单细胞分析流程

#install.packages("Seurat")

#install.packages("outliers")

#install.packages("pbmcapply")

#install.packages("doFuture")

#if (!requireNamespace("BiocManager", quietly = TRUE))

#    install.packages("BiocManager")

#BiocManager::install("singscore")

#BiocManager::install("GSVA")

#BiocManager::install("GSEABase")

#BiocManager::install("limma")

#install.packages("devtools")

#library(devtools)

#devtools::install_github('dviraran/SingleR')

###################################04.数据前期处理和矫正###################################

#读取数据

library(limma)

library(Seurat)

library(dplyr)

library(magrittr)

setwd("C:\\Users\\lexb4\\Desktop\\scRNA\\04-07.Seurat")             #设置工作目录

#读取文件，并对重复基因取均值

rt=read.table("geneMatrix.txt",sep="\t",header=T,check.names=F)

rt=as.matrix(rt)

rownames(rt)=rt[,1]

exp=rt[,2:ncol(rt)]

dimnames=list(rownames(exp),colnames(exp))

data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)

data=avereps(data)

#将矩阵转换为Seurat对象，并对数据进行过滤

pbmc <- CreateSeuratObject(counts = data,project = "seurat", min.cells = 3, min.features = 50, names.delim = "_",)

#使用PercentageFeatureSet函数计算线粒体基因的百分比

pbmc[["percent.mt"]] <- PercentageFeatureSet(object = pbmc, pattern = "^MT-")

pdf(file="04.featureViolin.pdf",width=10,height=6)           #保存基因特征小提琴图

VlnPlot(object = pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

dev.off()

pbmc <- subset(x = pbmc, subset = nFeature_RNA > 50 & percent.mt < 5) #对数据进行过滤

#测序深度的相关性绘图

pdf(file="04.featureCor.pdf",width=10,height=6)              #保存基因特征相关性图

plot1 <- FeatureScatter(object = pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt",pt.size=1.5)

plot2 <- FeatureScatter(object = pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA",,pt.size=1.5)

CombinePlots(plots = list(plot1, plot2))

dev.off()

#对数据进行标准化

pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", scale.factor = 10000)

#提取那些在细胞间变异系数较大的基因

pbmc <- FindVariableFeatures(object = pbmc, selection.method = "vst", nfeatures = 1500)

#输出特征方差图

top10 <- head(x = VariableFeatures(object = pbmc), 10)

pdf(file="04.featureVar.pdf",width=10,height=6)              #保存基因特征方差图

plot1 <- VariableFeaturePlot(object = pbmc)

plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)

CombinePlots(plots = list(plot1, plot2))

dev.off()

###################################05.PCA主成分分析###################################

##PCA分析

pbmc=ScaleData(pbmc)                     #PCA降维之前的标准预处理步骤

pbmc=RunPCA(object= pbmc,npcs = 20,pc.genes=VariableFeatures(object = pbmc))     #PCA分析

#绘制每个PCA成分的相关基因

pdf(file="05.pcaGene.pdf",width=10,height=8)

VizDimLoadings(object = pbmc, dims = 1:4, reduction = "pca",nfeatures = 20)

dev.off()

#主成分分析图形

pdf(file="05.PCA.pdf",width=6.5,height=6)

DimPlot(object = pbmc, reduction = "pca")

dev.off()

#主成分分析热图

pdf(file="05.pcaHeatmap.pdf",width=10,height=8)

DimHeatmap(object = pbmc, dims = 1:4, cells = 500, balanced = TRUE,nfeatures = 30,ncol=2)

dev.off()

#每个PC的p值分布和均匀分布

pbmc <- JackStraw(object = pbmc, num.replicate = 100)

pbmc <- ScoreJackStraw(object = pbmc, dims = 1:20)

pdf(file="05.pcaJackStraw.pdf",width=8,height=6)

JackStrawPlot(object = pbmc, dims = 1:20)

dev.off()

###################################06.TSNE聚类分析和marker基因###################################

##TSNE聚类分析

pcSelect=20

pbmc <- FindNeighbors(object = pbmc, dims = 1:pcSelect) #计算邻接距离

pbmc <- FindClusters(object = pbmc, resolution = 0.5) #对细胞分组,优化标准模块化

pbmc <- RunTSNE(object = pbmc, dims = 1:pcSelect) #TSNE聚类

pdf(file="06.TSNE.pdf",width=6.5,height=6)

TSNEPlot(object = pbmc, do.label = TRUE, pt.size = 2, label = TRUE)    #TSNE可视化

dev.off()

write.table(pbmc$seurat_clusters,file="06.tsneCluster.txt",quote=F,sep="\t",col.names=F)

##寻找差异表达的特征

logFCfilter=0.5

adjPvalFilter=0.05

pbmc.markers <- FindAllMarkers(object = pbmc,

only.pos = FALSE,

min.pct = 0.25,

logfc.threshold = logFCfilter)

sig.markers=pbmc.markers[(abs(as.numeric(as.vector(pbmc.markers$avg_logFC)))>logFCfilter & as.numeric(as.vector(pbmc.markers$p_val_adj))write.table(sig.markers,file="06.markers.xls",sep="\t",row.names=F,quote=F)

top10 <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)

#绘制marker在各个cluster的热图

pdf(file="06.tsneHeatmap.pdf",width=12,height=9)

DoHeatmap(object = pbmc, features = top10$gene) + NoLegend()

dev.off()

#绘制marker的小提琴图

pdf(file="06.markerViolin.pdf",width=10,height=6)

VlnPlot(object = pbmc, features = c("IGLL5", "MBOAT1"))

dev.off()

#绘制marker在各个cluster的散点图

pdf(file="06.markerScatter.pdf",width=10,height=6)

FeaturePlot(object = pbmc, features = c("IGLL5", "MBOAT1"),cols = c("green", "red"))

dev.off()

#绘制marker在各个cluster的气泡图

pdf(file="06.markerBubble.pdf",width=12,height=6)

cluster10Marker=c("MBOAT1", "NFIB", "TRPS1", "SOX4", "CNN3", "PIM2", "MZB1", "MS4A1", "ELK2AP", "IGLL5")

DotPlot(object = pbmc, features = cluster10Marker)

dev.off()

###################################07.注释细胞类型###################################

library(SingleR)

counts<-pbmc@assays$RNA@counts

clusters<[e-mail]$seurat_clusters

ann=[e-mail]$orig.ident

singler = CreateSinglerObject(counts, annot = ann, "pbmc", min.genes = 0,

species = "Human", citation = "",

ref.list = list(), normalize.gene.length = F, variable.genes = "de",

fine.tune = F, do.signatures = T, clusters = clusters, do.main.types = T,

reduce.file.size = T, numCores = 1)

singler$seurat = pbmc

singler$meta.data$xy = pbmc@reductions$[e-mail]

clusterAnn=singler$singler[[2]]$SingleR.clusters.main$labels

write.table(clusterAnn,file="07.clusterAnn.txt",quote=F,sep="\t",col.names=F)

write.table(singler$other,file="07.cellAnn.txt",quote=F,sep="\t",col.names=F)