Updated vignettes (check md/htmls)

README.Rmd

@@ -77,8 +77,19 @@ We provide several vignettes demonstrating the different types of analysis that
 
 We recommend users to start with the following vignette, which demonstrates the different steps in the analysis without too many details yet. This is the recommended vignette to learn the basics of MultiNicheNet.
 
+TEST 
+TEST
+TEST
+TEST
+
+* [MultiNicheNet analysis: MIS-C threewise comparison - step-by-step](vignettes/basic_analysis_steps_MISC.html): `vignette("basic_analysis_steps_MISC", package="multinichenetr")`
 * [MultiNicheNet analysis: MIS-C threewise comparison - step-by-step](vignettes/basic_analysis_steps_MISC.md): `vignette("basic_analysis_steps_MISC", package="multinichenetr")`
 
+TEST 
+TEST
+TEST
+TEST
+
 This vignette provides an example of a comparison between 3 groups. The following vignettes demonstrate how to analyze cell-cell communication differences in other settings. For sake of simplicity, these vignettes also use a MultiNicheNet wrapper function, which encompasses the different steps demonstrated in the previous vignette. These vignettes are the best vignettes to learn how to apply MultiNicheNet to different datastes for addressing different questions.
 
 * [MultiNicheNet analysis: MIS-C pairwise comparison - wrapper function](vignettes/pairwise_analysis_MISC.md): `vignette("pairwise_analysis_MISC.Rmd", package="multinichenetr")`

README.md

@@ -111,8 +111,15 @@ with:
     devtools::install_github("saeyslab/nichenetr")
     devtools::install_github("saeyslab/multinichenetr")
 
+It is possible that during installation the following warning is thrown:
+
+“glmmTMB was built with TMB version 1.9.4” “Current TMB version is 1.9.5
+
+This warning can be safely ignored since this does not affect
+multinichenetr’s installation and functionalities.
+
 multinichenetr is tested via Github Actions version control on Windows,
-Linux (Ubuntu) and Mac (most recently tested R version: R 4.3.0.).
+Linux (Ubuntu) and Mac (most recently tested R version: R 4.3.1.).
 
 ## Learning to use multinichenetr
 
@@ -125,10 +132,17 @@ demonstrates the different steps in the analysis without too many
 details yet. This is the recommended vignette to learn the basics of
 MultiNicheNet.
 
+TEST TEST TEST TEST
+
+-   [MultiNicheNet analysis: MIS-C threewise comparison -
+    step-by-step](vignettes/basic_analysis_steps_MISC.html):
+    `vignette("basic_analysis_steps_MISC", package="multinichenetr")`
 -   [MultiNicheNet analysis: MIS-C threewise comparison -
     step-by-step](vignettes/basic_analysis_steps_MISC.md):
     `vignette("basic_analysis_steps_MISC", package="multinichenetr")`
 
+TEST TEST TEST TEST
+
 This vignette provides an example of a comparison between 3 groups. The
 following vignettes demonstrate how to analyze cell-cell communication
 differences in other settings. For sake of simplicity, these vignettes

vignettes/add_proteomics_MISC.Rmd

@@ -457,8 +457,8 @@ Of this data, we will only keep information about ligands and receptors
 
 ```{r}
 olink_df = xlsx::read.xlsx(
-  "/Users/robinb/Work/current_projects/MISC OLINK/data/summary_difference_diorioMIS-C-vs-HC.xlsx", 1
-  ) %>% as_tibble() %>% 
+  url("https://zenodo.org/records/10908003/files/summary_difference_diorioMIS-C-vs-HC.xlsx"),
+  1) %>% as_tibble() %>% 
   dplyr::rename(gene = variables, logFC = mean_diff_d0, pval = adj_p_values) %>% 
   dplyr::select(gene, logFC, pval) %>% 
   dplyr::mutate(gene = gene %>% make.names()) %>% 

vignettes/basic_analysis_steps_MISC.Rmd

@@ -4,6 +4,7 @@ author: "Robin Browaeys"
 package: "`r BiocStyle::pkg_ver('multinichenetr')`"
 output: 
   BiocStyle::html_document
+output_dir: "/Users/robinb/Work/multinichenetr/vignettes"
 vignette: >
   %\VignetteIndexEntry{MultiNicheNet analysis: MIS-C threewise comparison - step-by-step}
   %\VignetteEngine{knitr::rmarkdown}
@@ -26,6 +27,10 @@ knitr::opts_chunk$set(
 library(BiocStyle)
 ```
 
+<!-- github markdown built using
+rmarkdown::render("vignettes/basic_analysis_steps_MISC.Rmd",output_format = "md_document")
+-->
+
 In this vignette, you can learn how to perform a MultiNicheNet analysis to compare cell-cell communication between conditions of interest. A MultiNicheNet analysis can be performed if you have multi-sample, multi-condition/group single-cell data. We strongly recommend having at least 4 samples in each of the groups/conditions you want to compare. With less samples, the benefits of performing a pseudobulk-based DE analysis are less clear. For those datasets, you can check and run our alternative workflow that makes use of cell-level sample-agnostic differential expression tools.
 
 As input you need a SingleCellExperiment object containing at least the raw count matrix and metadata providing the following information for each cell: the **group**, **sample** and **cell type**.
@@ -647,11 +652,11 @@ In multi-sample datasets, we have the opportunity to look whether expression of
 
 ```{r}
 lr_target_prior_cor = lr_target_prior_cor_inference(
-  receivers_oi = multinichenet_output$prioritization_tables$group_prioritization_tbl$receiver %>% unique(), 
+  receivers_oi = prioritization_tables$group_prioritization_tbl$receiver %>% unique(), 
   abundance_expression_info = abundance_expression_info, 
   celltype_de = celltype_de, 
   grouping_tbl = grouping_tbl, 
-  prioritization_tables = multinichenet_output$prioritization_tables, 
+  prioritization_tables = prioritization_tables, 
   ligand_target_matrix = ligand_target_matrix, 
   logFC_threshold = logFC_threshold, 
   p_val_threshold = p_val_threshold,