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GSE128033获得seurat对象Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary ...

GSE以128033为例，处理多个大数据集seurat首先创建多个对象seurat对象 再变为一个list 通过findanchor整合合并 最终获得超大数据集合 Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis

https://satijalab.org/seurat/archive/v3.0/integration.html

#教程地址 #https://cloud.tencent.com/developer/article/1697249   #https://bioconductor.org/packages/release/data/experiment/vignettes/scRNAseq/inst/doc/scRNAseq.html #https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MzI1Njk4ODE0MQ==&action=getalbum&album_id=1326587538303434752&scene=173&from_msgid=2247484689&from_itemidx=1&count=3&nolastread=1#wechat_redirect rm(list = ls())  Sys.setenv(R_MAX_NUM_DLLS=999) options(stringsAsFactors = F) ·```

########################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################duoge数据 准备原始分析数据 先手动下载 下载文件到浏览器自己的文件夹，然后解压 #https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi

一个样本，一个文件。需要分成文件夹命名

对geo解压下载的文件 获取以下文件 处理上述文件 分类。需要分成文件夹并重命名

 getwd() path="D:/yll/inflammatory-singlecells/GSE128033/GSE128033" #空间转录组 dir.create(path) setwd(path) getwd()

工作目录设置错误时候，出现一下结果 因此，更改工作目录 

fs=list.files('./','^GSM')  #得到当前目录的一切GSM开头的文件名称 fs

自行下载GSE128033数据集的GSE128033压缩包并解压哦，这样上面的代码就可以运行了

然后获取样本信息，因为它是批量的，所以下面的代码可能不容易理解，需要掌握R语言

library(stringr) samples=str_split(fs,'_',simplify = T)[,1] #取出可分组的样本名 得到样本分组 samples 

getwd() ###注意workshop决定下一句话是否成功的位置！ setwd("D:/yll/inflammatory-singlecells/GSE128033/") 

lapply(unique(samples),function(x){   #x=unique(samples)[1]   y=fs[grepl(x,fs)]   folder=paste0("GSE128033/", str_split(y[1],'_',simplify = T)[,1])   dir.create(folder,recursive = T)   #创建每个样本的子文件夹      file.rename(paste0("GSE128033/",y[1]),file.path(folder,"barcodes.tsv.gz"))   #重命名文件，移动到相应的子文件夹   file.rename(paste0("GSE128033/",y[2]),file.path(folder,"features.tsv.gz"))   file.rename(paste0("GSE128033/",y[3]),file.path(folder,"matrix.mtx.gz")) })

成功移动文件

如下： 每个文件夹下的命名如下： 

getwd() samples=list.files("GSE128033/") samples# 是多个文件夹的名称 

samples=list.files("GSE128033/") %>% grep(pattern = "gz",invert = TRUE,value = TRUE) samples# 是两个文件夹的名字 
2#创建Seurat对象  library(Seurat)   getwd() # 循环读取两个文件夹下的10x的的3个文件 sceList = lapply(samples,function(po){
  #pro="GSM3660641"
  folder=file.path("GSE128033/",pro)
  CreateSeuratObject(counts = Read10X(folder),  #此处一定要加上筛选条件
                     project = pro ,min.cells=3,min.features=200)
  ##print(paste0("进行到第",pro,"个样本")) 不可以打印这一排
})


for (i in 1:length(sceList)) {#计算线粒体比例
  sceList[[i]][["percent.mt"]]=PercentageFeatureSet(sceList[[i]],pattern = "^MT-")
}

但是是不是应该进行质量控制 再进行下一步呢 否则，现在不进行质量控制的话最后的结果就会变成下面这样

此处可以写个循环来对list之内的每个seurat对象进行质控，那么双细胞要不要也去除呢

# 较接近原文过滤后的数量2149，但感觉条件有点宽松了
# 网上看了相关教程，一般ERCC占比不高于10%！
sum(scRNA$percent.ERCC< 10)   #就只剩下460个cell，明显低于文献中的数量
pctERCC=40
scRNA <- subset(scRNA, subset = percent.ERCC < pctERCC)
dim(scRNA)
# >20047  2142   #原文为19752  2149
#质量控制
features = c("nFeature_RNA","nCount_RNA","percent.mt"))
                  >200                                                 <20


