Github代码文献复现之卵巢和子宫内膜癌(六)|| 单细胞第一次注释

生信技能树

发布于 2025-03-17 16:59:20

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我们的新专辑《Github带有全套代码分享的文献复现2025》开启后受到大家的热烈喜爱，里面学习的文章为：《A multi-omic single-cell landscape of human gynecologic malignancies》，前面的学习笔记如下：

文献的注释结果

作者文献中的单细胞部分的细胞注释结果如下：总共有75523个细胞。

malignant clusters 鉴定策略：

使用的 the U.S. Food and Drug Administration (FDA)-approved biomarkers MUC16/CA125 and WFDC2/HE4 to identify EC and OC cancer clusters
Expression of KIT/CD117 was used to identify GIST cancer clusters
结合inferCNV：nferred copy number variation (CNV) was used to help identify OC and GIST but not EC, as the disease rarely exhibits CNV

文章的附图：

第一次尝试注释

作者的注释策略也比较复杂，我们先来看看使用常规的细胞已知marker尝试进行第一次注释。图中的细胞类型名称有：

Ovarian cancer：WFDC2/HE4
GIST：KIT/CD117
Endometrial cancer：MUC16/CA125
NK/T cell：CD3D,CD3E,CD3G,CD2,KLRD1,KLRF1,NKG7
Mast cell: TPSAB1,TPSB2
Macrophage: CD163,CD68
B cell: CD19,MS4A1,CD79A,IGHG1
Smooth muscle: ACTA2,MYH11
Endothelial: RAMP2, PECAM1
Fibroblast: DCN, LUM,COL1A2
Cell Cycle: TOP2A, MKI67
Immu: PTPRC

先来看看没有进行harmony的注释结果。本次尝试使用res0.5的结果进行绘图，画一张markers气泡图：

rm(list=ls())
library(Seurat)
library(ggplot2)
library(SCP) # https://zhanghao-njmu.github.io/SCP/index.html
# https://scillus.netlify.app/vignettes/plotting
library(Scillus) # https://mp.weixin.qq.com/s/Z69GmXORqKczTsMQ68D4Vw
# https://samuel-marsh.github.io/scCustomize/
library(scCustomize)
library(qs)

###### step4:  看标记基因库 ######
# 原则上分辨率是需要自己肉眼判断，取决于个人经验
sce.all.int <- qread("2-harmony/sce.all_int-no.qs")
sce.all.int
table(Idents(sce.all.int))
table(sce.all.int$seurat_clusters)
table(sce.all.int$RNA_snn_res.0.1) 
table(sce.all.int$RNA_snn_res.0.3) 
table(sce.all.int$RNA_snn_res.0.5) 
table(sce.all.int$RNA_snn_res.0.8) 


getwd()
dir.create('3-check-by-0.5')
select_idet <- "RNA_snn_res.0.5"
sce.all.int$RNA_snn_res.0.5
#sce.all.int$RNA_snn_res.0.3 <- factor(sce.all.int$RNA_snn_res.0.3,levels = 0:14)
sce.all.int <- SetIdent(sce.all.int, value = select_idet)
table(sce.all.int@active.ident) 
head(sce.all.int@meta.data)

# markers 
cell_types <- list(
"Ovarian cancer" = c("WFDC2", "HE4"),
"GIST" = c("KIT", "CD117"),
"Endometrial cancer" = c("MUC16", "CA125"),
"NK/T cell" = c("CD3D", "CD3E", "CD3G", "CD2", "KLRD1", "KLRF1", "NKG7"),
"Mast cell" = c("TPSAB1", "TPSB2"),
"Macrophage" = c("CD163", "CD68"),
"B cell" = c("CD19", "MS4A1", "CD79A", "IGHG1"),
"Smooth muscle" = c("ACTA2", "MYH11"),
"Endothelial" = c("RAMP2", "PECAM1"),
"Fibroblast" = c("DCN", "LUM", "COL1A2"),
"Cell Cycle" = c("TOP2A", "MKI67"),
"Immu" = c("PTPRC")
)
p <- DotPlot(sce.all.int, features = cell_types, assay='RNA',group.by = select_idet,cols = c("grey", "red") ) + 
  ggtitle(select_idet) + 
  xlab("") + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # 更改x轴标签角度
p[["theme"]][["strip.text"]]$angle <- 75
p
ggsave(filename = "3-check-by-0.5/Markers_dotplot-use.pdf", plot=p, width=13, height = 8,bg="white")

结合没有进行harmony的uamp图：

p <- DimPlot(sce.all.int, reduction = "umap", group.by = select_idet, label = T) + 
  ggtitle(select_idet)
p
ggsave(plot=p, filename="3-check-by-0.5/Dimplot_resolution_0.5.pdf",width = 6, height = 6)


# 美化版
p <- CellDimPlot(sce.all.int, group.by = select_idet, reduction = "UMAP", label = T,label.size = 4, label_insitu = T,
                 label_point_size = 1, label_point_color =NA ,label_segment_color = NA)
p
ggsave(plot=p, filename="3-check-by-0.5/Dimplot_resolution_0.5-1.pdf",width = 7, height = 7)

不同样本上色的umap：

p <- DimPlot(sce.all.filt, reduction = "umap",label=T,group.by = "orig.ident") 
p
ggsave(filename='2-harmony/umap-by-orig.ident-before-harmony.png',plot = p, width = 6,height = 5.5)

尝试第一次注释：

cluster2：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM5276942，为单独一个样本，GIST类型，表达 KIT，注释为 GIST

cluster7：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM5276939，为单独一个样本，Endometrioid类型，表达上皮marker，以及WFDC2，注释为 Ovarian cancer

其余问号这里现在还没有确定，我需要再找一些信息辅助进来，cluster25感觉更像是双细胞，还有mast那一群很奇怪，它跟NK/T挤在了一起没有分开，而Fibroblast 则是分散成了很多群。

0	NK/T cell	10	Ovarian cancer	20	Fibroblast
1	Macrophage	11	？	21	Smooth muscle
2	GIST	12	？	22	mast?
3	？	13	Fibroblast	23	B cell
4	Endothelial	14	Fibroblast	24	Cycling cell
5	？	15	？	25	Macrophage？双细胞？
6	Fibroblast	16	？
7	Ovarian cancer	17	Fibroblast
8	Fibroblast	18	？
9	Ovarian cancer	19	Endothelial