你到底想要什么样的umap/tsne图？

随着生物学背景知识的增加，单细胞图谱的可视化直接用10X的Loup或者seurat的Dimplot函数直接绘制的umap/tsne图往往很难达到要求了，这就要求我们提高绘图技能。我们都知道ggplot2是一款很好的绘图R包，甚至可以说在语法上扩展了R语言本身。那么，当我们需要绘图的时候，自然我们会想到它及其周边。今天我们就主要地看一下ggforce这个包带给我们的可能性。

首先，我载入数据：

library(Seurat)library(ggplot2)library(tidyverse)pbmc <- readRDS('G:\\Desktop\\Desktop\\RStudio\\single_cell\\filtered_gene_bc_matrices\\hg19pbmc_tutorial.rds')pbmcAn object of class Seurat 13714 features across 2638 samples within 1 assay Active assay: RNA (13714 features) 3 dimensional reductions calculated: pca, umap, tsne···

为了提供更多的分群结果，我们再跑一次FindClusters,当然你也可自己构建分组方式，比如不同的样本，VDJ不同的克隆型等

pbmc<-FindClusters(pbmc,resolution = c(.4,.8,1.2,1.6,2)) head(pbmc@meta.data) orig.ident nCount_RNA nFeature_RNA percent.mt RNA_snn_res.0.5AAACATACAACCAC pbmc3k 2419 779 3.0177759 1AAACATTGAGCTAC pbmc3k 4903 1352 3.7935958 3AAACATTGATCAGC pbmc3k 3147 1129 0.8897363 1AAACCGTGCTTCCG pbmc3k 2639 960 1.7430845 2AAACCGTGTATGCG pbmc3k 980 521 1.2244898 6AAACGCACTGGTAC pbmc3k 2163 781 1.6643551 1 seurat_clusters RNA_snn_res.0.4 RNA_snn_res.0.8 RNA_snn_res.1.2AAACATACAACCAC 1 2 1 5AAACATTGAGCTAC 0 3 2 2AAACATTGATCAGC 2 2 1 1AAACCGTGCTTCCG 3 1 4 4AAACCGTGTATGCG 8 6 7 8AAACGCACTGGTAC 1 2 1 1 RNA_snn_res.1.6 RNA_snn_res.2AAACATACAACCAC 9 1AAACATTGAGCTAC 2 0AAACATTGATCAGC 1 2AAACCGTGCTTCCG 4 3AAACCGTGTATGCG 8 8AAACGCACTGGTAC 1 1

为了调用ggplot2我们把UMAP的坐标放到metadata中：

pbmc<-AddMetaData(pbmc,pbmc@reductions$umap@cell.embeddings,col.name = colnames(pbmc@reductions$umap@cell.embeddings))head(pbmc@meta.data)

读入一套我珍藏多年的颜色列表：

allcolour=c("#DC143C","#0000FF","#20B2AA","#FFA500","#9370DB","#98FB98","#F08080","#1E90FF","#7CFC00","#FFFF00", "#808000","#FF00FF","#FA8072","#7B68EE","#9400D3","#800080","#A0522D","#D2B48C","#D2691E","#87CEEB","#40E0D0","#5F9EA0", "#FF1493","#0000CD","#008B8B","#FFE4B5","#8A2BE2","#228B22","#E9967A","#4682B4","#32CD32","#F0E68C","#FFFFE0","#EE82EE", "#FF6347","#6A5ACD","#9932CC","#8B008B","#8B4513","#DEB887")

我用ggplot画一个带有标签的umap图：

class_avg <- pbmc@meta.data %>% group_by(RNA_snn_res.2) %>% summarise( UMAP_1 = median(UMAP_1), UMAP_2 = median(UMAP_2) ) umap <- ggplot(pbmc@meta.data ,aes(x=UMAP_1,y=UMAP_2))+ geom_point(aes(color=RNA_snn_res.2))+ scale_color_manual(values = allcolour)+ geom_text(aes(label = RNA_snn_res.2), data = class_avg)+ theme(text=element_text(family="Arial",size=18)) + theme(panel.background = element_rect(fill='white', colour='black'), panel.grid=element_blank(), axis.title = element_text(color='black', family="Arial",size=18),axis.ticks.length = unit(0.4,"lines"), axis.ticks = element_line(color='black'), axis.ticks.margin = unit(0.6,"lines"), axis.line = element_line(colour = "black"), axis.title.x=element_text(colour='black', size=18), axis.title.y=element_text(colour='black', size=18), axis.text=element_text(colour='black',size=18), legend.title=element_blank(), legend.text=element_text(family="Arial", size=18), legend.key=element_blank())+ theme(plot.title = element_text(size=22,colour = "black",face = "bold")) + guides(colour = guide_legend(override.aes = list(size=5)))umap

学会了吗?

