{"id":27778,"date":"2024-02-02T16:28:21","date_gmt":"2024-02-02T08:28:21","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27778"},"modified":"2024-02-02T16:28:22","modified_gmt":"2024-02-02T08:28:22","slug":"%e7%a9%ba%e9%97%b4%e8%bd%ac%e5%bd%95%e7%bb%84%e6%96%b0%e5%88%a9%e5%99%a8%ef%bc%81mia%e5%88%86%e6%9e%90%e5%b8%a6%e4%bd%a0%e5%b7%a7%e5%a6%99%e6%8b%86%e8%a7%a3%e7%bb%86%e8%83%9e","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27778","title":{"rendered":"\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u65b0\u5229\u5668\uff01MIA\u5206\u6790\u5e26\u4f60\u5de7\u5999\u201c\u62c6\u89e3\u201d\u7ec6\u80de\u7c7b\u578b\uff01\uff01"},"content":{"rendered":"

\u968f\u7740\u764c\u75c7\u7684\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5206\u6790\u7684\u7206\u706b\uff0c\u8d8a\u6765\u8d8a\u591a\u7684\u80bf\u7624\u5b66TOP\u671f\u520a\u6536\u5f55\u4e86\u76f8\u5f53\u4e00\u90e8\u5206\u7684\u751f\u4fe1\u5206\u6790\u6587\u7ae0\uff0c\u5f88\u591a\u90fd\u5219\u7528\u5230\u4e86MIA(Multimodal intersection analysis)\u5206\u6790\uff0c\u8fd9\u79cd\u5206\u6790\u65b9\u6cd5\u5c0f\u679c\u8fd8\u6ca1\u6709\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u8fc7\uff0c\u8fd9\u8ba9\u5c0f\u679c\u773c\u998b\u7684\u540c\u65f6\u4e5f\u5bf9\u8fd9\u79cd\u65b0\u7684\u5bcc\u96c6\u5206\u6790\u65b9\u6cd5\u6000\u6709\u4e86\u597d\u5947\u5fc3\uff0c\u6240\u4ee5\u5c0f\u679c\u9a6c\u4e0a\u641c\u96c6\u4e86\u4e00\u7bc7Cancer reserch\u4e0a\u7684\u6587\u7ae0\uff0c\u5e26\u7740\u5c0f\u4f19\u4f34\u4eec\u4e00\u8d77\u5b66\u4e60\u5e76\u5b8c\u6210\u6587\u7ae0\u4e2d\u7684MIA\u5206\u6790\u7684\u590d\u73b0\uff01\u672c\u6b21\u7684\u6570\u636e\u53ef\u80fd\u6bd4\u8f83\u591a\uff0c\u6b22\u8fce\u5c0f\u4f19\u4f34\u4eec\u79df\u8d41\u6211\u4eec\u7684\u670d\u52a1\u5668\u6765\u8fd0\u884c\u4ee3\u7801\u54e6~<\/p>\n

<\/p>\n

\u9996\u5148\uff0c\u5c0f\u679c\u9686\u91cd\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e0b\u4eca\u5929\u7684\u6838\u5fc3\u65b9\u6cd5\u2014\u2014MIA\u5206\u6790\uff0cMultimodal intersection analysis\uff0c\u4e5f\u5c31\u662f\u591a\u6a21\u5f0f\u76f8\u4ea4\u5206\u6790\uff0c\u662f\u4e00\u79cd\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u548c\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u6574\u5408\u5206\u6790\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u8bc4\u4f30\u7279\u5b9a\u7ec4\u7ec7\u533a\u57df\u4e2d\u7279\u5b9a\u7ec6\u80de\u7c7b\u578b\u7684\u5bcc\u96c6\u7a0b\u5ea6\u3002\u5176\u539f\u7406\u662f\u8ba1\u7b97\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u7684\u533a\u57df\u5dee\u5f02\u57fa\u56e0\u4e0e\u5355\u7ec6\u80de\u8f6c\u5f55\u7ec4\u9274\u5b9a\u7684\u7ec6\u80de\u7c7b\u578b\u7684marker\u57fa\u56e0\u4e4b\u95f4\u7684\u91cd\u53e0\u5173\u7cfb\uff0c\u4f7f\u7528\u8d85\u51e0\u4f55\u5206\u5e03\u63a8\u65adP\u503c\u3002MIA\u5206\u6790\u53ef\u4ee5\u5e94\u7528\u4e8e\u4e0d\u540c\u7684\u751f\u7269\u5b66\u95ee\u9898\uff0c\u4f8b\u5982\u80f0\u817a\u764c\u4ee5\u53ca\u5375\u5de2\u764c\u7684\u521d\u6b65\u5206\u6790\u79cd\uff0c\u53ef\u4ee5\u5229\u7528MIA\u5206\u6790\u8fdb\u884c\u5bcc\u96c6\uff0c\u5bf9\u7ec6\u80de\u7684\u4e0d\u540c\u7c7b\u578b\u8fdb\u884c\u201c\u62c6\u5206\u201d\u8bc4\u4f30\uff0c\u8fd9\u4f7f\u5f97MIA\u5206\u6790\u5728\u75be\u75c5\u8bca\u65ad\u548c\u4e34\u5e8a\u5b9e\u8df5\u4e2d\u4e5f\u9010\u6e10\u5f97\u5230\u4e86\u8d8a\u6765\u8d8a\u591a\u7684\u5173\u6ce8\u548c\u5e94\u7528\u3002<\/p>\n

