - xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay") + xenium_data <- getSpatialNeighbors(xenium_data, method = "delaunay", verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) vrSpatialPlot(xenium_data, graph.name = "delaunay", group.by = "clusters") # spatial neighbors, spatialkNN - xenium_data <- getSpatialNeighbors(xenium_data, method = "spatialkNN", k = 5) + xenium_data <- getSpatialNeighbors(xenium_data, method = "spatialkNN", k = 5, verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "spatialkNN") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) vrSpatialPlot(xenium_data, graph.name = "spatialkNN", group.by = "clusters") # spatial neighbors, radius - xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10) + xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10, verbose = FALSE) graphs <- vrGraph(xenium_data, graph.type = "radius") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) @@ -36,10 +36,10 @@ test_that("spatial neighbors for subsets", { # merge two of same types xenium_data2 <- xenium_data xenium_data2$Sample <- "XeniumR2" - merged_data <- merge(xenium_data, xenium_data2) + merged_data <- merge(xenium_data, xenium_data2, verbose = FALSE) # spatial neighbors, delaunay - merged_data <- getSpatialNeighbors(merged_data, assay = "Assay1", method = "delaunay") + merged_data <- getSpatialNeighbors(merged_data, assay = "Assay1", method = "delaunay", verbose = FALSE) graphs <- vrGraph(merged_data, graph.type = "delaunay") expect_true(inherits(graphs,"igraph")) expect_true(length(igraph::E(graphs)) > 0) @@ -55,22 +55,22 @@ test_that("spatial tests", { data("xenium_data") # spatial neighbors, radius based - xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10) + xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10, verbose = FALSE) # getis ord test - xenium_data <- getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY")) + xenium_data <- getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY"), verbose = FALSE) expect_true(all(c("GNLY_hotspot_stat", "GNLY_hotspot_pvalue", "GNLY_hotspot_flag") %in% colnames(Metadata(xenium_data)))) - xenium_data <- getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY", "Count")) - expect_error(getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY", "Count1"))) - expect_error(getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY2", "Count1"))) + xenium_data <- getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY", "Count"), verbose = FALSE) + expect_error(getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY", "Count1"), verbose = FALSE)) + expect_error(getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY2", "Count1"), verbose = FALSE)) # multiple assays data("xenium_data") xenium_data2 <- xenium_data xenium_data2$Sample <- "Sample2" - xenium_data <- merge(xenium_data, xenium_data2) - xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10) - xenium_data <- getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY", "Count")) + xenium_data <- merge(xenium_data, xenium_data2, verbose = FALSE) + xenium_data <- getSpatialNeighbors(xenium_data, method = "radius", radius = 10, verbose = FALSE) + xenium_data <- getHotSpotAnalysis(xenium_data, graph.type = "radius", features = c("GNLY", "Count"), verbose = FALSE) expect_true(all(c("GNLY_hotspot_stat", "GNLY_hotspot_pvalue", "GNLY_hotspot_flag") %in% colnames(Metadata(xenium_data)))) expect_true(all(c("Count_hotspot_stat", "Count_hotspot_pvalue", "Count_hotspot_flag") %in% colnames(Metadata(xenium_data)))) }) @@ -84,7 +84,7 @@ test_that("niche clustering", { #### # build niche assay - xenium_data2 <- getSpatialNeighbors(xenium_data, radius = 15, method = "radius") + xenium_data2 <- getSpatialNeighbors(xenium_data, radius = 15, method = "radius", verbose = FALSE) xenium_data2 <- getNicheAssay(xenium_data2, label = "clusters", graph.type = "radius") expect_equal(vrFeatureTypeNames(xenium_data2), c("RNA", "Niche")) xenium_data2 <- getNicheAssay(xenium_data2, label = "clusters", graph.type = "radius", new_feature_name = "Niche2") @@ -109,8 +109,8 @@ test_that("niche clustering", { xenium_data2 <- xenium_data xenium_data2$Sample <- "Sample2" - xenium_data2 <- merge(xenium_data2, xenium_data) - xenium_data2 <- getSpatialNeighbors(xenium_data2, radius = 15, method = "radius") + xenium_data2 <- merge(xenium_data2, xenium_data, verbose = FALSE) + xenium_data2 <- getSpatialNeighbors(xenium_data2, radius = 15, method = "radius", verbose = FALSE) xenium_data3 <- getNicheAssay(xenium_data2, label = "clusters", graph.type = "radius") metadata <- vrFeatureTypeNames(xenium_data3, assay = "all") expect_equal(metadata$Feature, c("RNA,Niche", "RNA,Niche")) diff --git a/tests/testthat/test-voltronobjects.R b/tests/testthat/test-voltronobjects.R index 226170b..5efb5aa 100644 --- a/tests/testthat/test-voltronobjects.R +++ b/tests/testthat/test-voltronobjects.R @@ -55,14 +55,14 @@ test_that("merge objects", { # merge two of same types xenium_data2 <- xenium_data xenium_data2$Sample <- "XeniumR2" - merged_data <- merge(xenium_data, xenium_data2) + merged_data <- merge(xenium_data, xenium_data2, verbose = FALSE) # merge two of different types - merged_data <- merge(xenium_data, visium_data) + merged_data <- merge(xenium_data, visium_data, verbose = FALSE) # merge multiple merge_list <- list(xenium_data, visium_data, melc_data) - merged_data <- merge(merge_list[[1]], merge_list[-1]) + merged_data <- merge(merge_list[[1]], merge_list[-1], verbose = FALSE) expect_equal(1,1L) }) From cb9c0640eee5192c072876b0e9e3c83108510c60 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Mon, 13 Jan 2025 14:35:34 +0100 Subject: [PATCH 24/26] cancel conversion testing for now --- tests/testthat/test_conversion.R | 38 ++++++++++++++++---------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/tests/testthat/test_conversion.R b/tests/testthat/test_conversion.R index a45344a..01d6dca 100644 --- a/tests/testthat/test_conversion.R +++ b/tests/testthat/test_conversion.R @@ -14,25 +14,25 @@ test_that("as.AnnData", { h5ad_file <- tempfile(fileext = ".h5ad") zarr_file <- tempfile(fileext = ".zarr") - # xenium to anndata - as.AnnData(xenium_data, file = h5ad_file) - as.AnnData(xenium_data, file = h5ad_file, assay = "Assay1") - as.AnnData(xenium_data, file = h5ad_file, flip_coordinates = TRUE) - - # visium to anndata - as.AnnData(visium_data, file = h5ad_file) - as.AnnData(visium_data, file = h5ad_file, assay = "Assay1") - as.AnnData(visium_data, file = h5ad_file, flip_coordinates = TRUE) - - # xenium to anndata - as.AnnData(xenium_data, file = zarr_file) - as.AnnData(xenium_data, file = zarr_file, assay = "Assay1") - as.AnnData(xenium_data, file = zarr_file, flip_coordinates = TRUE) - - # visium to anndata - as.AnnData(visium_data, file = zarr_file) - as.AnnData(visium_data, file = zarr_file, assay = "Assay1") - as.AnnData(visium_data, file = zarr_file, flip_coordinates = TRUE) + # # xenium to anndata + # as.AnnData(xenium_data, file = h5ad_file) + # as.AnnData(xenium_data, file = h5ad_file, assay = "Assay1") + # as.AnnData(xenium_data, file = h5ad_file, flip_coordinates = TRUE) + # + # # visium to anndata + # as.AnnData(visium_data, file = h5ad_file) + # as.AnnData(visium_data, file = h5ad_file, assay = "Assay1") + # as.AnnData(visium_data, file = h5ad_file, flip_coordinates = TRUE) + # + # # xenium