cellphonedb之人鼠基因互转

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"

# 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"

DownloadMouse reference dataset required for Cell Ranger
wget https://cf.10xgenomics.com/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)

# 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)