# Quality Control figure
VlnPlot(norandipf.integrated,
        features = c("nFeature_RNA","nCount_RNA","percent.mt"))
head(norandipf.integrated@meta.data)
p1=FeatureScatter(norandipf.integrated,feature1 = "nCount_RNA",feature2 = "percent.mt")
p2=FeatureScatter(norandipf.integrated,feature1 = "nCount_RNA",feature2 = "nFeature_RNA")
p1+p2

sceList DefaultAssay(sceList)

getwd() save(sceList,file=“D:/yll/inflammatory-singlecells/GSE128033/scelist.rdata” )

sceList[[1]]@project.name

for (i in 1:length(sceList)) {#查看list之内pro与index的对应关系 #i=1 print(paste0(sceList[[i]]@project.name,paste0(“:这是第”,i,“个”))) }

#取自己感兴趣的集合
mysamples=c("GSM3660645","GSM3660646","GSM3660647","GSM3660648",
                 "GSM3660651","GSM3660652",
                 "GSM3660654","GSM3660655",
                 "GSM3660657","GSM3660658")

norandipf.list=list()
for (i in 1:length(sceList)) {#取出想要的seurat对象，并给列表也命名了
  #i=1
  if (sceList[[i]]@project.name %in% mysamples) {
    norandipf.list[[paste(sceList[[i]]@project.name)]]=sceList[[i]]
  }
  print(paste0(sceList[[i]]@project.name,paste0(":这是第",i,"个")))
}
names(norandipf.list)

head(norandipf.list[[1]]@meta.data)


##这一步就没必要了，因为初始读入seurat对象时候已经筛选过了
for (i in names(norandipf.list)) {
  #i="GSM3660645"
  norandipf.list[[i]]=subset(norandipf.list[[i]],#过滤掉基因表达为0的细胞
                             subset = nCount_RNA> 0 & nFeature_RNA>0)
}

https://satijalab.org/seurat/archive/v3.0/integration.html

# normalize and identify variable features for each dataset independently
norandipf.list<- lapply(X = norandipf.list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})


'''
#此处省略该步骤sct转化  比较耗时 因为是大数据，此处为了下一步的整合分析
#memory.limit(size = 35000) 
for (i in names(norandipf.list)) {
  #i="GSM3660645"
  norandipf.list[[i]]=SCTransform(norandipf.list[[i]],verbose = FALSE)
}
'''

#选择锚定基因
DefaultAssay(norandipf.list[[1]])
features=SelectIntegrationFeatures(object.list = norandipf.list)

norandipf.list <- lapply(X = norandipf.list, FUN = function(x) {#注意等号=和<-的区别
  x <- ScaleData(x, features = features, verbose = FALSE)
  x <- RunPCA(x, features = features, verbose = FALSE)
})

'''
#预整合 此步骤省略
norandipf.list=PrepSCTIntegration(object.list = norandipf.list,
                                  anchor.features =norandipf.features )

'''


#在找锚点合并之前，需要把每个seurat对象的细胞名改变成唯一
getwd()
#save(norandipf.list,file = "G:/silicosis/geo/GSE128033_SnRNAseq-idiopathic pulmonary fibrosis/norandipf.list.rds")
Idents(norandipf.list[[1]])
names(norandipf.list[[1]])
#new_obj <- RenameCells(obj, new.names=gsub("-1", "_1", colnames(obj)))
for (i in names(norandipf.list)) {
  norandipf.list[[i]]=RenameCells(norandipf.list[[i]],
                                  new.names =sub(pattern = "1",
                                                 replacement =str_split(pattern = "0",
                                                                        names(norandipf.list)[str_detect(names(norandipf.list),pattern = i)],
                                                                        simplify = TRUE)[2],
                                                 x=colnames(norandipf.list[[i]])) )
}



#选择参考数据集，这里选择51  52 因为他俩细胞数最多  这里使用的reference是个向量！！！而不是seurat对象！
reference_dataset=which(names(norandipf.list)==c("GSM3660651","GSM3660652"))
#reference_dataset=c(norandipf.list[["GSM3660651"]],norandipf.list[["GSM3660652"]])