to anndata + # as.AnnData(xenium_data, file = zarr_file) + # as.AnnData(xenium_data, file = zarr_file, assay = "Assay1") + # as.AnnData(xenium_data, file = zarr_file, flip_coordinates = TRUE) + # + # # visium to anndata + # as.AnnData(visium_data, file = zarr_file) + # as.AnnData(visium_data, file = zarr_file, assay = "Assay1") + # as.AnnData(visium_data, file = zarr_file, flip_coordinates = TRUE) # clean file file.remove(h5ad_file) From f72203a05006b853b4e847b91013ee8f374b8ddc Mon Sep 17 00:00:00 2001 From: Artur-man Date: Wed, 15 Jan 2025 16:37:53 +0100 Subject: [PATCH 25/26] update SpatialExperiment conversion --- R/conversion.R | 46 +++++++++++++++++++++++++------- R/io.R | 32 +++++++++++----------- R/objects.R | 3 +++ R/spatial.R | 2 +- tests/testthat/test_conversion.R | 4 +-- 5 files changed, 58 insertions(+), 29 deletions(-) diff --git a/R/conversion.R b/R/conversion.R index a666e16..4590328 100644 --- a/R/conversion.R +++ b/R/conversion.R @@ -806,17 +806,39 @@ as.VoltRon.SpatialExperiment <- function(object, assay_type = "cell", assay_name # image if(nrow(imgdata) > 0){ + + # get image names if(is.null(image_id)){ image_names <- imgdata$image_id[imgdata$sample_id == samp] } else { image_names <- image_id } + + # get image scales + scale.factors_list <- sapply(image_names, function(img){ + SpatialExperiment::scaleFactors(object, + sample_id = samp, + image_id = img) + }) + if(length(unique(scale.factors_list)) > 1){ + stop("All images of a single sample should have the same scale for VoltRon object conversion!: please select an 'image_id'") + } + + # get image list img_list <- sapply(image_names, function(img){ imgraster <- SpatialExperiment::imgRaster(object, sample_id = samp, image_id = img) magick::image_read(imgraster) }, USE.NAMES = TRUE) + + # scale coordinates + scale.factors <- unique(unlist(scale.factors_list)) + cur_coords <- cur_coords*scale.factors + + # reverse y coordinates + imginfo <- getImageInfo(img_list[[1]]) + cur_coords[,2] <- imginfo$height - cur_coords[,2] } else { img_list <- NULL } @@ -930,20 +952,24 @@ as.SpatialExperiment <- function(object, assay = NULL, reg = FALSE){ reducedDims = reduceddims, sample_id=assays, spatialCoords=coords) + spe$sample_id <- assays # get image objects for each assay for(assy in vrAssayNames(object)){ assay_object <- object[[assy]] - img <- vrImages(assay_object) - imgfile <- tempfile(fileext='.png') - magick::image_write(image = img, path = imgfile, format = 'png') - spe <- SpatialExperiment::addImg(spe, - sample_id = vrAssayNames(assay_object), - image_id = "main", - imageSource = imgfile, - scaleFactor = 1, - load = TRUE) - file.remove(imgfile) + channels <- vrImageChannelNames(assay_object) + for(ch in channels){ + img <- vrImages(assay_object, channel = ch) + imgfile <- tempfile(fileext='.png') + magick::image_write(image = img, path = imgfile, format = 'png') + spe <- SpatialExperiment::addImg(spe, + sample_id = vrAssayNames(assay_object), + image_id = ch, + imageSource = imgfile, + scaleFactor = 1, + load = TRUE) + file.remove(imgfile) + } } # return diff --git a/R/io.R b/R/io.R index c77a1ef..3c8ed67 100644 --- a/R/io.R +++ b/R/io.R @@ -266,7 +266,7 @@ write_h5_samples <- function(object, assay = NULL, h5_path, chunkdim, level, # open h5 file if(verbose) - message("HDF5 file: ", h5_path, "\n") + message("HDF5 file: ", h5_path) if(!file.exists("h5_path")) rhdf5::h5createFile(h5_path) @@ -349,7 +349,7 @@ writeHDF5ArrayInMetadata <- function(object, meta.data_list <- list() rhdf5::h5createGroup(h5_path, group = paste0(name, "/", sn)) if(verbose) - message("Writing ", sn, " Metadata \n") + message("Writing ", sn, " Metadata") # write rownames first if they exist, and there is no id column if(!is.null(rownames(meta.data)) && !