继续升级你的单细胞可视化_scpubr包

Hi，大家好，我是晨曦
今天这期推文，我们继续来升级我们的单细胞可视化结果，前面一期推文我们讲解了Scillus包可以绘制不同平常的scRNA-seq可视化结果，详细推文如下：
“承包单细胞所有美图？这个R包就够了！”
然后今天这期我们继续介绍另一款单细胞美图的R包——scpubr包
scpubr包的详细帮助文档：About this package | SCpubr (enblacar.github.io)

代码实操

library(dplyr)library(Seurat)library(patchwork)library(Nebulosa)library(SCpubr)

然后我们为了获得一个标准的Seurat对象，我们需要快速走一遍标准流程，这里我们使用的数据就是Seurat官网导学部分的数据

pbmc.data <- Read10X(data.dir = "pbmc3k_filtered_gene_bc_matrices/filtered_gene_bc_matrices/hg19/")pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)all.genes <- rownames(pbmc)pbmc <- ScaleData(pbmc, features = all.genes)pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))pbmc <- FindNeighbors(pbmc, dims = 1:10)pbmc <- FindClusters(pbmc, resolution = 0.5)pbmc <- RunUMAP(pbmc, dims = 1:10)new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T", "FCGR3A+ Mono", "NK", "DC", "Platelet")names(new.cluster.ids) <- levels(pbmc)pbmc <- RenameIdents(pbmc, new.cluster.ids)DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()

好，到此为止，我们已经成功获得了一个Seurat对象，我们后续的可视化全部都是基于这个对象，更多详细内容大家可以直接去看这个R包的帮助文档，这里晨曦只是展示一些个人觉得好看的可视化展现形式

Figure 1

p <- Nebulosa::plot_density(pbmc, features = "CD14")p

Figure 2

genes <- list("Naive CD4+ T" = c("IL7R", "CCR7"), "CD14+ Mono" = c("CD14", "LYZ"), "Memory CD4+" = c("S100A4"), "B" = c("MS4A1"), "CD8+ T" = c("CD8A"), "FCGR3A+ Mono" = c("FCGR3A", "MS4A7"), "NK" = c("GNLY", "NKG7"), "DC" = c("FCER1A", "CST3"), "Platelet" = c("PPBP"))p <- SCpubr::do_DotPlot(sample = pbmc, features = genes)p

Figure 3

基于接口的选择可以达到修改参数即可以完成富集分析

genes <- c("ABCB1", "ABCG2", "AHR", "AKT1", "AR")p <- SCpubr::do_TermEnrichmentPlot(genes = genes, dbs_use = "C", ncol = 2)p

#参数解析#dbs_use参数决定了向哪个富集分析数据库进行富集

#“A”: Will perform a query to 4 databases for cell types (Azimuth, Descartes, PanglaoDB and Descartes) and 4 databases for functional terms (MsigDB, GO-BP, GO-MF and KEGG). This is the default option if this parameter is not provided.

#“B”: Performs a query for the cell type databases (Azimuth, Descartes, PanglaoDB and Descartes).#“C”: Performs a query for the functional terms (MsigDB, GO-BP, GO-MF and KEGG).

Figure 5

理解细胞的一个非常常见的方法是查询从文献中检索到的几个标记基因列表，并计算每个细胞在给定的基因列表中的富集程度。这是通过使用Seurat: : AddModuleScore实现的

genes <- list("Naive CD4+ T" = c("IL7R", "CCR7"), "CD14+ Mono" = c("CD14", "LYZ"), "Memory CD4+" = c("S100A4"), "B" = c("MS4A1"), "CD8+ T" = c("CD8A"), "FCGR3A+ Mono" = c("FCGR3A", "MS4A7"), "NK" = c("GNLY", "NKG7"), "DC" = c("FCER1A", "CST3"), "Platelet" = c("PPBP"))# Default parameters.p <- SCpubr::do_EnrichmentHeatmap(sample = sample, list_genes = genes)p