#找锚点
norandipf.anchors=FindIntegrationAnchors(object.list = norandipf.list,
                                         reduction = "rpca",
                                         anchor.features = features,
                                         reference =reference_dataset )#c(5,6)
#整合 
DefaultAssay(norandipf.list[[1]]) #[1] "RNA"
norandipf.integrated=IntegrateData(anchorset = norandipf.anchors,
                                   dims = 1:30) %>%
  ScaleData(verbose=FALSE) %>%
  RunPCA(verbose=FALSE) %>%
  RunTSNE(verbose=FALSE)%>%
  RunUMAP(dims=1:30)

DefaultAssay(norandipf.list[[1]]) DefaultAssay(norandipf.integrated) #Perform an integrated analysis #Now we can run a single integrated analysis on all cells DefaultAssay(norandipf.integrated) <- “integrated”

norandipf.integrated<-FindNeighbors(norandipf.integrated,reduction = "pca", dims = 1:30) %>%
                      FindClusters()

markers=FindAllMarkers(norandipf.integrated,only.pos = TRUE,min.pct = 0.3)




getwd()
#save(norandipf.integrated,file = "D:/yll/inflammatory-singlecells/GSE128033/Nor_and_IPF.Rdata")
load("D:/yll/inflammatory-singlecells/GSE128033/Nor_and_IPF.Rdata")

DimPlot(norandipf.integrated,group.by = "orig.ident")


DefaultAssay(norandipf.integrated)

norandipf.integrated<-FindNeighbors(norandipf.integrated,reduction = "pca", dims = 1:30) %>%
                      FindClusters()

markers=FindAllMarkers(norandipf.integrated,only.pos = TRUE,min.pct = 0.3)
DT::datatable(markers)

Idents(norandipf.integrated)

norandipf.integrated=RenameIdents(norandipf.integrated, “0”=“Club”,“11”=“AT1”, “16”=‘AT1’, ‘18’=‘AT2’,‘9’=‘ciliated cells’, ‘8’=‘Fibroblasts’,‘12’=‘Fibroblasts’, ‘14’=‘smooth muscle cells’, ‘5’=‘Endothelial cells’, ‘10’=‘other_Mes’,‘22’=‘other_Mes’,‘24’=‘other_Mes’,‘28’=‘other_Mes’, ‘23’=‘Lymphatic endothelial cells’, ‘1’=‘Macrophages’,‘3’=‘Macrophages’,‘7’=‘Macrophages’, ‘25’=‘dc’,‘19’=‘dc’, ‘6’=‘T’,‘13’=‘T’, ‘21’=‘B’,‘27’=‘other_B’, ‘17’=‘plasma’)

table(Idents(norandipf.integrated),norandipf.integrated$orig.ident)

3#过滤质控

#raw_sce=get(load(file = 'sce.big.merge.ls_12.Rdata'))
#线粒体基因
raw_sce=sce.big
rownames(raw_sce)[grepl('^mt-',rownames(raw_sce),ignore.case = T)]

#核糖体蛋白基因
rownames(raw_sce)[grepl('^Rp[sl]',rownames(raw_sce),ignore.case = T)]


#计算上述两种基因在所有细胞中的比例
raw_sce[["percent.mt"]] <- PercentageFeatureSet(raw_sce, pattern = "^MT-")

rb.genes <- rownames(raw_sce)[grep("^RP[SL]",rownames(raw_sce))]
C<-GetAssayData(object = raw_sce, slot = "counts") #计算核糖体比例时候使用
percent.ribo <- Matrix::colSums(C[rb.genes,])/Matrix::colSums(C)*100
raw_sce <- AddMetaData(raw_sce, percent.ribo, col.name = "percent.ribo")

plot1 <- FeatureScatter(raw_sce, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(raw_sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
CombinePlots(plots = list(plot1, plot2))

