在上次的推文中，我們介紹了腫瘤相關(guān)巨噬細(xì)胞呈現(xiàn)出一種M1和M2亞型的混合狀態(tài)寸士，有小伙伴要問了渤愁，這個(gè)結(jié)論真的是一種普遍規(guī)律嗎牵祟？會不會是一種偶然現(xiàn)象？

的確抖格，如果你是做基礎(chǔ)研究的诺苹，尤其是做巨噬細(xì)胞研究的，僅僅靠一份數(shù)據(jù)很難相信這樣一種過于顛覆傳統(tǒng)的認(rèn)知雹拄∈毡迹可能你已經(jīng)做了好幾年腫瘤相關(guān)巨噬細(xì)胞研究了，可以接受M1和M2的極化模型滓玖，也可以接受M1和M2之間有多種細(xì)胞類型的異質(zhì)化模型坪哄，但一時(shí)間還無法接受M1和M2共存在同一個(gè)細(xì)胞上的模型。因?yàn)閺臐撘庾R里我們認(rèn)為M1和M2之間的關(guān)系類似于水和火势篡，常言道水火不容啊翩肌，怎么可能共存呢？

因此禁悠，我們找到了一篇發(fā)表在cell上的乳腺癌單細(xì)胞文獻(xiàn)念祭，他也得出來了這個(gè)結(jié)論：M1和M2是可以共存的。

image-20200424193111978.png

image-20200424193203849.png

俗話說：耳聽為虛绷蹲，眼見為實(shí)棒卷。因此，我們嘗試使用自己的數(shù)據(jù)來重復(fù)一下這個(gè)結(jié)論祝钢。

此處所用的數(shù)據(jù)是GSE103322比规，是一份頭頸部鱗狀細(xì)胞癌的數(shù)據(jù)，具體可見上次的介紹：單細(xì)胞轉(zhuǎn)錄組分析腫瘤異質(zhì)性

俗話說：耳聽為虛拦英，眼見為實(shí)蜒什。因此，我們嘗試使用自己的數(shù)據(jù)來重復(fù)一下這個(gè)結(jié)論疤估。

此處所用的數(shù)據(jù)是GSE103322灾常，是一份頭頸部鱗狀細(xì)胞癌的數(shù)據(jù)霎冯，具體可見上次的介紹：單細(xì)胞轉(zhuǎn)錄組分析腫瘤異質(zhì)性

options(stringsAsFactors=FALSE)
library(scater)
library(scran)
library(stringr)
library(reshape2)
library(plyr)
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?
####################################################################

讀取數(shù)據(jù)，簡單整理

raw_tpm_file <- "./HNSCC_all_data.txt"
tmp_data <- read.table(raw_tpm_file,head=T,sep="\t",row.names=1,quote="'",stringsAsFactors=F)
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tumor <- sapply(str_split(colnames(tmp_data),"_"),function(x) x[1])
tumor <- str_sub(tumor,-2,-1)
tumor <- paste0("MEEI",str_replace(tumor,"C",""))
table(tumor)
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cell_type <- as.character(tmp_data[5,])
malignant <- as.character(tmp_data[3,]) == "1"
cell_type[malignant] <- "Malignant"
cell_type[cell_type==0] <- "Unknow"
table(cell_type)
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cell_type[cell_type =="-Fibroblast"]<-"Fibroblast"
table(cell_type)
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col_data <- data.frame(tumor=tumor,cellType=cell_type,
lymph=as.integer(tmp_data[2,]),
row.names=colnames(tmp_data))

移除注釋钞瀑，構(gòu)建表達(dá)矩陣

remove_rows <- c(1,2,3,4,5)
all_data <- tmp_data[-remove_rows,]
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####################################################################

