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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,62 +1,36 @@ | ||
| #' Cluster by features | ||
| #' | ||
| #' @description Clustering of the features_by_cell matrix | ||
| #' @param features features object or result of CNV calculation | ||
| #' @param features_by_cells features by cells | ||
| #' @param n_comp numeric, Dimensions of reduction to use as input | ||
| #' @param cnv TRUE/FALSE set it to TRUE for clustering CNV results | ||
| #' @param plot_umap whether to plot the UMAP | ||
| #' @param out_dir output directory | ||
| #' @param scale_features whether to scale features | ||
| #' @param ... arguments passed to Seurat::DimPlot | ||
| #' @param ... arguments passed to Seurat::FindCLusters | ||
| #' @return features_by_cells Seurat Object, object with saved dimension reduction components calculate on features by cells matrix | ||
| #' @import Seurat | ||
| #' @importFrom stats as.hclust | ||
| #' @export | ||
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| cluster_by_features <- function(features, n_comp = 10, cnv=FALSE, plot_umap=FALSE, out_dir="./", scale_features=TRUE, ...){ | ||
| cluster_by_features <- function(features_by_cells=NULL, n_comp = 10, ...){ | ||
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| if(!dir.exists(out_dir)){ | ||
| dir.create(out_dir) | ||
| } | ||
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| if(cnv){ | ||
| features_by_cells <- features | ||
| }else{ | ||
| features_by_cells <- t(features$df[, features$type != "factor", drop=F]) | ||
| features_by_cells[is.na(features_by_cells)] <- 0 | ||
| } | ||
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| features_by_cells <- Seurat::CreateSeuratObject(counts = features_by_cells, min.cells = 0, min.features = 0) | ||
| features_by_cells <- CreateSeuratObject(counts = features_by_cells, min.cells = 0, min.features = 0) | ||
| all.genes <- rownames(features_by_cells) | ||
| #scale data | ||
| if(scale_features){ | ||
| features_by_cells <- Seurat::ScaleData(features_by_cells, features = all.genes) | ||
| } | ||
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| features_by_cells <- Seurat::RunPCA(features_by_cells, features = all.genes) | ||
| #scale data | ||
| features_by_cells <- ScaleData(features_by_cells, features = all.genes) | ||
| features_by_cells <- RunPCA(features_by_cells, npcs=n_comp, features = all.genes) | ||
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| n_comp <- min(n_comp, ncol(features_by_cells@reductions$pca)) | ||
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| features_by_cells <- Seurat::FindNeighbors(features_by_cells, dims = 1:n_comp) | ||
| features_by_cells <- Seurat::FindClusters(features_by_cells) | ||
| #features_by_cells <- Seurat::RunUMAP(features_by_cells, dims = 1:n_comp) | ||
| features_by_cells <- FindNeighbors(features_by_cells, dims = 1:n_comp) | ||
| features_by_cells <- FindClusters(features_by_cells, ...) | ||
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| if(plot_umap){ | ||
| grDevices::jpeg(paste0(out_dir, "/feature_umap.jpg"), width = 180, height = 180, res=300, units="mm") | ||
| res <- DimPlot(features_by_cells, ..., combine = F) | ||
| plot(res[[1]]) | ||
| dev.off() | ||
| } | ||
| if(cnv){ | ||
| #features_by_cells <- Seurat::BuildClusterTree(features_by_cells, features = all.genes, reorder = T) | ||
| #hc_cells <- stats::as.hclust(features_by_cells@tools$BuildClusterTree) | ||
| #ans <- list([email protected], hc=hc_cells, sobj=features_by_cells) | ||
| ans <- list(clusters=features_by_cells@active.ident, sobj=features_by_cells) | ||
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| }else{ | ||
| # if(cnv){ | ||
| # #features_by_cells <- Seurat::BuildClusterTree(features_by_cells, features = all.genes, reorder = T) | ||
| # #hc_cells <- stats::as.hclust(features_by_cells@tools$BuildClusterTree) | ||
| # #ans <- list([email protected], hc=hc_cells, sobj=features_by_cells) | ||
| # ans <- list([email protected], sobj=features_by_cells) | ||
| # | ||
| # }else{ | ||
| ans <- list(clusters=features_by_cells@active.ident, sobj=features_by_cells) | ||
| } | ||
| # } | ||
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| return(ans) | ||
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