\u90a3\u4e48\u5e94\u8be5\u600e\u6837\u5b9e\u73b0\u5462\uff1f\u5c0f\u679c\u8fd9\u5c31\u4ee5\u6587\u732e\u63d0\u4f9b\u7684\u6570\u636e\u548c\u4ee3\u7801\u4e3a\u4f8b\u4e3a\u5c0f\u4f19\u4f34\u4eec\u8fdb\u884c\u8bf4\u660e\u3002<\/p>\n

\u5728\u590d\u73b0\u4e4b\u524d\uff0c\u9700\u8981\u642d\u5efa\u5408\u9002\u7684\u5de5\u4f5c\u73af\u5883\uff0c\u4ee5\u4e0b\u662f\u6587\u732e\u6240\u4f7f\u7528\u7684\u8be6\u7ec6\u914d\u7f6e\uff1a<\/p>\n

R 4.1.0<\/p>\n

Seurat 3.2.1<\/p>\n

dplyr 1.0.9<\/p>\n

limma latest<\/p>\n

scales 1.2.0<\/p>\n

imager 0.42.13<\/p>\n

RColorBrewer 1.1.3<\/p>\n

ggplot2 3.3.6<\/p>\n

gplots 3.1.3<\/p>\n

tidyverse 1.3.1<\/p>\n

\u642d\u5efa\u597d\u81ea\u5df1\u7684\u5e73\u53f0\u4e4b\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u5206\u6790\u4e86\uff01\u5982\u679c\u5927\u5bb6\u4e0d\u4f1a\u642d\u5efa\u81ea\u5df1\u7684\u5e73\u53f0\uff0c\u6b22\u8fce\u79df\u8d41\u6211\u4eec\u7684\u670d\u52a1\u5668\u54e6~<\/p>\n

\u7b2c\u4e00\u6b65\uff0c\u5c06\u6570\u636e\u77e9\u9635\u52a0\u8f7d\u5230Seurat\u4e2d<\/p>\n

\u672c\u6b21\u590d\u73b0\u6240\u7528\u7684\u6570\u636e\u5c0f\u679c\u5df2\u7ecf\u5e2e\u5927\u5bb6\u6574\u7406\u597d\u4e86\u5466\uff1a<\/p>\n

\u94fe\u63a5\uff1ahttps:\/\/pan.baidu.com\/s\/10mos4vmEvXunU1o3SQOz9g<\/p>\n

\u63d0\u53d6\u7801\uff1aikl5<\/p>\n

<\/p>\n

\u8f93\u5165\u6587\u4ef6\u5982\u56fe\u6240\u793a\uff0c\u662f\u4e00\u4e2a\u5b58\u50a8\u6709\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u7684\u6587\u4ef6\uff0c\u5b83\u5305\u542b\u4e86\u7ec6\u80de\u7684\u7a7a\u95f4\u5750\u6807\u548c\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u3002<\/p>\n

##1.\u5bfc\u5165\u5305<\/p>\n

library(Seurat)<\/p>\n

library(dplyr)<\/p>\n

library(limma)<\/p>\n

#\u8bbe\u7f6e\u5de5\u4f5c\u76ee\u5f55<\/p>\n

data_dir = ‘\/data\/ST_data_matrices’<\/p>\n

save_dir = ‘\/results’<\/p>\n

files = list.files(data_dir)<\/p>\n

#\u8bfb\u53d6\u6570\u636e<\/p>\n

files <- c(“file1.txt”, “file2.txt”, “file3.txt”, “file4.txt”)<\/p>\n

for (i in 1:4) {<\/p>\n

file_path <- paste0(data_dir, “\/”, files[i])<\/p>\n

assign(paste0(“dA”, i), read.delim2(file_path, sep = ” “))<\/p>\n

}<\/p>\n

#\u5c06\u6570\u636e\u5bfc\u5165Seurat\uff0c\u8fdb\u884cPCA\uff0cUMAP\u5206\u6790<\/p>\n

# \u7528\u4e00\u4e2a\u5411\u91cf\u5b58\u50a8\u6570\u636e\u6846\u540d<\/p>\n

data_frames <- c(“dA4”, “dA5”, “dA10”, “dA12”)<\/p>\n

# \u7528\u4e00\u4e2a\u5411\u91cf\u5b58\u50a8\u989c\u8272\u540d<\/p>\n

colors <- list(c(‘orange’,’paleturquoise4′, ‘red’, ‘orchid’, ‘seagreen2’, ‘darkgreen’, ‘slateblue1′,’yellow’,’pink’),<\/p>\n

c(‘orange’,’paleturquoise4′, ‘orchid’, ‘seagreen2′,’slateblue1′,’red’,’yellow’),<\/p>\n

c(‘orange’,’paleturquoise4′,’red’,’darkgreen’,’orchid’,’blue’,’cyan’),<\/p>\n

c(‘red’,’darkgreen’, ‘orange’,’paleturquoise4′, ‘orchid’,’blue’))<\/p>\n

# \u7528\u4e00\u4e2a\u5faa\u73af\u5bf9\u6bcf\u4e2a\u6570\u636e\u6846\u8fdb\u884c\u5206\u6790<\/p>\n

for (i in 1:4) {<\/p>\n

sample <- CreateSeuratObject(counts = get(data_frames[i]), project = “ST_HGSOC”, min.cells = 2, min.features = 200)<\/p>\n

#\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u548c\u53d8\u6362<\/p>\n

sample <- SCTransform(sample, verbose = FALSE, return.only.var.genes = FALSE)<\/p>\n

#\u4e3b\u6210\u5206\u5206\u6790<\/p>\n

sample <- RunPCA(sample, assay = “SCT”, verbose = FALSE) # return top 50 PCs<\/p>\n