過濾細(xì)胞數(shù)較少的樣本和細(xì)胞類型

all_data <- data.matrix(all_data)
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?
all_data[1:6,1:6]
ncol(all_data)
nrow(all_data)
all_data=all_data[apply(all_data,1, function(x) sum(x>0) > ncol(all_data)/2),]
nrow(all_data)
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sce <- SingleCellExperiment(
assays = list(exprs=all_data),
colData = col_data
)
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table(sce $tumor) ? sce<-sce[,!sce$ cellType == "Unknow"]
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nontumor_stats <- table(sce $cellType) nontumor_select <- names(nontumor_stats)[nontumor_stats>=50] selected_nontumor_sce <- sce[,sce$ cellType %in% nontumor_select]
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tumor_sample_stats <- table(sce $tumor) tumor_sample_select <- names(tumor_sample_stats)[tumor_sample_stats>=200] selected_sce <- sce[,sce$ tumor %in% tumor_sample_select]
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table(selected_sce $tumor) table(selected_sce$ cellType)
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selected_tumor_sce <- selected_sce[,selected_sce $cellType=="Malignant"] selected_nontumor_sce <- selected_sce[,selected_sce$ cellType!="Malignant"]
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####################################################################

選擇巨噬細(xì)胞

table(selected_sce $cellType) Macrophage <- selected_sce[,selected_sce$ cellType == "Macrophage"]
dim(assay(Macrophage))
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以下基因純手工整理沈撞。

M1_marker<-c("IL12","IL23","IL12","TNF","IL6","CD86","MHCII","IL1B","MARCO","iNOS",
"IL12","CD64","CD80","CXCR10","IL23","CXCL9","CXCL10","CXCL11",
"CD86","IL1A","IL1B","IL6","TNFa","MHCII","CCL5","IRF5","IRF1","CD40",
"IDO1","KYNU","CCR7","CD45","CD68","CD115","HLA-DR","CD205","CD14")
?
M2_marker<-c("ARG1","ARG2","IL10","CD32","CD163","CD23","CD200R1","PD-L2","PDL1",
"MARCO","CSF1R","CD206","IL1RN","IL1R2","IL4R","CCL4","CCL13","CCL20",
"CCL17","CCL18","CCL22","CCL24","LYVE1","VEGFA","VEGFB","VEGFC","VEGFD",
"EGF","CTSA","CTSB","CSTC","CTSD","TGFB1","TGFB2","TGFB3","MMP14","MMP19",
"MMP9","CLEC7A","WNT7B","FASL","TNFSF12","TNFSF8","CD276","VTCN1","MSR1",
"FN1","IRF4","CD45","CD68","CD115","HLA-DR","CD205","CD14")
?

只剩4個(gè)了，可見基因常用名和通用名經(jīng)常不一致雕什。

M1_marker<-M1_marker[M1_marker%in%rownames(Macrophage)]
M1_marker
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只剩7個(gè)基因了缠俺。

M2_marker<-M2_marker[M2_marker%in%rownames(Macrophage)]
M2_marker
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M1_sce<-Macrophage[M1_marker,]
M1_assay<-assay(M1_sce)
M1_expression<-colSums(M1_assay)/4
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M2_sce<-Macrophage[M2_marker,]
M2_assay<-assay(M2_sce)
M2_expression<-colSums(M2_assay)/7
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result<-as.data.frame(cbind(M1_expression,M2_expression))
cor.test(result[,1],result[,2])
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library(ggpubr)
p<-ggscatter(result,x="M1_expression", y="M2_expression",
add = "reg.line", conf.int = T,cor.coef = T)
ggsave("M1_M2_expression.pdf",p,width=4,height=3)
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M1_M2_expression.jpg

從以上結(jié)果來看，p值小于0.05贷岸，確實(shí)有統(tǒng)計(jì)學(xué)意義壹士，然而相關(guān)系數(shù)不大，考慮我們的標(biāo)志物過濾太多偿警，或者我們使用的細(xì)胞過少躏救，至少M(fèi)1與M2應(yīng)該是正相關(guān)，而非負(fù)相關(guān)關(guān)系螟蒸，因此盒使，我們大致還原了文獻(xiàn)中的結(jié)論，若想要更加精確的結(jié)果尿庐，可以嘗試換一個(gè)巨噬細(xì)胞數(shù)量較多的數(shù)據(jù)集或者將大多數(shù)基因名換成HUGO注釋哦忠怖。

重復(fù)cell文章的圖