add a raw tsv input

generate_workflow_snakefile.py

@@ -141,7 +141,7 @@ def generate(name, path_yaml = "", path_input = DEFAULT_PATH_INPUT, path_output
                                 #if not param["origin_command"] in raw_inputs:
                                 #   result += "\tinputs[\"" + param["input_name"] + "\"].append(" + expand_begin + param["origin_command"] + "[\"" + param["origin_name"] + "\"]" + expand_end + ")\n"
                                 #   raw_inputs.append(param["origin_command"])
-                                reslt += "\n"
+                                result += "\tinputs[\"" + param["input_name"] + "\"] = " + expand_begin + param["origin_command"] + "[\"" + param["origin_name"] + "\"] " + expand_end + "\n"
                             else:
                                 result += "\tinputs[\"" + param["input_name"] + "\"] = expand(" + expand_begin + "rules." + param["origin_step"] + "__" + param["origin_command"] + ".output." + param["origin_name"] + expand_end + ", sample=SAMPLES)\n"
                         # Inputs de type liste

tools/deseq2/deseq2.rule.snakefile

@@ -14,6 +14,7 @@ rule <step_name>__deseq2:
         script = config["<step_name>__deseq2_command"],
         tx2gene = config["<step_name>__deseq2_tx2gene"],
         annotations = config["<step_name>__deseq2_annotations"],
+        outDir = config["results_dir"]+'/'+config["<step_name>__deseq2_output_dir"],
         biomaRtDataset = config["<step_name>__deseq2_biomaRtDataset"],
     script:
         config["<step_name>__deseq2_script"]
\ No newline at end of file

tools/deseq2/deseq2.script.R

@@ -3,12 +3,11 @@
 #sink(log, type="message")
 library(DESeq2)
 library(apeglm)
-library(BiocParallel)
 library(tximport)
 library(GenomicFeatures)
 library(ggplot2)
 
-register(MulticoreParam(snakemake@threads))
+
 
 samples = read.table(snakemake@config[["popmap_file"]],stringsAsFactors=F)$V1
 conditions = read.table(snakemake@config[["popmap_file"]],stringsAsFactors=F)$V2
@@ -28,11 +27,11 @@ if (snakemake@config[["quantification"]] == "htseq_count")
     # create the DESeq data object
     dds <- DESeqDataSetFromHTSeqCount(
 		sampleTable = sampleTable, 
-		directory = "", #paste0(snakemake@config[["results_dir"]],"/",snakemake@config[["<step_name>__deseq2_output_dir"]],"/"), 
+		directory = "", 
 		design = ~ condition
 		)
 
-} else{
+} else{ # counts where generated with one of these software "salmon", "sailfish", "alevin", "kallisto", "rsem", "stringtie"
     if (snakemake@params[["tx2gene"]] == TRUE){
       # conversion transcrits vers gènes
       TxDb <- makeTxDbFromGFF(file = snakemake@params[["annotations"]])
@@ -46,7 +45,6 @@ if (snakemake@config[["quantification"]] == "htseq_count")
     groups = as.factor(conditions)
     designMat <- model.matrix(~groups)
     colnames(designMat)[2] = "condition"
-    #dds$condition <- factor(dds$condition, levels = unique(conditions))
 
     sampleTable <- data.frame(condition = factor(conditions))
     rownames(sampleTable) <- colnames(txi$counts)
@@ -83,12 +81,6 @@ png(filename = snakemake@output[["PCA"]], height=800, width=800)
   coord_fixed() + ggtitle("PCA on normalized, variance-stabilized transformed counts")
 dev.off()
 
-# cpm_counts = cpm(txi$counts)
-# pca = prcomp(t(cpm_counts))
-
-# png(filename = snakemake@output[["PCA"]], height=800, width=800)
-# ggplot(data.frame(pca$x),aes(x =pca$x[,1] , y = pca$x[,2],col = conditions)) + geom_point()
-# dev.off()
 
 options(digit=4)
 
@@ -110,14 +102,14 @@ cat("# id: Top_genes
 write.table(topGenes,snakemake@output[["Top_genes"]],append=TRUE,sep="\t")
 
 png(filename = snakemake@output[["MA_plot"]], height=800, width=800)
-DESeq2::plotMA(res, ylim=c(-5,5))
+ DESeq2::plotMA(res, ylim=c(-5,5))
 dev.off()
 
 png(filename = snakemake@output[["Volcano_plot"]], height=800, width=800)
-with(res, plot(log2FoldChange, -log10(pvalue), pch=20, main="Volcano plot", xlim=c(-2.5,2)))
-with(subset(res, padj<.05 ), points(log2FoldChange, -log10(pvalue), pch=20, col="red"))
-with(subset(res, abs(log2FoldChange)>1), points(log2FoldChange, -log10(pvalue), pch=20, col="orange"))
-with(subset(res, padj<.05 & abs(log2FoldChange)>1), points(log2FoldChange, -log10(pvalue), pch=20, col="green"))
+  with(res, plot(log2FoldChange, -log10(pvalue), pch=20, main="Volcano plot", xlim=c(-2.5,2)))
+  with(subset(res, padj<.05 ), points(log2FoldChange, -log10(pvalue), pch=20, col="red"))
+  with(subset(res, abs(log2FoldChange)>1), points(log2FoldChange, -log10(pvalue), pch=20, col="orange"))
+  with(subset(res, padj<.05 & abs(log2FoldChange)>1), points(log2FoldChange, -log10(pvalue), pch=20, col="green"))
 dev.off()
 
