肺癌单细胞数据集也有好几十个了,拿到表达量矩阵后的第一层次降维聚类分群通常是:
参考我前面介绍过CNS图表复现08—肿瘤单细胞数据第一次分群通用规则,这3大单细胞亚群构成了肿瘤免疫微环境的复杂。。比如 Science Advances 27 Jan 2021: 的文章《 Decoding the multicellular ecosystem of lung adenocarcinoma manifested as pulmonary subsolid nodules by single-cell RNA sequencing》,就是如此:
绝大部分文章都是抓住免疫细胞亚群进行细分,包括淋巴系(T,B,NK细胞)和髓系(单核,树突,巨噬,粒细胞)的两大类作为第二次细分亚群。但是也有不少文章是抓住stromal 里面的fibo 和endo进行细分,并且编造生物学故事的。
反而是上皮细胞,大家很少涉及到,但是肺癌既然是来源于结直肠这样的组织, 它的上皮细胞就不可能是一个纯粹的上皮,理论上是可以细分的。上面的这个文章其实也接下来部分细分:
可以看到来自于正常的肺的上皮细胞约2000个,可以分成如下所示的5个亚群:
上皮细胞的细分亚群数量肯定是不止这些,比如文章:《Pre-activated antiviral innate immunity in the upper airways controls early SARS-CoV-2 infection in children》,就列出来了近十种细胞亚群:
大家也可以去测试一下这些基因在你的肺部单细胞数据集里面是否好用。
代码给大家;
library(ggplot2)
genes_to_check = c(SPRR3","GDPD3","SPRR1A","SPRR2A","RARRES2","TMPRSS11E",
"ASCL3","CFTR","FOXI2","1SG20","FOXI1",
"SAA4","SAA2","EFHC1","CCDC153","CCDC113","SAA1","CDC20B","FOXJ1",
"MYCL","FOXN4","CCNO",
"PIGR","BP1","MUC5A","VMO1","SCGB3A1","CYP2A13","CYP2B6","SCGB1A1",
"BCAM","KRT1","RT5","P63")
当然了,这样的生物学认知还需要自己深入这个领域。
我给几个数据集给大家,去试试看,能不能从里面把上皮细胞拿出来,并且进行细分亚群,看看能不能有上面列出来的亚群。
页面更新:2024-04-07
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