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')
pbmc
An 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([email protected])
orig.ident nCount_RNA nFeature_RNA percent.mt RNA_snn_res.0.5
AAACATACAACCAC pbmc3k 2419 779 3.0177759 1
AAACATTGAGCTAC pbmc3k 4903 1352 3.7935958 3
AAACATTGATCAGC pbmc3k 3147 1129 0.8897363 1
AAACCGTGCTTCCG pbmc3k 2639 960 1.7430845 2
AAACCGTGTATGCG pbmc3k 980 521 1.2244898 6
AAACGCACTGGTAC pbmc3k 2163 781 1.6643551 1
seurat_clusters RNA_snn_res.0.4 RNA_snn_res.0.8 RNA_snn_res.1.2
AAACATACAACCAC 1 2 1 5
AAACATTGAGCTAC 0 3 2 2
AAACATTGATCAGC 2 2 1 1
AAACCGTGCTTCCG 3 1 4 4
AAACCGTGTATGCG 8 6 7 8
AAACGCACTGGTAC 1 2 1 1
RNA_snn_res.1.6 RNA_snn_res.2
AAACATACAACCAC 9 1
AAACATTGAGCTAC 2 0
AAACATTGATCAGC 1 2
AAACCGTGCTTCCG 4 3
AAACCGTGTATGCG 8 8
AAACGCACTGGTAC 1 1
为了调用ggplot2我们把UMAP的坐标放到metadata中:
pbmc<-AddMetaData(pbmc,pbmc@[email protected],col.name = colnames(pbmc@[email protected]))
head([email protected])
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 <- [email protected] %>%
group_by(RNA_snn_res.2) %>%
summarise(
UMAP_1 = median(UMAP_1),
UMAP_2 = median(UMAP_2)
)
umap <- ggplot([email protected] ,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
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([email protected] ,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$data)
umap$theme
umap$layers
umap$scales
umap$mapping
umap$coordinates
umap$facet
umap$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。
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:
[email protected] %>%
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()
[email protected] %>%
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 <- [email protected] %>%
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)
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https://www.r-bloggers.com/the-ggforce-awakens-again/
https://rviews.rstudio.com/2019/09/19/intro-to-ggforce/