ixxmu
commented
10 months ago 和降维图保持一致的细胞类群统计图 by YuLabSMU
首先来个Seurat的套路代码：
library(Seurat)
dir = "data/filtered_gene_bc_matrices/hg19"
pbmc.data <- Read10X(data.dir = dir)
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 <- ScaleData(pbmc)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst",
nfeatures = 2000)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- RunUMAP(pbmc, dims = 1:10)
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
## Assigning cell type identity to clusters
cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T",
"B", "CD8 T", "FCGR3A+ Mono", "NK", "DC", "Platelet")
names(cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, cluster.ids)
再来画个降维图：
library(ggplot2)
library(ggsc)
p <- sc_dim(pbmc) + sc_dim_geom_label()
p
如果你想可视化不同类别的细胞数目，是不是还稍微有点麻烦，统计一下，画个柱状图，这个一点都不麻烦，麻烦的是你要和你的降维图保持颜色对应。所以有一些解决方案，是写个函数，同时把两个图都出了，画在一起。但这样子绑死呢，就会降低灵活性。我的一向思路是保持最简单，然后可组合。那么在ggsc我们是怎么做的？那就是你画你的降维图，然后拿降维图来做为输入，画柱状图，让柱状图和降维图保持一致。
一条指令出图：
sc_dim_count(p)
假设我换个配色方案，柱状图跟着变：
p <- p + scale_color_viridis_d()
p2 <- sc_dim_count(p)
aplot::plot_list(p, p2, widths=c(1, .5))
另外如果你是用Seurat画的图，也是可以支持的。
p3 <- DimPlot(pbmc, reduction = "umap",
label = TRUE, pt.size = 0.5)
p4 <- sc_dim_count(p3)
aplot::plot_list(p3, p4, widths=c(1, .5))
KISS（Keep It Simple and Stupid）原则是我们所追求的。
https://mp.weixin.qq.com/s/PRStNRokP3oCgGStbiNRLw