immunedeconv：一個(gè)R軟件包，用于統(tǒng)一訪問從大量RNA測序數(shù)據(jù)估算免疫細(xì)胞組分的計(jì)算方法。

an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA sequencing data.

1. 安裝

conda create -n deconvolution conda activate deconvolution conda install -c bioconda -c conda-forge r-immunedeconv## 連接超時(shí) #install.packages("remotes") #remotes::install_github("icbi-lab/immunedeconv")#devtools::install_github('icbi-lab/immunedeconv', repos='http://cran.rstudio.com')

2. 準(zhǔn)備表達(dá)譜數(shù)據(jù)

The input data is a?gene?××?sample?gene expression matrix. In general values should be

• TPM-normalized
• not?log-transformed.

gene_expr <- read.table("/data/query_expr_ready.txt",sep='\t',header=T)head(gene_expr)colnames(gene_expr) rownames(gene_expr)rownames(gene_expr) <- gene_expr$IDgene_expr <- gene_expr[,-1] exprs_matrix <- as.matrix(gene_expr) head(exprs_matrix)

3.?估算免疫細(xì)胞比例

library(tidyr) library(immunedeconv)res = deconvolute(exprs_matrix, "quantiseq")res = deconvolute(exprs_matrix, "quantiseq") %>%map_result_to_celltypes(c("T cell CD4+"), "quantiseq")knitr::kable(res, digits=2)res1 <- deconvolute(your_mixture_matrix, "mcp_counter")res2 <- deconvolute(your_mixture_matrix, "xcell")res3 <- deconvolute(your_mixture_matrix, "epic")## method: #quantiseq #timer #cibersort #cibersort_abs #mcp_counter #xcell #epic

參考：

https://github.com/icbi-lab/immunedeconv

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