#\u805a\u7c7b\u5206\u6790<\/p>\n

sample <- FindNeighbors(sample, reduction = “pca”, dims = 1:10) %>%<\/p>\n

FindClusters(verbose = FALSE) # default k.param = 30 for the k-nearest neighbor algorithm<\/p>\n

#\u8fdb\u884c\u964d\u7ef4\u548c\u53ef\u89c6\u5316<\/p>\n

sample <- RunUMAP(sample, reduction = “pca”, dims = 1:10) %>%<\/p>\n

DimPlot(reduction = “umap”, label = F, cols = colors[[i]])<\/p>\n

}<\/p>\n

## 2.\u67e5\u627eDEG\uff08\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff09\u5e76\u7ed8\u5236\u70ed\u56fe\uff0c\u5229\u7528UMAP\u7ed8\u5236\u7c07<\/p>\n

#\u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u4e2d\u7684\u5e73\u5747\u57fa\u56e0<\/p>\n

# data=sample12@assays$RNA<\/p>\n

# temp = 0<\/p>\n

# for (i in seq(1,dim(data)[2]))<\/p>\n

# {temp = temp+sum(data[,i]==0)}<\/p>\n

# avg1 = dim(data)[1]-temp\/dim(data)[2]<\/p>\n

## 3.\u4fdd\u5b58Seurate\u6587\u4ef6<\/p>\n

fnm = paste0(save_dir,’\/seurat_object.RData’)<\/p>\n

save(sample4,sample5,sample10,sample12, file = fnm)<\/p>\n

## 4.\u4fdd\u5b58\u6bcf\u4e2a\u7c07\u7684\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0<\/p>\n

folder = paste0(save_dir,’\/ST differentially expresssed genes up and down’)<\/p>\n

if (!file.exists(folder)){<\/p>\n

dir.create(folder)<\/p>\n

}<\/p>\n

samples = c(sample4,sample5,sample10,sample12)<\/p>\n

sample_names = c(‘A4′,’A5′,’A10′,’A12′)<\/p>\n

for (j in seq(1,length(samples))){<\/p>\n

tmp1 = samples[[j]]<\/p>\n

fnm_pt1 = sample_names[j]<\/p>\n

tmp = tmp1@meta.data$seurat_clusters<\/p>\n

n_clu = nlevels(tmp)<\/p>\n

for (i in seq(0,n_clu-1,by = 1)){<\/p>\n

fnm = paste0(folder,’\/’,fnm_pt1,’_c’,i,’.xls’)<\/p>\n

tmp_marker <- FindMarkers(tmp1, ident.1 = i, min.pct = 0.25, only.pos = FALSE,logfc.threshold = 0)<\/p>\n

tmp_marker$gene <- rownames(tmp_marker)<\/p>\n

tmp_mk <- tmp_marker %>% select(gene, everything())<\/p>\n

tmp_mk <- tmp_mk[order(-tmp_mk$avg_logFC),]<\/p>\n

tmp_mk <- tmp_mk[tmp_mk$p_val<0.05,]<\/p>\n

write.table(tmp_mk,fnm,quote=F,sep=”\\t”,row.names = F)<\/p>\n

}<\/p>\n

}<\/p>\n

## 5.\u4fdd\u5b58\u6bcf\u4e2a\u7c07\u7684\u6570\u91cf<\/p>\n

find_cluster_numbers <- function(SOE, SID) {<\/p>\n

SOE@meta.data %>%<\/p>\n

count(seurat_clusters) %>%<\/p>\n

mutate(name = paste0(SID, “_c”, seurat_clusters)) %>%<\/p>\n

select(name, n) %>%<\/p>\n

column_to_rownames(“name”)<\/p>\n

}<\/p>\n

ss <- bind_rows(<\/p>\n

find_cluster_numbers(sample4, “A4”),<\/p>\n

find_cluster_numbers(sample5, “A5”),<\/p>\n

find_cluster_numbers(sample10, “A10”),<\/p>\n

find_cluster_numbers(sample12, “A12”)<\/p>\n

)<\/p>\n

colnames(ss) <- “spots”<\/p>\n

# \u521b\u5efa\u4e00\u4e2a\u6587\u4ef6\u5939\u6765\u4fdd\u5b58\u7ed3\u679c<\/p>\n

folder <- file.path(save_dir, “ligand_receptor”)<\/p>\n

dir.create(folder, showWarnings = FALSE)<\/p>\n

file_name <- file.path(folder, “ligand_receptor_node_attribute.csv”)<\/p>\n

write.csv(ss, file_name)<\/p>\n

\u4e0b\u56fe\u662f\u6211\u4eec\u805a\u7c7b\u4e4b\u540e\u7684\u7ec6\u80de\u7c07\u5206\u5e03\u6570\u636e\uff0c\u6587\u4ef6\u5b58\u50a8\u4e86\u6bcf\u4e2a\u7ec6\u80de\u7c07\u7684\u8282\u70b9\u5c5e\u6027\uff0c\u5176\u4e2d\u7b2c\u4e00\u5217\u662f\u7ec6\u80de\u7c07\u7684\u540d\u79f0\uff0c\u800c\u7b2c\u4e8c\u5217\u662f\u7ec6\u80de\u7c07\u4e2d\u7684\u7ec6\u80de\u6570\u3002\u6211\u4eec\u4ee5\u7b2c\u4e00\u4e2a\u6837\u672c\u4e3a\u4f8b\uff0c\u67098\u4e2a\u7ec6\u80de\u7c07\uff0c\u5206\u522b\u547d\u540d\u4e3aA4_c0\u5230A4_c7\uff0c\u5176\u4e2d\u7684\u7ec6\u80de\u6570\u4ece27\u5230240\u4e0d\u7b49\uff0c\u8fd9\u4e3a\u6211\u4eec\u540e\u7eed\u7684MIA\u5206\u6790\u6253\u4e0b\u4e86\u57fa\u7840\u3002<\/p>\n

