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cellphonedb目前针对人类基因，因此，对于小鼠基因涉及到人鼠基因的转换，本教程正是解决这一问题。 1 少量人鼠基因转换的函数1-1 小鼠基因转人类基因library("biomaRt")
human = useEnsembl(biomart="ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useEnsembl(biomart="ensembl", dataset = "mmusculus_gene_ensembl")
# Basic function to convert mouse to human gene names
convertMouseGeneList <- function(x){
genesV2 = getLDS(attributes = c("mgi_symbol"), filters = "mgi_symbol", values = x , mart = mouse, attributesL = c("hgnc_symbol"), martL = human, uniqueRows=T)
humanx <- unique(genesV2[, 2])
# Print the first 6 genes found to the screen
return(humanx)
}
musGenes <- c("Hmmr", "Tlx3", "Cpeb4")
convertMouseGeneList(musGenes)
## [1] "HMMR" "CPEB4" "TLX3"
1-2 人类基因转小鼠基因# Basic function to convert human to mouse gene names
convertHumanGeneList <- function(x){
genesV2 = getLDS(attributes = c("hgnc_symbol"), filters = "hgnc_symbol", values = x , mart = human, attributesL = c("mgi_symbol"), martL = mouse, uniqueRows=T)
humanx <- unique(genesV2[, 2])
# Print the first 6 genes found to the screen
return(humanx)
}
humGenes <- c("HMMR","TLX3","CPEB4")
convertHumanGeneList(humGenes)
## [1] "Cpeb4" "Hmmr" "Tlx3"
2 从源头转小鼠基因到人类基因从10X Genomics官方网站[1]下载小鼠参考基因组文件，
运行cellranger后，得到feature.tsv文件，然后，将小鼠基因从源头转为人类基因。 DownloadMouse reference dataset required for Cell Ranger
wget https://cf./supp/cell-exp/refdata-gex-mm10-2020-A.tar.gz
# Offer downloadable file function
library(tidyverse)
library(DT)
create_dt <- function(x){
DT::datatable(x,
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('csv', 'excel', 'pdf'),
lengthMenu = list(c(10,25,50,-1), c(10,25,50,"All"))))
}
# 读取features.tsv文件
gene_list <- read.table('features.tsv', header=F, sep='\t')
create_dt(gene_list)
 # 转小鼠基因名为人类基因名
genesV2 = getLDS(attributes = c("mgi_symbol"), filters = "mgi_symbol", values = gene_list$V2, mart = mouse, attributesL = c("hgnc_symbol"), martL = human, uniqueRows=T)
genesV2 <- genesV2[!duplicated(genesV2[,2]),]
create_dt(genesV2)
 # 保存文件
# write.table(genesV2, "mouse_to_human_genes.txt", sep="\t", row.names=F, quote=F)
3 应用转换后的小鼠到人类基因表# 读取文件
genesV2 <- read.table("mouse_to_human_genes.txt", sep="\t", header=T)
# 准备小鼠数据集
library(Seurat)
library(SeuratData)
# To see a manifest of all available datasets
AvailableData()
# Choose small mouse dataset
InstallData('stxKidney')
data('stxKidney')
stxKidney
## An object of class Seurat
## 31053 features across 1438 samples within 1 assay
## Active assay: Spatial (31053 features, 0 variable features)
## To accelerate, sample 100 cells
seurat_object <- stxKidney
sub_cells <- subset(seurat_object, cells = sample(Cells(seurat_object), 100))
sub_cells@assays$Spatial[1:4,1:4]
## 4 x 4 sparse Matrix of class "dgCMatrix"
## GTGGACGCATTTGTCC-1 CTTAGTGTAGTAGCAT-1 ACGCAAACTAATAGAT-1
## Xkr4 . . .
## Gm1992 . . .
## Gm37381 . . .
## Rp1 . . .
## GGGCTATGATCGATGG-1
## Xkr4 .
## Gm1992 .
## Gm37381 .
## Rp1 .
## Extract Expression Data
sp1 <- sub_cells
sp1_counts <- as.matrix(sp1@assays$Spatial@data) # Notice: 这里应用的是空间数据，常规转录组数据提取用sp1@assays$RNA@data
sp1_counts <- data.frame(gene=rownames(sp1_counts), sp1_counts, check.names = F)
dim(sp1_counts)
## [1] 31053 101
sp1_counts[1:4, 1:4]
## gene GTGGACGCATTTGTCC-1 CTTAGTGTAGTAGCAT-1 ACGCAAACTAATAGAT-1
## Xkr4 Xkr4 0 0 0
## Gm1992 Gm1992 0 0 0
## Gm37381 Gm37381 0 0 0
## Rp1 Rp1 0 0 0
# 转小鼠基因名为人类基因名
sp1_counts$Gene <- genesV2[match(sp1_counts$gene, genesV2[,1]),2]
sp1_counts <- subset(sp1_counts, Gene!='NA')
sp1_counts <- dplyr::select(sp1_counts, Gene, everything())
sp1_counts <- sp1_counts[, !(colnames(sp1_counts) %in% 'gene')]
dim(sp1_counts)
## [1] 16404 101
sp1_counts[1:4,1:4]
## Gene GTGGACGCATTTGTCC-1 CTTAGTGTAGTAGCAT-1 ACGCAAACTAATAGAT-1
## Xkr4 XKR4 0 0 0
## Rp1 RP1 0 0 0
## Sox17 SOX17 0 0 1
## Mrpl15 MRPL15 3 2 3
# # 保存文件
# write.table(sp1_counts, "sp1_counts_human.txt", row.names=F, sep='\t', quote=F)
文中链接[1]10X Genomics官方网站: https://support./single-cell-gene-expression/software/downloads/latest?
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