Stacked bar plot — irGSEA.barplot • irGSEA

Easy to show analysis results by stacked bar plot

irGSEA.barplot(
  object = NULL,
  method = NULL,
  significance.color = NULL,
  color.cluster = NULL,
  color.method = NULL,
  cluster.levels = NULL
)

Arguments

object: A list after perform irGSEA.integrate
method: A vector. It should be one or more of the followling : AUCell, UCell, singscore, ssgsea or RRA. Default all methods when it is set to NULL.
significance.color: A vector. Default "c("#D0DFE6FF","#f87669")" when it is set to NULL.
color.cluster: A vector. Default "ggsci::pal_igv()(the number of colnames of enrichment score matrix)" when it is set to NULL.
color.method: A vector. Default "ggsci::pal_igv()(the number of methods )" when it is set to NULL.
cluster.levels: A vector equal to the number of clusters.

Value

stacked bar plot

Examples

if (FALSE) {
# load PBMC dataset by R package SeuratData
library(Seurat)
library(SeuratData)
# download 3k PBMCs from 10X Genomics
InstallData("pbmc3k")
data("pbmc3k.final")
pbmc3k.final <- SeuratObject::UpdateSeuratObject(pbmc3k.final)

# Seurat object
pbmc3k.final <- irGSEA.score(object = pbmc3k.final, assay = "RNA",
slot = "data", msigdb = T, species = "Homo sapiens",
category = "H", geneid = "symbol",
method = c("AUCell", "UCell", "singscore", "ssgsea"), kcdf = 'Gaussian')

# Integrated analysis
result.dge <- irGSEA.integrate(object = pbmc3k.final,
group.by = "seurat_annotations", metadata = NULL, col.name = NULL,
method = c("AUCell","UCell","singscore","ssgsea"))

irGSEA.barplot.plot1 <- irGSEA.barplot(object = result.dge)

}