<\/p>\n

ligand_receptor_node_attribute.csv<\/p>\n

\u7b2c\u4e8c\u6b65\uff0c\u5c06Seurat\u5bf9\u8c61\u4e2d\u7684\u805a\u7c7b\u7ed3\u679c\u6620\u5c04\u5230\u7ec6\u80de\u7684\u7a7a\u95f4\u4f4d\u7f6e\u4e2d<\/p>\n

Seurat\u5305\u662f\u8fdb\u884c\u751f\u4fe1\u5206\u6790\u975e\u5e38\u91cd\u8981\u7684\u5de5\u5177\uff0c\u5c0f\u679c\u5728\u4e4b\u524d\u7684\u6587\u7ae0\u4e2d\u5df2\u7ecf\u548c\u5927\u5bb6\u4ecb\u7ecd\u8fc7Seurat\u7684\u8be6\u7ec6\u4f7f\u7528\u65b9\u6cd5\u4e86\uff0c\u5c0f\u4f19\u4f34\u4eec\u5982\u679c\u6709\u9700\u8981\uff0c\u53ef\u4ee5\u5173\u6ce8\u516c\u4f17\u53f7\u8fdb\u884c\u641c\u7d22\u54e6\u3002<\/p>\n

#\u5bfc\u5165\u5305<\/p>\n

library(ggplotify)<\/p>\n

library(ggplot2)<\/p>\n

library(magick)<\/p>\n

library(scales)<\/p>\n

library(imager)<\/p>\n

save_dir = ‘\/results’<\/p>\n

image_dir = ‘\/data\/images’<\/p>\n

fnm = paste0(save_dir,’\/seurat_object.RData’)<\/p>\n

load(fnm)<\/p>\n

#\u7ed8\u5236\u65e0\u80cc\u666f\u7684\u805a\u7c7b\u7ed3\u679c<\/p>\n

plot_clu_nbg <- function(SID,SOE,d,topleft,adjust_x,adjust_y,delta_x,delta_y,im){<\/p>\n

tmp = SOE@meta.data$seurat_cluster<\/p>\n

Nclu = length(levels(tmp))<\/p>\n

nspots = length(tmp)<\/p>\n

colorcode = hue_pal()(Nclu)<\/p>\n

colorcode = c(‘orange’,’green’,’blue’,’red’,’cyan’,’magenta’,’yellow’,’brown’)<\/p>\n

mycols <- c(rep(NA,length(tmp)))<\/p>\n

for (i in seq(1,nspots)){<\/p>\n

mycols[i] = colorcode[tmp[i]]<\/p>\n

}<\/p>\n

filenm = paste0(folder,’\/A’,SID,’noBG.png’)<\/p>\n

png(filenm, width = 512, height = 512)<\/p>\n

par(mar = c(0,0,0,0)) # set zero margins on all 4 sides<\/p>\n

plot(x = NULL, y = NULL, xlim = c(0,dim(im)[1]), ylim = c(0,dim(im)[2]), pch = ”,<\/p>\n

xaxt = ‘n’, yaxt = ‘n’, xlab = ”, ylab = ”, xaxs = ‘i’, yaxs = ‘i’,<\/p>\n

bty = ‘n’) # plot empty figure<\/p>\n

# rasterImage(im, xleft = 0, ybottom = 0, xright = dim(im)[1], ytop = dim(im)[2])<\/p>\n

#\u89e3\u9664\u672c\u884c\u6ce8\u91ca\u5373\u53ef\u7ed8\u5236\u6709\u80cc\u666f\u7684\u805a\u7c7b\u56fe<\/p>\n

points(topleft[1]+(d[,1]-1)*delta_x+apply(d,1,function(x){return(adjust_x(x[2]))}),<\/p>\n

topleft[2]-(d[,2]-1)*delta_y+apply(d,1,function(x){return(adjust_y(x[1]))}),<\/p>\n

col=mycols,pch=20,cex=2.8)<\/p>\n

dev.off()<\/p>\n

}<\/p>\n

#\u7ed8\u5236\u6bcf\u4e2a\u6837\u672c\u7684\u7c07\u56fe<\/p>\n

plot_clu <- function(SID, SOE, d, topleft, adjust_x, adjust_y, delta_x, delta_y, im) {<\/p>\n

tmp <- SOE@meta.data$seurat_cluster<\/p>\n

colorcode <- c(‘orange’,’green’,’blue’,’red’,’cyan’,’magenta’,’yellow’,’brown’)<\/p>\n

df <- data.frame(<\/p>\n

x = topleft[1] + (d[, 1] – 1) * delta_x + apply(d, 1, function(x) adjust_x(x[2])),<\/p>\n

y = topleft[2] – (d[, 2] – 1) * delta_y + apply(d, 1, function(x) adjust_y(x[1])),<\/p>\n

col = colorcode[tmp]<\/p>\n

)<\/p>\n

filenm <- file.path(folder, paste0(“A”, SID, “.png”))<\/p>\n

ggplot(df, aes(x, y, color = col)) +<\/p>\n

annotation_raster(im, xmin = 0, xmax = dim(im)[1], ymin = 0, ymax = dim(im)[2]) +<\/p>\n

