文章信息
题目:Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance 期刊:nature medicine 日期:2023-4-26 DOI:https://doi.org/10.1038/s41591-023-02371-y 简介:来自德克萨斯大学安德森癌症中心的Linghua Wang研究团队收集了16种肿瘤类型的>30W个T细胞表达数据,对多种T细胞亚型及其分子、临床特征展开了深入地分析。值得一提的是文章发现了一种曾被忽视的Tstr亚型,并揭示了其与免疫治疗抵抗的关系。
(1)数据规模
(2)初步分析
# 参考Github代码,结合10X示例数据绘制如下
library(TENxPBMCData)
library(Seurat)
library(patchwork)
tenx_pbmc3k <- TENxPBMCData(dataset = "pbmc3k")
counts = as.matrix(assay(tenx_pbmc3k, "counts"))
rownames(counts) = rowData(tenx_pbmc3k)$Symbol_TENx
colnames(counts) = paste0("cell-",1:ncol(counts))
sce = CreateSeuratObject(counts = counts)
sce = sce %>%
NormalizeData() %>%
FindVariableFeatures(nfeatures = 2000) %>%
ScaleData() %>%
RunPCA() %>%
RunUMAP(dims = 1:30) %>%
FindNeighbors(dims = 1:30) %>%
FindClusters(resolution = c(0.01, 0.05, 0.1, 0.2, 0.3, 0.5,0.8,1))
Idents(sce) <- sce$seurat_clusters
coord = Embeddings(object = sce, reduction = "umap")
coord = coord[,c(1,2)]
colnames(coord) = c("UMAP_1", "UMAP_2")
coord = data.frame(ID = rownames(coord), coord)
meta = sce@meta.data
meta = data.frame(ID = rownames(meta), meta,stringsAsFactors = F)
meta = left_join(meta, coord, by = 'ID')
theme_black <- function(base_size = 12, base_family = "") {
theme_grey(base_size = base_size, base_family = base_family) %+replace%
theme(
axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.position = "none",
panel.background = element_rect(fill = "black", color = NA),
panel.border = element_blank(),
panel.grid = element_blank(),
panel.spacing = unit(0, "lines"),
strip.background = element_rect(fill = "grey30", color = "grey10"),
strip.text.x = element_text(size = base_size*0.8, color = "white"),
strip.text.y = element_text(size = base_size*0.8, color = "white",angle = -90),
plot.background = element_rect(color = "black", fill = "black"),
plot.title = element_text(size = base_size*1.2, color = "white"),
plot.margin = unit(rep(0, 4), "lines")
)
}
p1 = DimPlot(sce, reduction = "umap", group.by = "RNA_snn_res.0.5",
label = T, label.size = 5) + theme_void() +
theme(legend.position = "none", plot.title = element_blank()) +
theme(panel.border = element_rect(fill=NA,color="black"))
p2 <- ggplot(data = coord, mapping = aes(x = UMAP_1, y = UMAP_2)) +
stat_density_2d(aes(fill = ..density..), geom = "raster", contour = F) +
geom_point(color = 'white', size = .05) +
scale_fill_viridis(option="magma") +
theme_black()
p1 + p2