今天給大家介紹的是BMKMANU S1000空間轉錄組數(shù)據(jù)與單細胞轉錄組數(shù)據(jù)的關聯(lián)分析。在我們的關聯(lián)分析中可以使用不同分辨率的空間數(shù)據(jù)分別與單細胞數(shù)據(jù)進行關聯(lián)分析，本期我們使用RCTD進行關聯(lián)分析。詳細分析流程如下所示：
RCTD
使用監(jiān)督學習的方法，利用帶注釋的scRNA-seq數(shù)據(jù)來定義空間轉錄組學數(shù)據(jù)中預期存在的細胞類型的細胞特異性情況。
1、單細胞數(shù)據(jù)格式要求
（1）data 1 細胞注釋結果（cellType）
（2）data 2 基因表達矩陣（sc_counts）
2、空間數(shù)據(jù)格式要求
（1）data 1 空間spots位置信息（coords）
（2）data 2 基因表達矩陣（sp_counts）
#安裝spacexr包
# install.packages(“devtools”)
devtools::install_github(“dmcable/spacexr”, build_vignettes?=?FALSE)
#加載R包
library(Seurat)
library(tidyverse)
library(Matrix)
library(spacexr)
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(reshape2)
library(readr)
library(Seurat)
library(config)
library(ggpubr)
library(gridExtra)
library(reshape2)
library(png)
library(patchwork)
library(SingleR)
library(celldex)
#矩陣轉化
Rcpp::sourceCpp(code=’
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
IntegerMatrix asMatrix(NumericVector rp,
NumericVector cp,
NumericVector z,
int nrows,
int ncols){
int k = z.size() ;
IntegerMatrix ?mat(nrows, ncols);
for (int i = 0; i < k; i++){
mat(rp[i],cp[i]) = z[i];
}
return mat;
}
‘?)
as_matrix?<-?function(mat){
row_pos?<-?mat@i
col_pos?<-?findInterval(seq(mat@x)-1,mat@p[-1])
tmp?<-?asMatrix(rp?=?row_pos, cp?=?col_pos, z?=?mat@x,
nrows?=??mat@Dim[1], ncols?=?mat@Dim[2])
row.names(tmp) <-?mat@Dimnames[[1]]
colnames(tmp) <-?mat@Dimnames[[2]]
return(tmp)
}
#讀取單細胞數(shù)據(jù)
expr?<-?Read10X(‘/xxx/04.QC/filtered_feature_bc_matrix/’, cell.column?=?1)
sc_data?<-?CreateSeuratObject(counts?=?expr,assay?=?“RNA”,min.cells=5,min.features=100)
#counts & nUMI
sc_counts?<-?as_matrix(sc_data[[‘RNA’]]@counts)
sc_nUMI?=?colSums(sc_counts)
#注釋
##方法一：SingleR 注釋
load(‘/share/nas1/guochao/database/celldex/MouseRNAseqData.Rdata’)
mouseRNA?<-?ref
sce_for_SingleR<-GetAssayData(sc_data,slot=”data”)
pred.mouseRNA?<-?SingleR(test=sce_for_SingleR,ref=mouseRNA,labels=mouseRNA$label.fine,assay.type.test=”logcounts”,assay.type.ref?=”logcounts”)
pred.mouseRNA$labels<-as.factor(pred.mouseRNA$labels)
cellType=data.frame(barcode=sc_data@assays$RNA@counts@Dimnames[2], celltype=pred.mouseRNA$labels)
names(cellType) =?c(‘barcode’, ‘cell_type’)
cell_types?=?cellType$cell_type; names(cell_types) <-?cellType$barcode?# create cell_types named list
cell_types?=?as.factor(cell_types) # convert to factor data type
##方法二：手動注釋
sc_data?=?NormalizeData(sc_data, normalization.method?=?‘LogNormalize’, scale.factor?=?10000)
sc_data?=?FindVariableFeatures(sc_data, selection.method?=?‘vst’, nfeatures?=?2000)
sc_data?=?ScaleData(sc_data)
sc_data?=?RunPCA(sc_data, features?=?VariableFeatures(object?=?sc_data))
sc_data?=?FindNeighbors(sc_data, dims?=?1:15)
sc_data?=?FindClusters(sc_data, resolution?=?0.25)
markers?=?FindAllMarkers(sc_data, only.pos?=?TRUE, min.pct?=?0.25, logfc.threshold?=?0.25)
top10?=?markers?%>%?group_by(cluster) %>%?top_n(n?=?5, wt?=?avg_log2FC)
my_df?=?sc_data@meta.data?%>%?as.data.frame() %>%
select(seurat_clusters) %>%
rownames_to_column(var?=?‘barcode’) %>%
rename(cluster?=?seurat_clusters)
cellType?=?my_df?%>%?mutate(cell_type?=?case_when( cluster?==?‘0’?~?‘Allantois cell’,
cluster?==?‘1’?~?‘Bergmann glial cell’,
cluster?==?‘2’?~?‘Neuroblast’,
cluster?==?‘3’?~?‘Neuroendocrine cell’,
cluster?==?‘4’?~?‘Fibroblast’,
cluster?==?‘5’?~?‘Type I spiral ganglion neuron’,
cluster?==?‘6’?~?‘Erythroid cell’,
cluster?==?‘7’?~?‘Type II spiral ganglion neuron’,
cluster?==?‘8’?~?‘Epithelial cell’,
cluster?==?‘9’?~?‘Cardiac progenitor cell’,
cluster?==?’10’?~?‘Oligodendrocyte precursor cell’,
cluster?==?’11’?~?‘Endothelial cell’,
cluster?==?’12’?~?‘Megakaryocyte’,
cluster?==?’13’?~?‘Hepatocyte’,
cluster?==?’14’?~?‘Ventricular cardiomyocyte’))
cell_types?=?cellType$cell_type; names(cell_types) <-?cellType$barcode?# create cell_types named list