geom_point(size = 1.7) +<\/p>\n

theme_void() +<\/p>\n

guides(color = FALSE) +<\/p>\n

ggsave(filenm, width = 512, height = 512, units = “px”)<\/p>\n

}<\/p>\n

# \u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6<\/p>\n

fnm <- file.path(image_dir, “Sample_4_ds.jpg”)<\/p>\n

im <- image_read(fnm)<\/p>\n

# \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3aggplot\u5bf9\u8c61<\/p>\n

p <- as.ggplot(im)<\/p>\n

# \u4f7f\u7528ggplot2\u7684\u529f\u80fd\u6765\u4fee\u6539\u56fe\u50cf<\/p>\n

p + ggtitle(“Sample 4”) + theme(plot.title = element_text(hjust = 0.5))<\/p>\n

#\u5c06\u6240\u6709\u805a\u7c7b\u5f97\u5230\u7684\u7c07\u7ed8\u5236\u5728\u80cc\u666f\u4e0a<\/p>\n

plot_clu(SID,SOE,d,topleft,adjust_x,adjust_y,delta_x,delta_y,im)<\/p>\n

plot_clu_nbg(SID,SOE,d,topleft,adjust_x,adjust_y,delta_x,delta_y,im)<\/p>\n

<\/p>\n

A4.png<\/p>\n

A4noBG.png<\/p>\n

\u5982\u56fe\uff0c\u6211\u4eec\u5c06\u6765\u81ea\u5375\u5de2\u764c\u60a3\u8005\u6837\u672c\u7684\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u4e0e\u516c\u5f00\u53ef\u7528\u7684\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u8fdb\u884c\u4e86\u6574\u5408\u3002\u5e76\u5c06\u6bcf\u4e2a\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u7ec4\u7ec7\u51b7\u51bb\u5207\u7247\u90fd\u5b89\u88c5\u5728\u4e00\u4e2a\u7a7a\u95f4\u7f16\u7801\u7684\u5fae\u9635\u5217\u4e0a\u3002\u5fae\u9635\u5217\u4e0a\u7684\u6591\u70b9\u6839\u636e\u591a\u4e2a\u533a\u57df\u7684\u57fa\u56e0\u8868\u8fbe\u8fdb\u884c\u4e86\u6d4b\u5e8f\u548c\u805a\u7c7b\u3002\u5355\u7ec6\u80deRNA\u6d4b\u5e8f\u6570\u636e\u4e5f\u8fdb\u884c\u4e86\u805a\u7c7b\uff0c\u751f\u6210\u4e86\u591a\u79cd\u7ec6\u80de\u7c7b\u578b\u53ca\u5176\u7279\u5f02\u6027\u57fa\u56e0\u96c6\u3002<\/p>\n

\u901a\u8fc7\u8fd9\u4e00\u6b65\u6620\u5c04\uff0c\u5c31\u53ef\u4ee5\u6e05\u6670\u7684\u5229\u7528\u4e0d\u540c\u7684\u989c\u8272\u533a\u5206\u7279\u5f02\u7684\u57fa\u56e0\u96c6\u4e86\uff0c\u800c\u4e14\u7ed8\u5236\u51fa\u7684\u56fe\u7247\u4e5f\u662f\u975e\u5e38\u6709\u5bf9\u7167\u7279\u6027\u7684\u3002<\/p>\n

\u7b2c\u4e09\u6b65\uff0c\u8bbe\u5b9ascRNA\u7684\u7279\u5f81\u57fa\u56e0<\/p>\n

#\u5bfc\u5165\u5305<\/p>\n

library(ComplexHeatmap)<\/p>\n

library(stringr)<\/p>\n

library(RColorBrewer)<\/p>\n

data_dir = ‘\/data\/scRNAseq_data’<\/p>\n

save_dir = ‘\/results’<\/p>\n

folder = paste0(save_dir,’\/scRNAseq’)<\/p>\n

if (!file.exists(folder)){<\/p>\n

dir.create(folder)<\/p>\n

}<\/p>\n

#\u4ece scRNAseq Shih et al PLoS ONE 13(11): e0206785\u4e0b\u8f7d\u6570\u636e<\/p>\n

library(purrr)<\/p>\n

# \u8bfb\u53d6\uff0c\u8f6c\u7f6e\u6570\u636e<\/p>\n

read_transpose <- function(file) {<\/p>\n

data <- read.csv(gzfile(file), check.names = FALSE)<\/p>\n

data <- t(data)<\/p>\n

colnames(data) <- data[1, ]<\/p>\n

data <- data[-1, ]<\/p>\n

class(data) <- “numeric”<\/p>\n

data<\/p>\n

}<\/p>\n

HG <- c(<\/p>\n

“GSM3348305_589_Omentum_S1.counts.umiCounts.aboveBackground.table.csv.gz”,<\/p>\n

“GSM3348309_TB10040580_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348310_TB10040580met_S1_1_.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348311_TB10040589_4_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348312_TB10040589met_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348313_TB10040600_CD31_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348314_TB10040600_NP-1_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348315_TB10040600_NP-2_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348316_TB10040600_Tumor_S1.counts.umiCounts.table.csv.gz”,<\/p>\n

“GSM3348319_V00331081_Primary_S1.counts.umiCounts.aboveBackground.table.csv.gz”,<\/p>\n

“GSM3348320_V00331151_Metastatic_S1.counts.umiCounts.aboveBackground.table.csv.gz”<\/p>\n

