gs_alluvial {GeneTonic} | R Documentation |
Generate an interactive alluvial plot linking genesets to their associated genes
gs_alluvial( res_enrich, res_de, annotation_obj, gtl = NULL, n_gs = 5, gs_ids = NULL ) gs_sankey( res_enrich, res_de, annotation_obj, gtl = NULL, n_gs = 5, gs_ids = NULL )
res_enrich |
A |
res_de |
A |
annotation_obj |
A |
gtl |
A |
n_gs |
Integer value, corresponding to the maximal number of gene sets to be displayed |
gs_ids |
Character vector, containing a subset 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_alluvial( res_enrich = res_enrich, res_de = res_de, annotation_obj = anno_df, n_gs = 4 ) # or using the alias... gs_sankey( res_enrich = res_enrich, res_de = res_de, annotation_obj = anno_df, n_gs = 4 )