在上次的推文中，我們介紹了腫瘤相關巨噬細胞呈現(xiàn)出一種M1和M2亞型的混合狀態(tài)，有小伙伴要問了，這個結(jié)論真的是一種普遍規(guī)律嗎？會不會是一種偶然現(xiàn)象？
的確，如果你是做基礎研究的，尤其是做巨噬細胞研究的，僅僅靠一份數(shù)據(jù)很難相信這樣一種過于顛覆傳統(tǒng)的認知?？赡苣阋呀?jīng)做了好幾年腫瘤相關巨噬細胞研究了，可以接受M1和M2的極化模型，也可以接受M1和M2之間有多種細胞類型的異質(zhì)化模型，但一時間還無法接受M1和M2共存在同一個細胞上的模型。因為從潛意識里我們認為M1和M2之間的關系類似于水和火，常言道水火不容啊，怎么可能共存呢？
因此，我們找到了一篇發(fā)表在cell上的乳腺癌單細胞文獻，他也得出來了這個結(jié)論：M1和M2是可以共存的。
俗話說：耳聽為虛，眼見為實。因此，我們嘗試使用自己的數(shù)據(jù)來重復一下這個結(jié)論。
此處所用的數(shù)據(jù)是GSE103322，是一份頭頸部鱗狀細胞癌的數(shù)據(jù)，具體可見上次的介紹：單細胞轉(zhuǎn)錄組分析腫瘤異質(zhì)性
俗話說：耳聽為虛，眼見為實。因此，我們嘗試使用自己的數(shù)據(jù)來重復一下這個結(jié)論。
此處所用的數(shù)據(jù)是GSE103322，是一份頭頸部鱗狀細胞癌的數(shù)據(jù)，具體可見上次的介紹：單細胞轉(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，構(gòu)建表達矩陣
remove_rows <- c(1,2,3,4,5)
all_data <- tmp_data[-remove_rows,]
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####################################################################
過濾細胞數(shù)較少的樣本和細胞類型
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(scecellType == "Unknow"]
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nontumor_stats <- table(scecellType %in% nontumor_select]
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tumor_sample_stats <- table(scetumor %in% tumor_sample_select]
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table(selected_scecellType)
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selected_tumor_sce <- selected_sce[,selected_scecellType!="Malignant"]
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####################################################################
選擇巨噬細胞
table(selected_scecellType == "Macrophage"]
dim(assay(Macrophage))
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以下基因純手工整理。
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")
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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")
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只剩4個了，可見基因常用名和通用名經(jīng)常不一致。
M1_marker<-M1_marker[M1_marker%in%rownames(Macrophage)]
M1_marker
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只剩7個基因了。
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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從以上結(jié)果來看，p值小于0.05，確實有統(tǒng)計學意義，然而相關系數(shù)不大，考慮我們的標志物過濾太多，或者我們使用的細胞過少，至少M1與M2應該是正相關，而非負相關關系，因此，我們大致還原了文獻中的結(jié)論，若想要更加精確的結(jié)果，可以嘗試換一個巨噬細胞數(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。
