gs_radar {GeneTonic} | R Documentation |
Radar (spider) plot for gene sets, either for one or more results from functional enrichment analysis.
gs_radar( res_enrich, res_enrich2 = NULL, n_gs = 20, p_value_column = "gs_pvalue" ) gs_spider( res_enrich, res_enrich2 = NULL, n_gs = 20, p_value_column = "gs_pvalue" )
res_enrich |
A |
res_enrich2 |
Analogous to |
n_gs |
Integer value, corresponding to the maximal number of gene sets to be displayed |
p_value_column |
Character string, specifying the column of |
A plotly
object
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) gs_radar(res_enrich = res_enrich) # or using the alias... gs_spider(res_enrich = res_enrich) # with more than one set res_enrich2 <- res_enrich[1:60, ] set.seed(42) shuffled_ones <- sample(seq_len(60)) # to generate permuted p-values res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones] # ideally, I would also permute the z scores and aggregated scores gs_radar(res_enrich = res_enrich, res_enrich2 = res_enrich2)