)<\/p>\n

#\u8bbe\u7f6e\u5de5\u4f5c\u76ee\u5f55<\/p>\n

setwd(file.path(data_dir, “GSE118828_RAW”))<\/p>\n

data <- map_dfr(HG, read_transpose, .id = “file”)<\/p>\n

#\u5c06\u6570\u636e\u52a0\u8f7d\u5230 Seurat\u3001Clustering\u3001Cluster Annotation \u4e2d<\/p>\n

#tmp.index = grep(“_”,rownames(data))<\/p>\n

#rownames(data)[tmp.index] = lapply(rownames(data)[tmp.index],str_replace,pattern=”_”,replacement=”-“)<\/p>\n

D13_data <- CreateSeuratObject(counts = data, project = “HGSOC”, min.cells = 3, min.features = 200)<\/p>\n

D13_data[[“percent.mt”]] <- PercentageFeatureSet(D13_data, pattern = “^mt-“)<\/p>\n

VlnPlot(D13_data, features = c(“nFeature_RNA”, “nCount_RNA”, “percent.mt”), ncol = 3)<\/p>\n

D13_data <- subset(D13_data, subset = nFeature_RNA > 500 & nFeature_RNA < 10000 & percent.mt < 25)<\/p>\n

D13_data <- NormalizeData(D13_data, normalization.method = “LogNormalize”, scale.factor = 10000)<\/p>\n

D13_data <- FindVariableFeatures(D13_data, selection.method = “vst”, nfeatures = 3000)<\/p>\n

#\u4e3a\u6240\u6709\u7c07\u7ed8\u5236\u70ed\u56fe\u5e76\u8fdb\u884c\u8d28\u91cf\u63a7\u5236<\/p>\n

data0 <- D13_data@assays$RNA@counts<\/p>\n

D13_data <- CreateSeuratObject(counts = data0, min.cells = 3, min.features = 200)<\/p>\n

D13_data <- NormalizeData(D13_data)<\/p>\n

D13_data <- FindVariableFeatures(D13_data, selection.method = “vst”, nfeatures = 2000)<\/p>\n

VizDimLoadings(D13_data, dims = 1:4, reduction = “pca”)<\/p>\n

D13_data <- RunPCA(D13_data)<\/p>\n

ElbowPlot(D13_data)<\/p>\n

# \u5bfb\u627e\u76f8\u90bb\u7ec6\u80de<\/p>\n

D13_data <- FindNeighbors(D13_data, dims = 1:10)<\/p>\n

# \u5bfb\u627e\u7ec6\u80de\u7c07<\/p>\n

D13_data <- FindClusters(D13_data, resolution = 0.5)<\/p>\n

# \u8fd0\u884cUMAP<\/p>\n

D13_data <- RunUMAP(D13_data, dims = 1:10)<\/p>\n

# \u53ef\u89c6\u5316UMAP\u7ed3\u679c<\/p>\n

DimPlot(D13_data, reduction = “umap”, pt.size = 0.5)<\/p>\n

ctypes <- c(“Ep_1”, “FB_1”, “T”, “Ep_2”, “Myeloid”, “Ep_3”, “FB_2”, “Ep_4”, “Endothelial”, “B”, “FB_3”)<\/p>\n

D13_data$ctypes <- factor(ctypes[D13_data$seurat_clusters])<\/p>\n

Idents(D13_data) <- “ctypes”<\/p>\n

DimPlot(D13_data, reduction = “umap”, pt.size = 0.5)<\/p>\n

D13_data.markers <- FindAllMarkers(D13_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)<\/p>\n

D13_data.markers <- D13_data.markers[D13_data.markers$p_val < 10^(-5), ]<\/p>\n

D13_data.markers$cell_type <- ctypes[D13_data.markers$cluster]<\/p>\n

fnm <- file.path(folder, “scRNAseq_differentially_expressed_genes_shih_2018_annotated.xls”)<\/p>\n

write.table(D13_data.markers, fnm, quote = FALSE, sep = “\\t”, row.names = FALSE)<\/p>\n

#\u4e3a\u6240\u6709\u7c07\u7ed8\u5236\u70ed\u56fe<\/p>\n

Tc.averages <- AverageExpression(Tc, return.seurat=TRUE)<\/p>\n

temp = Tc.averages@assays$RNA@scale.data<\/p>\n

fnm = paste0(folder,’\/scRNAseq_differentially_expressed_genes_shih_2018.xls’)<\/p>\n

DE = read.table(fnm,header = TRUE, stringsAsFactors = F, check.names=F)<\/p>\n

top15 <- DE %>% group_by(cluster) %>% top_n(n = 15, wt = avg_logFC)<\/p>\n

# DE = DE[DE$avg_logFC>1.8,]<\/p>\n

genelist = top15$gene<\/p>\n

# genelist = DE$gene<\/p>\n

temp = temp[genelist,]<\/p>\n

my_palette <- c(“black”, “white”, “brown”)<\/p>\n

data <- data.matrix(temp)<\/p>\n

ht <- Heatmap(data,<\/p>\n

name = “scaled value”,<\/p>\n

cluster_rows = FALSE,<\/p>\n

cluster_columns = FALSE,<\/p>\n

show_row_names = FALSE,<\/p>\n

show_column_names = TRUE,<\/p>\n

column_names_rot = 60,<\/p>\n

col = my_palette,<\/p>\n

heatmap_legend_param = list(title_position = “topleft”, legend_width = unit(4, “cm”))<\/p>\n

