gs_summary_overview_pair {GeneTonic} | R Documentation |
Plots a summary of enrichment results - for two sets of results
gs_summary_overview_pair( res_enrich, res_enrich2, n_gs = 20, p_value_column = "gs_pvalue", color_by = "z_score", alpha_set2 = 1 )
res_enrich |
A |
res_enrich2 |
As |
n_gs |
Integer value, corresponding to the maximal number of gene sets to be displayed |
p_value_column |
Character string, specifying the column of |
color_by |
Character, specifying the column of |
alpha_set2 |
Numeric value, between 0 and 1, which specified the alpha transparency used for plotting the points for gene set 2. |
A ggplot
object
gs_summary_overview()
, gs_horizon()
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) # 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) res_enrich2 <- res_enrich[1:42, ] set.seed(42) shuffled_ones <- sample(seq_len(42)) # to generate permuted p-values res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones] res_enrich2$z_score <- res_enrich2$z_score[shuffled_ones] res_enrich2$aggr_score <- res_enrich2$aggr_score[shuffled_ones] # ideally, I would also permute the z scores and aggregated scores gs_summary_overview_pair(res_enrich = res_enrich, res_enrich2 = res_enrich2)