前次的视频中已泯灭大量时间解说过单样天职析的根本流程，以是这节课的学习必要有上节课的底子，盼望各人按次序观看。此次的内容较简单、篇幅也较小，代码与视频请看下文，测试数据集与代码存于文末链接之中。由于测试数据比力特殊，并没有展示出去批次的精妙之处，留一个牵挂给各人吧，可以用本身的数据集测试一下。
手把手教你做单细胞测序（四）——多样本整合
（B站同步播出，先看一遍视频再跟着代码一起利用，发起每个视频至少看三遍）
###########单纯的merge################# library(Seurat) library(multtest) library(dplyr) library(ggplot2) library(patchwork) ##########预备用于拆分的数据集###########pbmc <- subset(pbmc, downsample = 50)ifnb <- readRDS('pbmcrenamed.rds')ifnb.list <- SplitObject(ifnb, split.by = "group")C57 <- ifnb.list$C57AS1 <- ifnb.list$AS1######简单merge######## #不具有去批次效应功能pbmc <- merge(C57, y = c(AS1), add.cell.ids = c("C57", "AS1"), project = "ALL")pbmchead(colnames(pbmc))unique(sapply(X = strsplit(colnames(pbmc), split = "_"), FUN = "[", 1))table(pbmc$orig.ident)##############anchor###############library(Seurat)library(tidyverse)### testA ----myfunction1 <- function(testA.seu){ testA.seu <- NormalizeData(testA.seu, normalization.method = "LogNormalize", scale.factor = 10000) testA.seu <- FindVariableFeatures(testA.seu, selection.method = "vst", nfeatures = 2000) return(testA.seu)}C57 <- myfunction1(C57)AS1 <- myfunction1(AS1)### Integration ----testAB.anchors <- FindIntegrationAnchors(object.list = list(C57,AS1), dims = 1:20)testAB.integrated <- IntegrateData(anchorset = testAB.anchors, dims = 1:20)#必要注意的是：上面的整合步调相对于harmony整合方法，对于较大的数据集（几万个细胞）#非常斲丧内存和时间，约莫9G的数据32G的内存就已经无法运行；#当存在某一个Seurat对象细胞数很少（印象中200以下如许子），#会报错，这时发起用第二种整合方法DefaultAssay(testAB.integrated) <- "integrated"# # Run the standard workflow for visualization and clusteringtestAB.integrated <- ScaleData(testAB.integrated, features = rownames(testAB.integrated))testAB.integrated <- RunPCA(testAB.integrated, npcs = 50, verbose = FALSE)testAB.integrated <- FindNeighbors(testAB.integrated, dims = 1:30)testAB.integrated <- FindClusters(testAB.integrated, resolution = 0.5)testAB.integrated <- RunUMAP(testAB.integrated, dims = 1:30)testAB.integrated <- RunTSNE(testAB.integrated, dims = 1:30)p1<- DimPlot(testAB.integrated,label = T,split.by = 'group')#integratedDefaultAssay(testAB.integrated) <- "RNA"testAB.integrated <- ScaleData(testAB.integrated, features = rownames(testAB.integrated))testAB.integrated <- RunPCA(testAB.integrated, npcs = 50, verbose = FALSE)testAB.integrated <- FindNeighbors(testAB.integrated, dims = 1:30)testAB.integrated <- FindClusters(testAB.integrated, resolution = 0.5)testAB.integrated <- RunUMAP(testAB.integrated, dims = 1:30)testAB.integrated <- RunTSNE(testAB.integrated, dims = 1:30)p2 <- DimPlot(testAB.integrated,label = T,split.by = 'group')p1|p2###########harmony 速率快、内存少################if(!require(harmony))devtools::install_github("immunogenomics/harmony")test.seu <- pbmctest.seu <- test.seu%>% Seurat::NormalizeData() %>% FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>% ScaleData()test.seu <- RunPCA(test.seu, npcs = 50, verbose = FALSE)#####run 到PCA再举行harmony，相称于降维########test.seu=test.seu %>% RunHarmony("group", plot_convergence = TRUE)test.seu <- test.seu %>% RunUMAP(reduction = "harmony", dims = 1:30) %>% FindNeighbors(reduction = "harmony", dims = 1:30) %>% FindClusters(resolution = 0.5) %>% identity()test.seu <- test.seu %>% RunTSNE(reduction = "harmony", dims = 1:30) p3 <- DimPlot(test.seu, reduction = "tsne", group.by = "group", pt.size=0.5)+theme( axis.line = element_blank(), axis.ticks = element_blank(),axis.text = element_blank())p4 <- DimPlot(test.seu, reduction = "tsne", group.by = "ident", pt.size=0.5, label = TRUE,repel = TRUE)+theme( axis.line = element_blank(), axis.ticks = element_blank(),axis.text = element_blank())p3|p4本系列其他课程
手把手教你做单细胞测序数据分析（一）——绪论
手把手教你做单细胞测序数据分析（二）——各类输入文件读取
手把手教你做单细胞测序数据分析（三）——单样天职析
手把手教你做单细胞测序数据分析（四）——多样本整合
手把手教你做单细胞测序数据分析（五）——细胞范例表明
手把手教你做单细胞测序数据分析（六）——组间差异分析及可视化
手把手教你做单细胞测序数据分析（七）——基因集富集分析
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