)<\/p>\n

# \u4fdd\u5b58\u70ed\u56fe\u4e3apdf\u6587\u4ef6<\/p>\n

filename <- file.path(folder, “scRNAseq differentially expresssed genes.pdf”)<\/p>\n

pdf(filename, 8, 8)<\/p>\n

draw(ht)<\/p>\n

dev.off()<\/p>\n

\u5982\u56fe\u6240\u793a\uff0c\u662f\u5c0f\u679c\u7ed8\u5236\u7684\u7ec6\u80de\u7c7b\u578b\u7279\u5f02\u6027\u57fa\u56e0\u96c6\uff0c\u7528\u4e8e\u5c55\u793a\u591a\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u5176\u4e2d\u7684\u7ea2\u8272\u4ee3\u8868\u8f83\u9ad8\u7684\u6bd4\u4f8b\u6570\u503c\uff0c\u767d\u8272\u4ee3\u8868\u4e2d\u7b49\u6570\u503c\uff0c\u7070\u8272\u4ee3\u8868\u8f83\u4f4e\u7684\u6570\u503c\u3002\u4e0b\u65b9\u7684\u6807\u7b7e\uff08T,B, Myeloid,Ep_1\u7b49\u7b49\uff09\u8868\u793a\u7684\u662f\u4e0d\u540c\u7684\u7ec6\u80de\u7c7b\u578b\u6216\u4e9a\u7fa4\uff0c\u53ea\u662f\u8fdb\u884cMIA\u5206\u6790\u6240\u5fc5\u9700\u7684\u6570\u636e\u3002\u800c\u53f3\u4fa7\u7684\u6807\u7b7e\uff08\u5982CD81, \uff2d\uff33\uff381\u7b49\uff09\u8868\u793a\u7684\u662f\u9009\u5b9a\u7684scRNA\u3002\u7ea2\u8272\u533a\u57df\u8868\u793a\u7ec6\u80de\u4e9a\u7fa4\u548cscRNA\u4e4b\u95f4\u5b58\u5728\u8f83\u5f3a\u7684\u76f8\u5173\u6027\u6216\u76f8\u4e92\u4f5c\u7528\u3002<\/p>\n

\u4f8b\u5982\uff0c\u56fe\u4e2d\u7684T\u7ec6\u80de\u548cB\u7ec6\u80de\u5206\u522b\u5728CD3D\u548cCD83\u57fa\u56e0\u4e0a\u6709\u8f83\u9ad8\u7684\u8868\u8fbe\uff0c\u800cEP 1-1\u7ec6\u80de\u548cEP 1-2\u7ec6\u80de\u5728ATHL1\u57fa\u56e0\u4e0a\u6709\u8f83\u9ad8\u7684\u8868\u8fbe\uff0c\u8fdb\u800c\u53cd\u6620\u4e86\u4e0d\u540c\u7ec6\u80de\u7c7b\u578b\u6216\u4e9a\u7fa4\u7684\u7279\u5f81\u548c\u529f\u80fd\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u975e\u5e38\u660e\u663e\u7684\u770b\u5230\u764c\u75c7\u75c5\u4eba\u7684\u7279\u5f02\u7684scRNA\u4e86\u3002<\/p>\n

<\/p>\n

ScRNA\u7279\u5f81\u57fa\u56e0\u7684DEG<\/p>\n

\u7b2c\u56db\u6b65\uff0cMIA\u5206\u6790<\/p>\n

\u63a5\u4e0b\u6765\u662f\u5c0f\u679c\u671f\u5f85\u5df2\u4e45\u7684MIA\u5206\u6790\uff01<\/p>\n

#\u5bfc\u5165\u5305<\/p>\n

library(tidyverse)<\/p>\n

save_dir = ‘\/results’<\/p>\n

fnm = paste0(save_dir,’\/seurat_object.RData’)<\/p>\n

load(fnm)<\/p>\n

folder = paste0(save_dir,’\/MIA’)<\/p>\n

if (!file.exists(folder)){<\/p>\n

dir.create(folder)<\/p>\n

}<\/p>\n

##1.\u4ece PLoS One. 2018; 13(11): e0206785 \u4e2d\u52a0\u8f7d scRNAseq \u7684 DEG\uff08\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff09<\/p>\n

load_scRNA_DEG <- function(){<\/p>\n

fnm = paste0(save_dir,’\/scRNAseq\/scRNAseq_differentially_expressed_genes_shih_2018.xls’)<\/p>\n

tmp1 = read.table(fnm,header = TRUE, stringsAsFactors = F, check.names=F)<\/p>\n

scRNA = tmp1[tmp1$p_val<10^(-5),]<\/p>\n

clusters = unique(scRNA$cluster)<\/p>\n

n_sc = length(clusters)<\/p>\n

scgenelist = list()<\/p>\n

for (i in seq(1,n_sc)){<\/p>\n

scgenelist[i] = list(scRNA[scRNA$cluster==clusters[i],”gene”])<\/p>\n

}<\/p>\n

return(scgenelist)<\/p>\n

}<\/p>\n

##2.\u52a0\u8f7d ST \u6570\u636e\u7684 DEG\uff08\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff09<\/p>\n

load_ST_DEG <- function(SOE){<\/p>\n

tmp = SOE@meta.data$seurat_clusters<\/p>\n

n_st = nlevels(tmp)<\/p>\n

stgenelist = list()<\/p>\n

for (i in seq(0,n_st-1,by = 1)){<\/p>\n

tmp_marker <- FindMarkers(SOE, ident.1 = i, min.pct = 0.25)<\/p>\n

tmp_marker$gene <- rownames(tmp_marker)<\/p>\n

tmp_mk <- tmp_marker %>% select(gene, everything())<\/p>\n

tmp_mk <- tmp_mk[order(-tmp_mk$avg_logFC),]<\/p>\n

tmp_mk <- tmp_mk[tmp_mk$p_val<0.01,]<\/p>\n

tmp_mk <- tmp_mk[tmp_mk$avg_logFC>0,]<\/p>\n

stgenelist[i+1] = list(tmp_mk$gene)<\/p>\n

}<\/p>\n

return(stgenelist)<\/p>\n

}<\/p>\n

##3.\u8ba1\u7b97 MIA\uff08\u591a\u6a21\u6001\u4ea4\u53c9\u5206\u6790\uff09<\/p>\n