cell_types?=?as.factor(cell_types) # convert to factor data type
#構建參考集
reference?=?Reference(sc_counts, cell_types, sc_nUMI)
#讀取空間數(shù)據(jù)，這里選擇的是level 13的數(shù)據(jù)
#位置信息
coords?=?read.table(gzfile(‘/xxx/05.AllheStat/BSTViewer_project/subdata/L13_heAuto/barcodes_pos.tsv.gz’), ???header?=?F) %>%?dplyr::rename(barcodes?=?V1, xcoord?=?V2, ycoord?=?V3)
rownames(coords) <-?coords$barcodes; coords$barcodes?<-?NULL
#表達量矩陣
expr?<-?Read10X(‘/xxx/05.AllheStat/BSTViewer_project/subdata/L13_heAuto/’, cell.column?=?1)
sp_data?<-?CreateSeuratObject(counts?=?expr,assay?=?“Spatial”)
sp_counts?<-?as_matrix(sp_data[[‘Spatial’]]@counts)
#nUMI
sp_nUMI?<-?colSums(sp_counts)
#構建空間實驗集
puck?<-?SpatialRNA(coords, sp_counts, sp_nUMI)
#聯(lián)合分析
myRCTD?<-?create.RCTD(puck, reference, max_cores?=?8)
myRCTD?<-?run.RCTD(myRCTD, doublet_mode?=?‘doublet’) #run.RCTD
#查找marker基因
get_marker_data?<-?function(cell_type_names, cell_type_means, gene_list) {
marker_means?=?cell_type_means[gene_list,]
marker_norm?=?marker_means?/?rowSums(marker_means)
marker_data?=?as.data.frame(cell_type_names[max.col(marker_means)])
marker_data$max_epr?<-?apply(cell_type_means[gene_list,],1,max)
colnames(marker_data) =?c(“cell_type”,’max_epr’)
rownames(marker_data) =?gene_list
marker_data$log_fc?<-?0
epsilon?<-?1e-9
for(cell_type?in?unique(marker_data$cell_type)) {
cur_genes?<-?gene_list[marker_data$cell_type?==?cell_type]
other_mean?=?rowMeans(cell_type_means[cur_genes,cell_type_names?!=?cell_type])
marker_data$log_fc[marker_data$cell_type?==?cell_type] <-?log(epsilon?+?cell_type_means[cur_genes,cell_type]) –?log(epsilon?+?other_mean)
}
return(marker_data)
}
cell_type_info_restr?=?myRCTD@cell_type_info$info
de_genes?<-?get_de_genes(cell_type_info_restr, puck, fc_thresh?=?3, expr_thresh?=?.0001, MIN_OBS?=?3)
marker_data_de?=?get_marker_data(cell_type_info_restr[[2]], cell_type_info_restr[[1]], de_genes)
saveRDS(marker_data_de,’/share/nas1/guochao/Test/221107marker_data_de_standard.RDS’)
write.table(marker_data_de, file?=?‘/share/nas1/guochao/Test/221107marker_data_de_standard.tsv’, sep?=?‘\t’,quote?=?F, row.names?=?T)
#構建繪圖數(shù)據(jù)
results?<-?myRCTD@results
results_df?<-?results$results_df
barcodes?=?rownames(results_df[results_df$spot_class?!=?“reject”?&?puck@nUMI?>=?1,])
my_table?=?puck@coords[barcodes,]
my_table$class?=?results_df[barcodes,]$first_type
#繪圖
cal_zoom_rate?<-?function(width, height){
std_width?=?1000
std_height?=?std_width?/?(46?*?31) *?(46?*?36?*?sqrt(3) /?2.0)
if?(std_width?/?std_height?>?width?/?height){
scale?=?width?/?std_width
}
else{
scale?=?height?/?std_height
}
return(scale)
}
png?<-?readPNG(‘/share/nas1/dengdj/testing/Barcode_YF/20220923-YF-N1295-1-2/analysis/XSPT-T/05.AllheStat/allhe/he_roi_small.png’)
zoom_scale?=?cal_zoom_rate(dim(png)[2], dim(png)[1])
my_table?=?my_table?%>%?mutate(across(c(x,y), ~.x*zoom_scale))
col?=?c(“#F56867″,”#FEB915″,”#C798EE”,”#59BE86″,”#7495D3″,”#D1D1D1″,”#6D1A9C”,”#15821E”,”#3A84E6″,”#997273″,”#787878″,”#DB4C6C”,”#9E7A7A”,”#554236″,”#AF5F3C”,”#93796C”,”#F9BD3F”,”#DAB370″)
p?=?ggplot(my_table, aes(x?=?x, y?=?dim(png)[1] –?y))+
background_image(png)+
geom_point(shape?=?16, size?=?1.8, aes(color?=?class))+
coord_cartesian(xlim?=?c(0, dim(png)[2]), y?=?c(0, dim(png)[1]), expand?=?FALSE)+
scale_color_manual(values?=?col)+
theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank(), axis.title.y=element_blank(),axis.text.y=element_blank(),axis.ticks.y=element_blank())+
guides(color?=?guide_legend(override.aes?=?list(size?=?2.5, alphe?=?0.1)))
ggsave(p, file?=?‘/share/nas1/guochao/Test/temp/221107_all.png’, width=10, height=7, dpi?=?300)
ggsave(p, file?=?‘/share/nas1/guochao/Test/temp/221107_all.pdf’, width=10, height=7)
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