The gene set or data used for data analysis
You can install the development version of csGeneset from GitHub with:
# install.packages("devtools")
devtools::install_github("BioInfoCloud/csGeneset")Gene set from MSigDB database
library(csGeneset)
listMSigDB(gsMSigDB)
gobp <- gsMSigDB[["c5.go.bp.v2022-1.Hs.symbols.gmt"]]
gobp_gs <- gobp[["geneSet"]]
head(gobp_gs)
MCP-counter(Microenvironment Cell Populations-counter)
library(csGeneset)
library(MCPcounter)
# Probe annotation.
probesets <- annoMCPcounter[["probesets"]]
# gene annotation
genes <- annoMCPcounter[["genes"]]
?MCPcounter.estimate
probesets and genes are equivalent to the following result:
library(MCPcounter)
library(preprocessCore)
# Probe annotation results.
probesets = read.table(curl('http:https://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/probesets.txt'),
sep='\t',stringsAsFactors=FALSE,colClasses='character')
# gene annotation.
genes = read.table(curl('http:https://raw.githubusercontent.com/ebecht/MCPcounter/master/Signatures/genes.txt'),
sep='\t',
stringsAsFactors=FALSE,
header=TRUE,
colClasses='character',
check.names=FALSE)
Becht E, Giraldo N A, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression[J]. Genome biology, 2016, 17(1): 1-20.
The getGmt function reads data from the .gmt file.
names(gsGSVA)
# Gene sets for GSVA package analysis were extracted.
gsImmCell <- gsGSVA[["ImmCell"]][["geneSet"]]
Classification or annotation of some genes for analysis.