# \u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\u6765\u8ba1\u7b97MIA\u503c<\/p>\n

compute_MIA <- function(scgenelist, stgenelist, SOE, SID) {<\/p>\n

N_tot <- SOE@assays$RNA@counts@Dim[1]<\/p>\n

MIA <- matrix(0, nrow = length(scgenelist), ncol = length(stgenelist))<\/p>\n

MIA <- apply(MIA, c(1, 2), function(i, j) {<\/p>\n

# \u83b7\u53d6scRNA\u548cST\u7684\u57fa\u56e0\u6570<\/p>\n

N_scRNA <- length(scgenelist[[i]])<\/p>\n

N_ST <- length(stgenelist[[j]])<\/p>\n

# \u83b7\u53d6\u4ea4\u96c6\u7684\u57fa\u56e0\u6570<\/p>\n

N_intersect <- length(intersect(scgenelist[[i]], stgenelist[[j]]))<\/p>\n

# \u8ba1\u7b97\u8d85\u51e0\u4f55\u5206\u5e03\u7684\u6982\u7387<\/p>\n

P <- phyper(N_intersect, N_scRNA, N_tot – N_scRNA, N_ST, lower.tail = FALSE, log.p = FALSE)<\/p>\n

# \u8ba1\u7b97MIA\u503c<\/p>\n

MIA_enrich <- -log10(P)<\/p>\n

MIA_deplete <- -log10(1 – P)<\/p>\n

if (MIA_enrich >= MIA_deplete) {<\/p>\n

MIA_enrich<\/p>\n

} else {<\/p>\n

-MIA_deplete<\/p>\n

}<\/p>\n

})<\/p>\n

MIA <- data.frame(MIA)<\/p>\n

rownames(MIA) <- c(“Ep_1”, “FB_1”, “T”, “Ep_2”, “Myeloid”, “Ep_3”, “FB_2”, “Ep_4”, “Endothelial”, “B”, “FB_3”)<\/p>\n

colnames(MIA) <- levels(SOE@meta.data$seurat_clusters)<\/p>\n

# \u4fdd\u5b58\u7ed3\u679c<\/p>\n

fnm <- paste0(folder, “\/”, SID, “.xls”)<\/p>\n

MIA <- format(MIA, digits = 3)<\/p>\n

write.table(MIA, fnm, quote = FALSE, sep = “\\t”, col.names = NA)<\/p>\n

}<\/p>\n

scgenelist <- load_scRNA_DEG()<\/p>\n

SOE <- sample4<\/p>\n

SID <- “A4”<\/p>\n

stgenelist <- load_ST_DEG(SOE)<\/p>\n

compute_MIA(scgenelist, stgenelist, SOE, SID)<\/p>\n

##4.\u7ed8\u5236MIA\u7684\u70ed\u56fe<\/p>\n

load_MIA <- function(SID){<\/p>\n

fnm = paste0(folder,’\/’,SID,’.xls’)<\/p>\n

temp1 = read.table(fnm,header = TRUE, stringsAsFactors = F, check.names=F)<\/p>\n

cnames = colnames(temp1)<\/p>\n

cnames1 = paste0(SID,’_c’,cnames)<\/p>\n

colnames(temp1) = cnames1<\/p>\n

return(temp1)<\/p>\n

}<\/p>\n

#\u5408\u5e76\u4e0d\u540c\u7684\u7c07<\/p>\n

temp = load_MIA(‘A4’)<\/p>\n

for (id in c(‘A5′,’A10′,’A12’)){<\/p>\n

temp1 = load_MIA(id)<\/p>\n

temp = merge(temp,temp1,by = 0)<\/p>\n

rownames(temp) = temp[,1]<\/p>\n

temp = temp[,-1]<\/p>\n

}<\/p>\n

#\u7ed8\u5236\u70ed\u56fe<\/p>\n

my_palette <- c(“red”, “black”, “cyan”)<\/p>\n

data <- data.matrix(temp)<\/p>\n

data[data > 40] <- 40<\/p>\n

filename <- file.path(folder, “MIA_heatmap.pdf”)<\/p>\n

#\u4f7f\u7528pheatmap\u7ed8\u5236\u5e76\u4fdd\u5b58\u70ed\u56fe<\/p>\n

pheatmap(data,<\/p>\n

filename = filename,<\/p>\n

cluster_rows = FALSE,<\/p>\n

cluster_cols = FALSE,<\/p>\n

color = my_palette,<\/p>\n

legend = TRUE,<\/p>\n

legend_breaks = seq(0, 40, by = 5),<\/p>\n

legend_labels = c(“0”, “5”, “10”, “15”, “20”, “25”, “30”, “35”, “40”),<\/p>\n

cellwidth = 10,<\/p>\n

cellheight = 6,<\/p>\n

fontsize = 10,<\/p>\n

fontsize_row = 10,<\/p>\n

fontsize_col = 10,<\/p>\n

angle_col = 90,<\/p>\n

main = “P value (-log10)”,<\/p>\n

xlab = “ST clusters”<\/p>\n

\uff09<\/p>\n

<\/p>\n

MIA\u5206\u6790\u70ed\u56fe<\/p>\n

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