单细胞数据的二次分群

生信技能树

发布于 2024-07-05 14:45:17

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发布于 2024-07-05 14:45:17

文章被收录于专栏：生信技能树

单细胞数据的二次分群和初次分群没有本质区别，就使用subset(seu.obj, idents = "DC")获取单个细胞亚群的Seurat对象再跑常规的降维聚类分群流程即可。

0.背景知识

Seurat里的FindClusters函数设置的resolution数值越大，分群的数量就越多，但是当单细胞数量太多的时候，会遇到resolution再变大，分群的数量也不再增加的情况。一次分群分不开时就会需要二次分群。

1.示例数据

rm(list = ls())
library(Seurat)
library(dplyr)
load("../24.5.23scGSVA/seu.obj.Rdata")
p1 = DimPlot(seu.obj, reduction = "umap",label=T)+NoLegend()
p1

2.二次分群

这里以树突细胞(DC)为例进行二次分群

核心就是提取感兴趣的亚群的细胞，后面就是标准流程和可视化了，没有区别

sub.cells <- subset(seu.obj, idents = "DC")
f = "obj.Rdata"
if(!file.exists(f)){
  sub.cells = sub.cells %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData(features = rownames(.)) %>%
  RunPCA(features = VariableFeatures(.))  %>%
  FindNeighbors(dims = 1:15) %>%
  FindClusters(resolution = 0.5) %>%
  RunUMAP(dims = 1:15) 
  save(sub.cells,file = f)
}
load(f)
DimPlot(sub.cells, reduction = 'umap',label = T)+NoLegend()

3.marker基因及其可视化

sub.cells.markers <- FindAllMarkers(sub.cells, only.pos = TRUE,  
                            min.pct = 0.25, logfc.threshold = 0.25)

top10 <- sub.cells.markers %>% 
  group_by(cluster) %>% 
  top_n(n = 10, wt = avg_log2FC) %>% 
  pull(gene);top10

##  [1] "JCHAIN"  "IGKC"    "MZB1"    "PACSIN1" "WNT10A"  "MAP1A"   "VASH2"  
##  [8] "NIBAN3"  "SMPD3"   "TNFRSF4" "LYZ"     "TIMP1"   "GPAT3"   "ITGAX"  
## [15] "SAMSN1"  "OLR1"    "FPR3"    "EREG"    "FCN1"    "AKAP12"

VlnPlot(sub.cells, features = top10)

RidgePlot(sub.cells, features = top10)

FeaturePlot(sub.cells, features = top10)

DotPlot(sub.cells,features = top10)+ RotatedAxis()

DoHeatmap(sub.cells, features = top10) + NoLegend()

4.放回原来的Seurat对象里面

上面的umap图是感兴趣的单独的展示，也可以把它放回原来的seurat对象里。

sub.cells@meta.data$celltype = paste0("M",sub.cells$seurat_clusters)

seu.obj$celltype = as.character(Idents(seu.obj))
seu.obj$celltype = ifelse(seu.obj$celltype=="DC",
       sub.cells$celltype[match(colnames(seu.obj),colnames(sub.cells))],
       seu.obj$celltype) 
Idents(seu.obj) = seu.obj$celltype
p2 = DimPlot(seu.obj,label = T)+NoLegend()
p1+p2