R package：Seurat 计算细胞周期并可视化

计算细胞周期，需要先对数据进行标准化

pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", scale.factor = 10000) 
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
pbmc<- CellCycleScoring(pbmc, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
head(pbmc@meta.data)

CellCycleScoring函数回自动计算细胞周期并储存在pbmc@meta.data

#                         orig.ident nCount_RNA nFeature_RNA     S.Score     G2M.Score Phase old.ident
#P1TLH_AAACCTGAGCAGCCTC_1      P1TLH   1633.605         1436  0.07536769  0.0002187129     S     P1TLH
#P1TLH_AAACCTGTCCTCATTA_1      P1TLH   1439.645         2533 -0.02311835  0.0107256067   G2M     P1TLH
#P1TLH_AAACCTGTCTAAGCCA_1      P1TLH   1517.970         1030  0.03511153 -0.0900560635     S     P1TLH
#P1TLH_AAACGGGAGTAGGCCA_1      P1TLH   1817.202         1808 -0.04005376 -0.0817285494    G1     P1TLH
#P1TLH_AAACGGGGTTCGGGCT_1      P1TLH   1617.453         1428  0.12846110 -0.0080281217     S     P1TLH
#P1TLH_AAAGCAACAGTAAGAT_1      P1TLH   2159.746          786  0.02397663 -0.0780498558     S     P1TLH

library(ggplot2)

p <- ggplot(pbmc@meta.data,aes(G2M.Score,S.Score,color = Phase))+
  geom_point()
p

Fig1.png

library(scales)
p <- ggplot(pbmc@meta.data,aes(x=old.ident,fill= Phase))+
  geom_bar(position ="fill")+
  xlab(NULL)+ylab("PERCENT")+
  scale_y_continuous(labels = percent,breaks=seq(0,1,by=0.2))
p

Fig2.png

p <- ggplot(pbmc@meta.data,aes(x=old.ident,fill= Phase))+
  geom_bar(position ="dodge")+xlab(NULL)
p

Fig3.png

p <- ggplot(pbmc@meta.data,aes(x=orig.ident,fill= Phase))+
  geom_bar(position ="stack")+xlab(NULL)+ylab("Cell")
p

Fig4.png