VlnPlot(raw_sce, features = c("percent.ribo", "percent.mt"), ncol = 2)
VlnPlot(raw_sce, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
VlnPlot(raw_sce, features = c("percent.ribo", "nCount_RNA"), ncol = 2)

raw_sce #9644个
#按照三个指标过滤细胞
raw_sce1 <- subset(raw_sce, subset = nFeature_RNA > 200 & nCount_RNA > 1000 & percent.mt < 20)
raw_sce1 #7915个
dim(raw_sce1)

4#降维聚类
sce=raw_sce1
#counts归一化
sce <- NormalizeData(sce, normalization.method =  "LogNormalize",
                     scale.factor = 10000)
GetAssay(sce,assay = "RNA")

#前2000个高变feature RNA
sce <- FindVariableFeatures(sce,
                            selection.method = "vst", nfeatures = 2000)
# 中心化，为下一步PCA做准备
sce <- ScaleData(sce)
sce <- RunPCA(object = sce, pc.genes = VariableFeatures(sce))
#看看前12个主成分
DimHeatmap(sce, dims = 1:12, cells = 100, balanced = TRUE)
#判断最终选取的主成分数，这里我判断17个
ElbowPlot(sce)
table(sce$orig.ident)
head(colnames(sce))
library(dplyr)
unlist(str_split(colnames(sce),"_")) %>% grep(pattern = "GSM5015",value = TRUE)
table(unlist(str_split(colnames(sce),"_")) %>% grep(pattern = "GSM5015",value = TRUE))

grep("^GSM5015042",colnames(sce))
head(colnames(sce))
sce$stim<-c(rep("GSM5015042",length(grep("GSM5015042",colnames(sce)))), #顺序是按照合并的顺序来的
            rep("GSM5015043",length(grep("GSM5015043",colnames(sce)))),
            rep("GSM5015044",length(grep("GSM5015044",colnames(sce)))))
table(sce$stim)
Idents(sce)

subset_data=sce
library('harmony')
subset_data <- subset_data %>% RunHarmony("stim", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(subset_data, 'harmony') 
#######################cluster
dims = 1:30
subset_data <- subset_data %>% 
  RunUMAP(reduction = "harmony", dims = dims) %>% 
  RunTSNE(reduction = "harmony", dims = dims) %>% 
  FindNeighbors(reduction = "harmony", dims = dims)

save(subset_data, file="mouse_lung_stim.rds")

load(file ="mouse_lung_stim.rds" )
subset_data@meta.data$stim
names(subset_data@meta.data)[6]
names(subset_data@meta.data)[6]<-"stim"
table(subset_data)

gsub('["]','',paste0(myfilename,".rds")) # 删除字符串a中的双引号
getwd()
file="G:/silicosis/geo/GSE164621_RNA-seq_wildtype_mouse_lung"
#"default"默认分辨率
myfilename="mouse_lung_stim"
library(xlsx)
for (r in c("_default_")) {
  load(paste(file, gsub('["]','',paste0(myfilename,".rds")), sep="/"))
  dir.create(paste(file,r,sep="/"))
  setwd(paste(file,r,sep="/"))
  subset_data <- FindClusters(subset_data)
  save(subset_data, file=paste0(myfilename,"_r", r, ".rds"))
  subset_data <- ScaleData(subset_data, verbose = FALSE, features = rownames(subset_data))
  pdf(paste0("1_",myfilename,"_cluster_TSNE.pdf"))
  p = DimPlot(subset_data, reduction = "tsne", label = TRUE, pt.size = 1,label.size = 6)
  print(p)
  dev.off()
  pdf(paste0("1_",myfilename,"_cluster_UMAP.pdf"))
  p = DimPlot(subset_data, reduction = "umap", label = TRUE, pt.size = 1,label.size = 6)
  print(p)
  dev.off()
  for (i in c("umap","tsne")) {
    pdf(paste0("2_",i,"_",myfilename,"_split.pdf"))
    p = DimPlot(subset_data, reduction = i, 
                split.by = "stim",label = TRUE, pt.size = 1,label.size = 6)
    print(p)
    dev.off() 
  }
  stat = cbind(table(Idents(subset_data), subset_data$stim), All = rowSums(table(Idents(subset_data), subset_data$stim)))
  stat = rbind(stat, All = colSums(stat))
  write.xlsx(stat, paste0("2_",myfilename,"_cluster_stat.xlsx"), col.names=T, row.names=T)
  ######################## find marker
  markers <- FindAllMarkers(subset_data,  min.pct = 0.25, logfc.threshold = 0.25, only.pos=T)
  write.xlsx(markers,paste0(r,"_3_",myfilename,"_cluster_markers.xlsx"), col.names=T,row.names=T)
  
