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❞ 玩转单细胞系列都是些很小的问题，但是很实用，上面是之前的链接，可以收藏，需要的时候直接使用即可。今天这一期的话我们讲一些小的作图以及数据运行的技巧，能够方便你的学习。这一期内容感谢我们KS科研分享与服务微信VIP群的小伙伴@G。第一部分就是他的分享提示。之前我们分享过Seurat批量做图修饰，和很多人一样，我们的方式也是采取循环修饰加拼图的办法实现，但是后来才知道，将“+”改成“&”连接ggplot的修饰，就可以实现对seurat作图的批量修饰了！library(Seurat)library(ggplot2)library(ggpubr)setwd("D:\\KS项目\\公众号文章\\单细胞作图小技巧")genes <- c("S100a8","Zfhx3","Numb","Ltf", "S100a9","Trps1", "Cd52","Anxa1", "Neat1","Ly6g", "Tnf","Il1b")
mouse_data$sex <- ifelse(mouse_data$orig.ident=="10X_ntph_F","F",'M')
VlnPlot(mouse_data, cols = c("limegreen", "navy"), pt.size = 0, group.by = "sex", features = genes, ncol = 4, log = FALSE)&theme(axis.title.x = element_blank(), panel.border = element_rect(fill=NA,color="black"), axis.title.y = element_text(color = 'black', size = 12))& geom_boxplot(width=0.1, fill="white", outlier.shape = NA) & theme(legend.position = 'none')
第二部分是一个差异基因的分析，来源于之前我们分享的一篇Nature文章（（视频教程）复现Nature图表：富集分析DEscore及可视化）。我们以前要批量分析celltype的差异基因的方式采取的是分别subset每种celltype的seurat然后计算的方式，其实我们可以直接将分组和celltype结合，进行分析，省去了很多麻烦！
#1、差异基因# unique(human_data$celltype)celltypes = c("Neutrophil","Macrophage","mDC","T cell")#结合celltype与分组，做每种celltype两组之间差异基因deg = c()Idents(human_data) = paste(human_data$celltype, human_data$group, sep = '_')
for (cell in celltypes) try({ tmp = FindMarkers(human_data, ident.1 = paste0(cell, '_GM'), ident.2 = paste0(cell, '_BM')) tmp$gene = rownames(tmp) tmp$celltype = cell deg = rbind(deg, tmp) })
#显著性基因deg = subset(deg, p_val_adj < 0.05)deg$DE =''deg$DE = ifelse(deg$avg_log2FC>0, 'up', 'down')#上下调分组
第三部分是关于单细胞堆叠小提琴图的做法，这个已经是一个写的很多的话题了，我们还写过一个函数（ggplot堆叠图无缝拼接(自写一个简洁堆叠小提琴图函数））。如果只是简单的做堆叠小提琴图，没必要像以前网上的分享那样，乱七八糟的做法，只需要在VlnPlot中加入stack = T参数就可以实现了。library(dittoSeq)VlnPlot(mouse_data, cols = dittoColors(), pt.size = 0, group.by = "sex", features = genes, log = FALSE, stack = T)+ NoLegend()
第四部分则是文献摘录的好看的配色组合，遗憾的是文献并未记载，不过依然感谢他们的分享，希望对你作图有用：完整版已发布微信群和QQ群！
major_trajectory_color_plate = c("Neuroectoderm_and_glia" = "#f96100", "Intermediate_neuronal_progenitors" = "#2e0ab7", "Eye_and_other" = "#00d450", "Ependymal_cells" = "#b75bff", "CNS_neurons" = "#e5c000", "Mesoderm" = "#bb46c5", "Definitive_erythroid" = "#dc453e", "Epithelium" = "#af9fb6", "Endothelium" = "#00a34e", "Muscle_cells" = "#ffa1f5", "Hepatocytes" = "#185700", "White_blood_cells" = "#7ca0ff", "Neural_crest_PNS_glia" = "#fff167", "Adipocytes" = "#7f3e39", "Primitive_erythroid" = "#ffa9a1", "Neural_crest_PNS_neurons" = "#b5ce92", "T_cells" = "#ff9d47", "Lung_and_airway" = "#02b0d1", "Intestine" = "#ff007a", "B_cells" = "#01b7a6", "Olfactory_sensory_neurons" = "#e6230b", "Cardiomyocytes" = "#643e8c", "Oligodendrocytes" = "#916e00", "Mast_cells" = "#005361", "Megakaryocytes" = "#3f283d", "Testis_and_adrenal" = "#585d3b") day_color_plate = c("E8.0" = "#5E4FA2", "E8.25" = "#545BA8", "E8.5" = "#4A68AE", "E8.75" = "#4075B4", "E9.0" = "#3682BA", "E9.25" = "#398FB9", "E9.5" = "#449DB4", "E9.75" = "#50AAAE", "E10.0" = "#5CB7A9", "E10.25" = "#69C3A4", "E10.5" = "#78C9A4", "E10.75" = "#88CFA4", "E11.0" = "#98D5A4", "E11.25" = "#A7DBA4", "E11.5" = "#B5E1A1", "E11.75" = "#C3E69F", "E12.0" = "#D0EC9C", "E12.25" = "#DDF199", "E12.5" = "#E8F59B", "E12.75" = "#EDF8A4", "E13.0" = "#F3FAAD", "E13.25" = "#F9FCB6", "E13.5" = "#FFFFBF", "E13.75" = "#FEF7B3", "E14.0" = "#FEF0A7", "E14.25" = "#FEE99B", "E14.333" ="#FEE28F", "E14.75" = "#FDD985", "E15.0" = "#FDCD7B", "E15.25" = "#FDC272", "E15.5" = "#FDB768", "E15.75" = "#FCAB5F", "E16.0" = "#FA9C58", "E16.25" = "#F88D52", "E16.5" = "#F67E4B", "E16.75" = "#F46F44", "E17.0" = "#EE6445", "E17.25" = "#E75947", "E17.5" = "#E04F4A", "E17.75" = "#D9444D", "E18.0" = "#CF384D", "E18.25" = "#C32A4A", "E18.5" = "#B71C47", "E18.75" = "#AA0E44", "P0" = "#9E0142")
#################################### Somite counts color plate ####################################
somite_color_plate = rev(c("#fde725", "#eae51a", "#d5e21a", "#c0df25", "#a8db34", "#93d741", "#7fd34e", "#6ccd5a", "#58c765", "#48c16e", "#3aba76", "#2eb37c", "#25ab82", "#20a386", "#1e9c89", "#1f948c", "#228c8d", "#25848e", "#287d8e", "#2b758e", "#2e6d8e", "#32658e", "#365d8d", "#3a548c", "#3e4a89", "#424186", "#453882", "#472e7c", "#482374", "#48186a", "#470d60", "#440154"))
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