上次的视频中已花费大量时间讲解过单样本分析的基本流程，所以这节课的学习需要有上节课的基础，希望大家按顺序观看。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。由于测试数据比较特殊，并没有展示出去批次的精妙之处，留一个悬念给大家吧，可以用自己的数据集测试一下。

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操作，建议每个视频至少看三遍）

###########单纯的merge#################
  library(Seurat)
  library(multtest)
  library(dplyr)
  library(ggplot2)
  library(patchwork)
  
##########准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$C57
AS1 <- ifnb.list$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")
pbmc
head(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)

#需要注意的是：上面的整合步骤相对于harmony整合方法，对于较大的数据集（几万个细胞）
#非常消耗内存和时间，大约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以下这样子），
#会报错，这时建议用第二种整合方法

DefaultAssay(testAB.integrated) <- "integrated"

# # Run the standard workflow for visualization and clustering
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)
p1<- DimPlot(testAB.integrated,label = T,split.by = 'group')#integrated

DefaultAssay(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 速度快、内存少################
if(!require(harmony))devtools::install_github("immunogenomics/harmony")
test.seu <- pbmc
test.seu <-  test.seu%>%
  Seurat::NormalizeData() %>%
  FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>% 
  ScaleData()
test.seu <- RunPCA(test.seu, npcs = 50, verbose = FALSE)


#####run 到PCA再进行harmony，相当于降维########
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）——单样本分析

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）——细胞类型注释

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

欢迎关注同名公众号~