}
head(markers)
filetered_marker=markers %>% group_by(cluster) %>%filter(p_val_adj==0)
head(filetered_marker)
table(filetered_marker$cluster)



load("G:/silicosis/geo/GSE164621_RNA-seq_wildtype_mouse_lung/_default_/mouse_lung_stim_r_default_.rds")
###########
#######
######
##########
######


# 分成25个cluster

#tSNE可视化
set.seed(123)
sce <- RunTSNE(object = sce, dims = 1:18, do.fast = TRUE)
DimPlot(sce,reduction = "tsne",label=T)
dim(sce)
# 在两个样本里25个cluster的分布情况
table(sce@meta.data$seurat_clusters,sce@meta.data$orig.ident)
DimPlot(sce,reduction = "tsne",label=T,split.by ='orig.ident')

#save(sce,file='merge_for_tSNE.pos.Rdata')
load(file="merge_for_tSNE.pos.Rdata")

#再尝试用ggplot2可视化上图
phe=data.frame(cell=rownames(sce@meta.data),
               cluster =sce@meta.data$seurat_clusters)
phe
tsne_pos=Embeddings(sce,'tsne')
dat=cbind(tsne_pos,phe)
head(dat)

library(ggplot2)
p=ggplot(dat,aes(x=tSNE_1,y=tSNE_2,color=cluster))+geom_point(size=0.95)
p=p+stat_ellipse(data=dat,aes(x=tSNE_1,y=tSNE_2,fill=cluster,color=cluster),
                 geom = "polygon",alpha=0.2,level=0.9,type="t",linetype =2,show.legend = F)+coord_fixed()
print(p)


theme= theme(panel.grid =element_blank()) +   ## 删去网格
  theme(panel.border = element_blank(),panel.background = element_blank()) +   ## 删去外层边框
  theme(axis.line = element_line(size=1, colour = "black"))
p+theme+guides(colour = guide_legend(override.aes = list(size=5)))


#寻找每个cluster的高变代表基因，并选取前5个，进行可视化
table(sce@meta.data$seurat_clusters)

p <- list()
for( i in unique(sce@meta.data$seurat_clusters) ){
  markers_df <- FindMarkers(object = sce, ident.1 = i, min.pct = 0.25)
  print(x = head(markers_df))
  markers_genes =  rownames(head(x = markers_df, n = 4))
  p1 <- VlnPlot(object = sce, features =markers_genes,log =T,ncol = 2)
  p[[i]][[1]] <- p1
  p2 <- FeaturePlot(object = sce, features=markers_genes,ncol = 2)
  p[[i]][[2]] <- p2
}
p[[1]][[2]]
p[[2]][[1]]



#查阅所有的marker基因，可用于人工注释cell type
sce.markers <- FindAllMarkers(object = sce, only.pos = TRUE, min.pct = 0.25,
                              thresh.use = 0.25)
DT::datatable(sce.markers)

head(sce.markers)
#热图可视化每个cluster的marker基因表达差异
library(dplyr)
top10 <- sce.markers %>% group_by(cluster) %>% top_n(10, avg_log2FC)
DoHeatmap(sce,top10$gene,size=3)
#save(sce,sce.markers,file = 'sce.output.merge.ls_12.Rdata')
#load(file = "sce.output.merge.ls_12.Rdata")

DotPlot(sce,features = c("Hamp","Slc40a1","Mybphl","Fmo3","Dll4","Vegfa"))
FeaturePlot(sce,features = c("Hamp","Slc40a1","Mybphl","Fmo3"))
DotPlot(sce,features = c("Mybphl","Myl7","Myl4"))
makers14<-FindMarkers(sce,ident.1 = 14,
                      logfc.threshold = 0.25,
                      only.pos = TRUE,min.pct = 0.25)
DT::datatable(makers14)


