gs_scores {GeneTonic} | R Documentation |
Compute gene set scores for each sample, by transforming the gene-wise change to a geneset-wise change
gs_scores(se, res_de, res_enrich, annotation_obj = NULL)
se |
A |
res_de |
A |
res_enrich |
A |
annotation_obj |
A |
A matrix with the geneset Z scores, e.g. to be plotted with gs_scoresheat()
gs_scoresheat()
plots these scores
library("macrophage") library("DESeq2") library("org.Hs.eg.db") library("AnnotationDbi") # dds object data("gse", package = "macrophage") dds_macrophage <- DESeqDataSet(gse, design = ~line + condition) rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15) dds_macrophage <- estimateSizeFactors(dds_macrophage) vst_macrophage <- vst(dds_macrophage) # annotation object anno_df <- data.frame( gene_id = rownames(dds_macrophage), gene_name = mapIds(org.Hs.eg.db, keys = rownames(dds_macrophage), column = "SYMBOL", keytype = "ENSEMBL"), stringsAsFactors = FALSE, row.names = rownames(dds_macrophage) ) # res object data(res_de_macrophage, package = "GeneTonic") res_de <- res_macrophage_IFNg_vs_naive # res_enrich object data(res_enrich_macrophage, package = "GeneTonic") res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive) res_enrich <- get_aggrscores(res_enrich, res_de, anno_df) scores_mat <- gs_scores(vst_macrophage, res_de, res_enrich[1:50,], anno_df)