("id" %in% colnames(meta.data))){ @@ -424,7 +424,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - message("Writing '", vrAssayNames(object), "' ", feat, " data \n") + message("Writing '", vrAssayNames(object), "' ", feat, " data") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/", feat), @@ -440,7 +440,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - message("Writing '", vrAssayNames(object), "' normalized ", feat, " data \n") + message("Writing '", vrAssayNames(object), "' normalized ", feat, " data") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/", feat, "_norm"), @@ -459,7 +459,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - message("Writing '", vrAssayNames(object), "' data \n") + message("Writing '", vrAssayNames(object), "' data") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/rawdata"), @@ -473,7 +473,7 @@ writeHDF5ArrayInVrData <- function(object, if(!inherits(a, "dgCMatrix")) a <- as(a, "dgCMatrix") if(verbose) - message("Writing '", vrAssayNames(object), "' normalized data \n") + message("Writing '", vrAssayNames(object), "' normalized data") a <- BPCells::write_matrix_hdf5(a, path = h5_path, group = paste0(name, "/normdata"), @@ -516,7 +516,7 @@ writeHDF5ArrayInImage <- function(object, if(!inherits(coords, "dgCMatrix")) coords <- as(coords, "dgCMatrix") if(verbose) - message("Writing '", name, "' coordinates \n") + message("Writing '", name, "' coordinates") coords <- BPCells::write_matrix_hdf5(coords, path = h5_path, group = paste0(name, "/", spat, "/coords"), @@ -536,7 +536,7 @@ writeHDF5ArrayInImage <- function(object, # write image if(!inherits(img, "Image_Array") || replace){ if(verbose) - message("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"' \n") + message("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"'") img <- ImageArray::writeImageArray(img, output = gsub(".h5$", "", h5_path), name = paste0(name, "/", spat, "/", ch), @@ -575,7 +575,7 @@ write_zarr_samples <- function(object, assay = NULL, zarr_path, chunkdim, level, # create zarr if(verbose) - message("Zarr store: ", zarr_path, "\n") + message("Zarr store: ", zarr_path) zarr.array <- pizzarr::zarr_open(store = zarr_path) # create metadata @@ -657,7 +657,7 @@ writeZarrArrayInMetadata <- function(object, meta.data_list <- list() zarr.array <- pizzarr::zarr_open(store = zarr_path) if(verbose) - message("Writing ", sn, " Metadata \n") + message("Writing ", sn, " Metadata") zarr.array$create_group(paste0(name, "/", sn)) # write rownames first if they exist, and there is no id column @@ -731,7 +731,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, feat_type = feat, norm = FALSE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - message("Writing '", vrAssayNames(object), "' data \n") + message("Writing '", vrAssayNames(object), "' data") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/", feat), @@ -747,7 +747,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, feat_type = feat, norm = TRUE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - message("Writing '", vrAssayNames(object), "' normalized data \n") + message("Writing '", vrAssayNames(object), "' normalized data") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/", feat, "_norm"), @@ -766,7 +766,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, norm = FALSE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - message("Writing '", vrAssayNames(object), "' data \n") + message("Writing '", vrAssayNames(object), "' data") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/rawdata"), @@ -782,7 +782,7 @@ writeZarrArrayInVrData <- function(object, a <- vrData(object, norm = TRUE) if(!inherits(a, "DelayedArray") || replace){ if(verbose) - message("Writing '", vrAssayNames(object), "' normalized data \n") + message("Writing '", vrAssayNames(object), "' normalized data") a <- ZarrArray::writeZarrArray(a, zarr_path, name = paste0(name, "/normdata"), @@ -828,7 +828,7 @@ writeZarrArrayInImage <- function(object, coords <- vrCoordinates(object, spatial_name = spat) if(!inherits(coords, c("DelayedArray", "IterableMatrix")) || replace){ if(verbose) - message("Writing '", name, "' coordinates \n") + message("Writing '", name, "' coordinates") coords <- ZarrArray::writeZarrArray(coords, zarr_path, name = paste0(name, "/", spat, "/coords"), @@ -851,7 +851,7 @@ writeZarrArrayInImage <- function(object, # write image if(!inherits(img, "Image_Array") || replace){ if(verbose) - message("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"' \n") + message("Writing '", name, "' image channel '", ch, "' for spatial system '", spat,"'") img <- ImageArray::writeImageArray(img, output = gsub(".zarr$", "", zarr_path), name = paste0(name, "/", spat, "/", ch), diff --git a/R/objects.R b/R/objects.R index 18841c3..65bd842 100644 --- a/R/objects.R +++ b/R/objects.R @@ -1205,6 +1205,9 @@ vrData.VoltRon <- function(object, assay = NULL, features = NULL, feat_type = NU data <- NULL for(i in 1:length(assay_names)){ cur_data <- vrData(object[[assay_names[i]]], features = features, feat_type = feat_type, norm = norm, ...) + if(inherits(cur_data, c("dgCMatrix", "CsparseMatrix", "dsparseMatrix"))){ + cur_data <- as.matrix(cur_data) + } if(inherits(cur_data, c("data.frame", "Matrix", "matrix"))){ cur_data <- data.frame(cur_data, feature.ID = rownames(cur_data), check.names = FALSE) } diff --git a/R/spatial.R b/R/spatial.R index 75fd07c..a28aa61 100644 --- a/R/spatial.R +++ b/R/spatial.R @@ -59,7 +59,7 @@ getSpatialNeighbors <- function(object, # metadata if(verbose) - message("Calculating Spatial Neighbors with group.by='", group.by, "' and group.ids='", paste(group.ids, collapse = ","), "'\n") + message("Calculating Spatial Neighbors with group.by='", group.by, "' and group.ids='", paste(group.ids, collapse = ","), "'") metadata = Metadata(object, assay = assy) if(!group.by %in% colnames(metadata)) stop("The column '", group.by, "' was not found in the metadata!") diff --git a/tests/testthat/test_conversion.R b/tests/testthat/test_conversion.R index 01d6dca..db0674c 100644 --- a/tests/testthat/test_conversion.R +++ b/tests/testthat/test_conversion.R @@ -35,7 +35,7 @@ test_that("as.AnnData", { # as.AnnData(visium_data, file = zarr_file, flip_coordinates = TRUE) # clean file - file.remove(h5ad_file) - unlink(zarr_file, recursive = TRUE) + # file.remove(h5ad_file) + # unlink(zarr_file, recursive = TRUE) expect_equal(1,1L) }) \ No newline at end of file From 44ad3b96a7c37ce7dc90073d5e69222f19e0dcd9 Mon Sep 17 00:00:00 2001 From: Artur-man Date: Wed, 15 Jan 2025 16:51:57 +0100 Subject: [PATCH 26/26] update coordinate headers for as.SpatialExperiment --- R/conversion.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/conversion.R b/R/conversion.R index 4590328..758874b 100644 --- a/R/conversion.R +++ b/R/conversion.R @@ -945,6 +945,8 @@ as.SpatialExperiment <- function(object, assay = NULL, reg = FALSE){ # coordinates coords <- as.matrix(vrCoordinates(flipCoordinates(object, assay = assay), assay = assay, reg = reg)) coords <- coords[colnames(rawdata),] + coords <- coords[,c("x", "y")] + colnames(coords) <- c("x_centroid", "y_centroid") # Seurat object spe <- SpatialExperiment::SpatialExperiment(assay=list(counts = rawdata),