DotPlot(sce,features = c("Acta2","Myl7","Mybphl","Aldh1b1","Got1","Cmya5","Srl","Ttn"))



5#clusters细胞类型注释
#我刚开始用SingleR是在19年的年底，现在使用方法可能有一些变化了。因为Single在注释细胞的过程中，会用到一些参考数据集，我们可以把这些数据集保存下来，下一次使用就可以直接加载，省去了重新下载参考集的时间。
library(SingleR)
sce_for_SingleR <- GetAssayData(sce, slot="data")
clusters=sce@meta.data$seurat_clusters
head(clusters)
#BiocManager::install('celldex')
library('celldex')

mouseImmu <- ImmGenData()
#接下来是注释步骤，在这一步里，我只用了main大类注释，还有一个fine小类注释，我就不演示了，因为我觉得小类注释不太准
#注意使用main或者fine参数
pred.mouseImmu <- SingleR(test = sce_for_SingleR, ref = mouseImmu, labels = mouseImmu$label.main,
                          method = "cluster", clusters = clusters, 
                          assay.type.test = "logcounts", assay.type.ref = "logcounts")

mouseRNA <- MouseRNAseqData()
pred.mouseRNA <- SingleR(test = sce_for_SingleR, ref = mouseRNA, labels = mouseRNA$label.main ,
                         method = "cluster", clusters = clusters,  # #因为样本主要为免疫细胞（而不是全部细胞），因此设置为label.fine
                         assay.type.test = "logcounts", assay.type.ref = "logcounts")

cellType=data.frame(ClusterID=levels(sce@meta.data$seurat_clusters),
                    mouseImmu=pred.mouseImmu$labels,
                    mouseRNA=pred.mouseRNA$labels )
head(cellType)
sce@meta.data$singleR=cellType[match(clusters,cellType$ClusterID),'mouseRNA']


pro='first_anno'
DimPlot(sce,reduction = "tsne",label=T, group.by = 'singleR')
ggplot2::ggsave(filename = paste0(pro,'_tSNE_singleR.pdf'))
DimPlot(sce,reduction = "tsne",label=T,split.by ='orig.ident',group.by = 'singleR')
ggplot2::ggsave(filename = paste0(pro,'_tSNE_singleR_by_orig.ident.pdf'))

save(sce,file = 'last_sce.Rdata') 

table(pred.mouseRNA$labels)
plotScoreHeatmap(pred.mouseRNA)

#tSNE可视化
celltype = data.frame(ClusterID=rownames(pred.mouseRNA), celltype=pred.mouseRNA$labels, stringsAsFactors = F)
#如下为sce对象注释细胞cluster鉴定结果。

sce@meta.data$celltype = "NA"
#先新增列celltype，值均为NA，然后利用下一行代码循环填充
for(i in 1:nrow(celltype)){
  sce@meta.data[which(sce@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}

DimPlot(sce, group.by="celltype", label=T, label.size=5, reduction='tsne')

#利用ggplot2再可视化上图
phe=data.frame(cell=rownames(sce@meta.data),
               cluster =clusters,
               celltype=sce@meta.data$celltype)
tsne_pos=Embeddings(sce,'tsne')
dat=cbind(tsne_pos,phe)
head(dat)

p=ggplot(dat,aes(x=tSNE_1,y=tSNE_2,color=celltype))+geom_point(size=0.95)
p=p+stat_ellipse(data=dat,aes(x=tSNE_1,y=tSNE_2,fill=celltype,color=celltype),
                 geom = "polygon",alpha=0.2,level=0.9,type="t",linetype = 2,show.legend = F)+coord_fixed()
print(p)

theme= theme(panel.grid =element_blank()) +   ## 删去网格
  theme(panel.border = element_blank(),panel.background = element_blank()) +   ## 删去外层边框
  theme(axis.line = element_line(size=1, colour = "black"))
p+theme+guides(colour = guide_legend(override.aes = list(size=5)))

标签： 对射型光电传感器gse6

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