115 lines
3.4 KiB
R
115 lines
3.4 KiB
R
## Written By LiShang
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## Notice: 仅适用于10x平台
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#加载依赖包
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library(Seurat)
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library(sctransform)
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library(dplyr)
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library(ggplot2)
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########################读取样本并生成Seurat Object##########################
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#获取所有样本的存放目录
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setwd("~/scRNA-seq")
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sample_dirs <- list.dirs(full.names = FALSE)[-1]
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#同时读取所有样本, 并转化成Seurat Object
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sample_list <- mapply(function(dir) {
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Read10X(data.dir = dir) %>%
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CreateSeuratObject(project = dir, min.cells = 3, min.features = 200)
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}, dir = sample_dirs, SIMPLIFY = FALSE)
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#合并所有样本
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merged <- merge(sample_list[[1]], sample_list[-1], add.cell.ids = sample_dirs)
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#提取样本的分组信息, 本模板分为cancer和normal两个组
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merged[["sample.grp"]] <- merged@meta.data$orig.ident %>%
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strsplit(split = "_") %>%
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sapply(function(x) x[2]) %>%
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as.factor()
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#################################质量控制####################################
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#计算线粒体转录本占比
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merged[["percent.mt"]] <- PercentageFeatureSet(merged, pattern = "^MT-")
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#细胞过滤
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merged <- merged %>%
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subset(subset = nFeature_RNA > 200 & nFeature_RNA < 2500) %>%
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subset(subset = percent.mt < 5)
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###############################标准分析流程##################################
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#SCTransform标准化、线性降维
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merged <- merged %>%
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SCTransform(vars.to.regress = "percent.mt", verbose = FALSE) %>%
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RunPCA(verbose = FALSE)
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#多样本数据整合, harmony去批次
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merged <- IntegrateLayers(
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object = merged,
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method = HarmonyIntegration,
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normalization.method = "SCT",
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orig.reduction = "pca",
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new.reduction = "harmony",
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verbose = FALSE
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)
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merged[["RNA"]] <- JoinLayers(merged[["RNA"]])
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#聚类、非线性降维
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merged <- merged %>%
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FindNeighbors(dims = 1:30, reduction = "harmony", verbose = FALSE) %>%
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FindClusters(resolution = 2, verbose = FALSE) %>%
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RunUMAP(dims = 1:30, reduction = "harmony", verbose = FALSE)
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###############################细胞类型注释##################################
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DimPlot(merged, reduction = "umap", label = TRUE)
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DotPlot(merged, c("EPCAM", "CDH1", "GATA3", "KRT18", #Epithelial
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"ESR1", "PGR", "ERBB2", #Breast Cancer
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"PDGFRA", "COL1A1", "FAP", "DCN", "VIM", #Fibroblast
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"CD34", "VWF", "CDH5", "ENG", "PECAM1", #Endothelial
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"PTPRC", #CD45: Immune
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"CD2", "CD3D", "CD3E", "CD3G", #T cell
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"MS4A1", "CD79A", "CD79B", #B cell
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"KLRF1", "KLRD1", "NKG7", "GNLY" #NK cell
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), cols = c("yellow","blue")
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) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
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new_cluster_ids <- c(
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"T cell",
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"malignant",
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"T cell",
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"T cell",
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"malignant",
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"malignant",
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"malignant",
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"malignant",
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"fibroblast",
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"malignant",
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"fibroblast",
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"malignant",
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"malignant",
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"malignant",
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"malignant",
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"endothelial",
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"fibroblast",
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"T cell",
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"fibroblast",
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"malignant",
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"malignant",
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"malignant",
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"malignant",
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"B cell"
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)
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names(new_cluster_ids) <- levels(merged)
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merged <- RenameIdents(merged, new_cluster_ids)
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###############################数据分析作图##################################
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merged <- PrepSCTFindMarkers(merged)
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markers <- FindMarkers(merged, ident.1 = "malignant")
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FeaturePlot(merged, features = c("CREB1","CREB3"))
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DotPlot(merged, features = c("CREB1","CREB3"))
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VlnPlot(merged, features = c("CREB1"))
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