 
@@ -131,78 +123,59 @@ colnames(df) = "Conditions"
 assayNTD = assay(ntd)
 
 png(filename = snakemake@output[["Heatmap"]], height=800, width=800)
-pheatmap(assayNTD[select,], cluster_rows=FALSE, show_rownames=FALSE, cluster_cols=TRUE, annotation_col=df)
-#heatmap(cpmTop,main = "Heatmap",margins = c(10,1))
+  pheatmap(assayNTD[select,], cluster_rows=FALSE, show_rownames=FALSE, cluster_cols=TRUE, annotation_col=df)
 dev.off()
 
 speciesData = snakemake@params[["biomaRtDataset"]]
-print(speciesData)
+
 if (speciesData != "None") {
   # Add annotation with biomaRt
   library(biomaRt)
-  mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="ensembl.org")
+  tryCatch({
+  mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="uswest.ensembl.org")
 
-  species_mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "ensembl.org", dataset = )
+  species_mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "uswest.ensembl.org", dataset = speciesData)
 
   #for the moment the top genes change to significant genes ?
   #what if we have tarnscript ID instead of gene ID ?
   ann <- getBM(filter="ensembl_gene_id",value=rownames(topGenes),attributes=c("ensembl_gene_id","description","transcript_length"),mart=species_mart)
 
   go_ann <- getBM(filter="ensembl_gene_id",value=rownames(topGenes),attributes=c("ensembl_gene_id","description","go_id","name_1006","namespace_1003"),mart=species_mart)
-  write.table(go_ann,paste(snakemake@output[["Top_genes"]],"_mqc.tsv",sep="\t"))
-
+  write.table(go_ann,paste0(snakemake@params[["outDir"]],"/TopGenes_go_ann_mqc.tsv"),sep="\t",row.names =FALSE, quote = FALSE)
+  },
+  error = function(err) {
+    print("Can't access Ensembl Biomart !")
+  }
+  )
   #test for gene enrichment with gprofiler2
   library(gprofiler2)
   #for the moment the top genes change to significant genes ?
   organisme = gsub("_gene_ensembl", "", speciesData)
-  gostres <- gost(query = rownames(topGenes), 
+  #limit to GO sources
+
+  sources = c("GO:BP", "GO:MF", "GO:CC")
+  query = rownames(topGenes) #setNames(as.list(rownames(topGenes)), rownames(topGenes))
+  gostres <- gost(query = query, 
                   organism = organisme, ordered_query = FALSE, 
-                  multi_query = FALSE, significant = FALSE, exclude_iea = FALSE, 
+                  multi_query = FALSE, significant = FALSE, exclude_iea = TRUE, 
                   measure_underrepresentation = FALSE, evcodes = FALSE, 
                   user_threshold = 0.05, correction_method = "g_SCS", 
                   domain_scope = "annotated", custom_bg = NULL, 
-                  numeric_ns = "", sources = NULL, as_short_link = FALSE)
-
+                  numeric_ns = "", sources = sources, as_short_link = FALSE)
 
-  png(filename = paste0(snakemake@config[["results_dir"]],"/",snakemake@config[["<step_name>__deseq2_output_dir"]],"/gprofiler2_mqc.png"), height=800, width=800)
-  gostplot(gostres, capped = TRUE, interactive = TRUE)
+  png(filename = paste0(snakemake@params[["outDir"]],"/gprofiler2_plot_mqc.png"), height=800, width=800)
+    p <- gostplot(gostres, capped = FALSE, interactive = FALSE)
+    print(p  + ggtitle("gprofiler2 enrichment analysis for top genes to find over-representation of terms from Gene Ontology"))
   dev.off()
 
-  write.table(gostres$result, paste0(snakemake@config[["results_dir"]],"/",snakemake@config[["<step_name>__deseq2_output_dir"]],"/gprofiler2_mqc.tsv"), sep="\t")
+  
+  #la colonne parents est une liste qu'il faut transformer avant write.table !!
+  #write.table(apply(gostres$result,2,as.character), paste0(snakemake@params[["outDir"]],"/gprofiler2_mqc.tsv"), sep="\t",row.names =FALSE, quote = FALSE)
+  #ou
+  ttable = cbind(1:nrow(gostres$result), gostres$result[,-14])
+  colnames(ttable)[1]="id"
+  write.table(ttable, paste0(snakemake@params[["outDir"]],"/gprofiler2_mqc.tsv"), sep="\t",row.names =FALSE, quote = FALSE)
 
 }
-#cpmTop = cpm_counts[rownames(resSigdf),]
-
-
-
-#resLFC <- lfcShrink(dds, coef="groups", type="apeglm")
-#resOrdered <- res[order(res$pvalue),]
- 
-#plotMA(res, ylim=c(-2,2))
-#plotMA(resLFC, ylim=c(-2,2))
-# 
-# ntd <- normTransform(dds)
-# 
-# library("vsn")
-# meanSdPlot(assay(ntd))
-# 
-# library("pheatmap")
-# select <- order(rowMeans(counts(dds,normalized=TRUE)), decreasing=TRUE)[1:20]
-# df <- as.data.frame(colData(dds)[,c("condition")])
-# pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE, cluster_cols=FALSE, annotation_col=df)
-# 
-# 
-# sampleDists <- dist(t(assay(vsd)))
-# 
-# library("RColorBrewer")
-# sampleDistMatrix <- as.matrix(sampleDists)
-# rownames(sampleDistMatrix) <- paste(vsd$condition, vsd$type, sep="-")
-# colnames(sampleDistMatrix) <- NULL
-# colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
-# pheatmap(sampleDistMatrix,
-#          clustering_distance_rows=sampleDists,
-#          clustering_distance_cols=sampleDists,
-#          col=colors)
-# 
-# plotPCA(vsd, intgroup=c("condition"))
-#sink(NULL, type = "message")
+
+