背marker list 不要貪多哦肄鸽，要慢慢地背社证，一個(gè)一個(gè)一個(gè)地背，然后背了再很快地忘記乌妒，如此反復(fù)汹想。
外周血單個(gè)核細(xì)胞（Peripheral blood mononuclear cell,PBMC)是外周血中具有單個(gè)核的細(xì)胞，包括淋巴細(xì)胞和單核細(xì)胞撤蚊。注意這里是 mononuclear cell 單個(gè)核細(xì)胞古掏，而不是 Monocyte 單核細(xì)胞。外周血單個(gè)核細(xì)胞主要的分離方法是Ficoll-hypaque（聚蔗糖－泛影葡胺）密度梯度離心法侦啸。
單個(gè)核細(xì)胞是血液白細(xì)胞中具有單個(gè)核細(xì)胞的一個(gè)含糊術(shù)語(yǔ)槽唾。以淋巴細(xì)胞為主，也包括少數(shù)單核細(xì)胞光涂、漿細(xì)胞等庞萍。外周血中單個(gè)核細(xì)胞的相對(duì)密度在1.076-1.090之間，常用相對(duì)密度1.077±0.001聚蔗糖-泛影葡胺作密度梯度離心加以分離忘闻。
PBMCs是與人類健康和疾病有關(guān)的研究和臨床研究的重要組成部分和有力工具钝计。通過(guò)有效和成功的處理和分析PBMCs，研究人員和臨床醫(yī)生可以測(cè)試免疫反應(yīng)，加深對(duì)免疫系統(tǒng)的了解私恬，并將他們的發(fā)現(xiàn)應(yīng)用于人類疾病的治療和治療债沮。
# 函數(shù)來(lái)自 微信公眾號(hào) 單細(xì)胞天地 一篇推文
# 也一并背下來(lái)把。
library(Seurat)
library(SeuratData)
library(ggplot2)
data("pbmc3k.final")
modify_vlnplot<- function(obj,
feature,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
p<- VlnPlot(obj, features = feature, pt.size = pt.size, ... ) +
xlab("") + ylab(feature) + ggtitle("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_line(),
axis.title.y = element_text(size = rel(1), angle = 0, vjust = 0.5),
plot.margin = plot.margin )
return(p)
}
## main function
StackedVlnPlot<- function(obj, features,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
plot_list<- purrr::map(features, function(x) modify_vlnplot(obj = obj,feature = x, ...))
plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
theme(axis.text.x=element_text(), axis.ticks.x = element_line())
p<- patchwork::wrap_plots(plotlist = plot_list, ncol = 1)
return(p)
}
my36colors <-c('#E5D2DD', '#53A85F', '#F1BB72', '#F3B1A0', '#D6E7A3', '#57C3F3', '#476D87',
'#E95C59', '#E59CC4', '#AB3282', '#23452F', '#BD956A', '#8C549C', '#585658',
'#9FA3A8', '#E0D4CA', '#5F3D69', '#C5DEBA', '#58A4C3', '#E4C755', '#F7F398',
'#AA9A59', '#E63863', '#E39A35', '#C1E6F3', '#6778AE', '#91D0BE', '#B53E2B',
'#712820', '#DCC1DD', '#CCE0F5', '#CCC9E6', '#625D9E', '#68A180', '#3A6963',
'#968175'
)
classmk <-c("IL7R", "CCR7", "IL7R", "S100A4","CD14", "LYZ","MS4A1","CD8A","FCGR3A", "MS4A7", "GNLY",
"NKG7","FCER1A", "CST3","PPBP")
StackedVlnPlot(pbmc3k.final, c(classmk), pt.size=0, cols=my36colors)
就算是Seurat自帶的數(shù)據(jù)集本鸣，定義的細(xì)胞類型也有marker之間的交叉啊疫衩。我還怕什么呢？大膽地定義細(xì)胞的類型吧荣德。
[1] https://divingintogeneticsandgenomics.rbind.io/post/stacked-violin-plot-for-visualizing-single-cell-data-in-seurat/
[2] https://rpubs.com/DarrenVan/628853
[3] https://www.stemexpress.com/blogs/peripheral-blood-mononuclear-cells/