为了使我们的图层不要那么复杂，还是先画一个简单的：

umap <- ggplot(pbmc@meta.data ,aes(x=UMAP_1,y=UMAP_2,color=RNA_snn_res.2))+ geom_point() umap

好奇的我们来看一下umap这个图都有什么：

head(umap$data)umap$themeumap$layersumap$scalesumap$mappingumap$coordinatesumap$facetumap$plot_env umap$labels

然后，我们请出ggforce这个包，看看第一次的惊喜。

library(ggforce) umap + facet_zoom(x = RNA_snn_res.2 == "14")

想要细致刻画某个亚群，这不失是一个方法：

umap + facet_zoom(xlim = c(-15, -10), ylim = c(0, 2.5))

umap + geom_mark_rect(aes(label = RNA_snn_res.2), show.legend = FALSE) + theme_void()

给每个群加框加标签，优雅：

library(concaveman)umap +geom_mark_hull(aes(label = RNA_snn_res.2)) + theme_void()

可以根据自己的数据格式换一换：

umap + geom_mark_hull(aes(label = RNA_snn_res.2, fill = RNA_snn_res.0.4), show.legend = FALSE) + theme_void()

如果有需要特别化为一类的可以用背景色来圈住：

umap + geom_mark_hull(aes(label = RNA_snn_res.2, fill = RNA_snn_res.2), show.legend = FALSE, expand = unit(3, "mm")) + theme_void()

umap + geom_mark_hull(aes(label = RNA_snn_res.2, fill = RNA_snn_res.2), show.legend = FALSE, expand = unit(3, "mm")) + theme_no_axes()

desc <- 'I am a luck dog'umap + geom_mark_ellipse(aes(filter = RNA_snn_res.2 == '14', label = '14', description = desc))

想对某一亚群做进一步的注释：

你也可以：

umap + geom_mark_hull(aes(filter = RNA_snn_res.2 == '14', label = '14', description = desc))

umap +geom_voronoi_tile(aes(fill = RNA_snn_res.2, group = -1L)) + geom_voronoi_segment()

Voronoi图背后的想法是将图的区域分割成尽可能多的部分。与网格或热图不同，Voronoi根据与其他点的接近程度为每个点绘制自定义形状。它返回一个看起来像彩色玻璃的图。这可以很好地确定每个区域内的最近点。例如，零售商可以使用它来查看他们的商店位置所覆盖的区域，并可以帮助他们做出决策，根据每个Voronoi形状的大小来优化他们的位置。

其实这并看不清

umap +geom_voronoi_tile(aes(fill = RNA_snn_res.2), max.radius = 1,colour = 'black')

看一看出哪些地方的细胞比较密集，这一点当然需要好的降维结构了，细胞密集与否分别代表什么？越密集的区域细胞距离越近，说明异质性较低。当然，这和降维结构有关。

umap +geom_voronoi_tile(aes(fill = RNA_snn_res.2), max.radius = .1,colour = 'black')

umap + geom_mark_hull(aes(fill = RNA_snn_res.2), expand = unit(3, "mm")) + coord_cartesian(xlim = c(-15, -10), ylim = c(0, 2.5)) + geom_voronoi_segment()

有种细胞的感觉了吗?

最后，作为附赠：

pbmc@meta.data %>% gather_set_data(6:11) %>% ggplot(aes(x, id = id, split = y, value = 1)) + geom_parallel_sets(aes(fill = RNA_snn_res.0.4), show.legend = FALSE, alpha = 0.3) + geom_parallel_sets_axes(axis.width = 0.1, color = "lightgrey", fill = "white") + geom_parallel_sets_labels(angle = 0) + theme_no_axes()

pbmc@meta.data %>% count(RNA_snn_res.0.4) %>% ggplot() + geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = 0.7, r = 1, amount = n, fill = RNA_snn_res.0.4), alpha = 0.3, stat = "pie")

p1 <- pbmc@meta.data %>% count(RNA_snn_res.0.4) %>% mutate(focus = ifelse(RNA_snn_res.0.4 == "0", 0.2, 0)) %>% ggplot()+ geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = 0.7, r = 1, amount = n, fill = RNA_snn_res.0.4, explode = focus), alpha = 1, stat = "pie") + scale_fill_manual(values = allcolour)

sc-RAN-seq 数据分析||Seurat新版教程:Guided Clustering Tutorial

sc-RAN-seq 数据分析||Seurat新版教程: Integrating datasets to learn cell-type specific responses

sc-RAN-seq 数据分析||Seurat新版教程: Using sctransform in Seurat

单细胞转录组数据分析||Seurat新版教程:Differential expression testing

单细胞转录组数据分析||Seurat新版教程:New data visualization methods in v3.0