我们很久之前发布过这样的帖子（玩转单细胞(2):Seurat批量做图修饰，Seurat单细胞基因显著性检验函数及批量添加显著性）。都解决了一定的问题，但是不够完美，且小伙伴说某些地方有报错。所以我们这里重新探究一下，如何批量修饰seurat包中Vlnplot作图，以及批量添加显著性，其实很简单，只用到一个&连接符。在某些帖子中我们也讲过。但是，本贴最最重要的是我们要复现一篇Cell子刊中的图表，基本图形还是Vlnplot展示基因表达，特点是在图的上部展示了表达基因的细胞比例：这幅图的难点在于获取饼图数据，以及将其对应展示在小提琴图上！
（reference：Distinctive multicellular immunosuppressive hubs confer different intervention strategies for left- and right-sided colon cancers）首先我们演示下Vlnplot作图的修饰：
#加载R包library(ggpubr)library(ggimage)library(ggplot2)library(Seurat)
#设置比较-两两比较my_comparisons <- list(c("GM", "BM"))
#单个featuresVlnPlot(human_data, features = "ANXA1", group.by = "group")& theme_bw()& theme(axis.title.x = element_blank(), axis.text.x = element_text(color = 'black',face = "bold", size = 12), axis.text.y = element_text(color = 'black', face = "bold"), axis.title.y = element_text(color = 'black', face = "bold", size = 15), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_rect(color="black",size = 1.2, linetype="solid"), panel.spacing = unit(0.12, "cm"), plot.title = element_text(hjust = 0.5, face = "bold.italic"), legend.position = 'none')& stat_compare_means(method="t.test",hide.ns = F, comparisons = my_comparisons, label="p.signif", bracket.size=0.8, tip.length=0, size=6)& scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))& scale_fill_manual(values = c("#FF5744","#208A42"))
多个基因批量修饰：
#多个featuresVlnPlot(human_data, features = c("ANXA1","S100A8"), group.by = "group")& theme_bw()& theme(axis.title.x = element_blank(), axis.text.x = element_text(color = 'black',face = "bold", size = 12), axis.text.y = element_text(color = 'black', face = "bold"), axis.title.y = element_text(color = 'black', face = "bold", size = 15), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_rect(color="black",size = 1.2, linetype="solid"), panel.spacing = unit(0.12, "cm"), plot.title = element_text(hjust = 0.5, face = "bold.italic"), legend.position = 'none')& stat_compare_means(method="t.test",hide.ns = F, comparisons = my_comparisons, label="p.signif", bracket.size=0.8, tip.length=0, size=6)& scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))& scale_fill_manual(values = c("#FF5744","#208A42"))
多组展示，显著性检验我们用的两两t检验，可自行修改别的检验方式：
#三组，多个features,两两比较my_comparisons1 <- list(c("HC", "EEC"))my_comparisons2 <- list(c("EEC", "AEH"))my_comparisons3 <- list(c("HC","AEH"))
#设置x轴样本顺序Idents(uterus) <- "orig.ident"Idents(uterus) <- factor(Idents(uterus), levels = c("HC","AEH","EEC"))
VlnPlot(uterus, features = c("TXNIP","CXCL1","CCL5","FTH1"), ncol = 2)& theme_bw()& theme(axis.title.x = element_blank(), axis.text.x = element_text(color = 'black',face = "bold", size = 12), axis.text.y = element_text(color = 'black', face = "bold"), axis.title.y = element_text(color = 'black', face = "bold", size = 15), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_rect(color="black",size = 1.2, linetype="solid"), panel.spacing = unit(0.12, "cm"), plot.title = element_text(hjust = 0.5, face = "bold.italic"), legend.position = 'none')& stat_compare_means(method="t.test",hide.ns = F, comparisons = c(my_comparisons1,my_comparisons2,my_comparisons3), label="p.signif", bracket.size=0.8, tip.length=0, size=6)& scale_y_continuous(expand = expansion(mult = c(0.05, 0.1)))& scale_fill_manual(values = c("#FF5744","#208A42", "#FCB31A"))
接下来就是复现文章中的图表了，也比较简单，主题就是上面的这些小提琴图，只不过需要计算一下比例，做一下饼图添加上去就可以了。一步步也能够完成，饼图可以参考余老师的ggimage（https://cosx.org/2017/03/ggimage/）。但是考虑到每次换个基因就需要重新来一遍，流程繁琐，所以本着我们号“麻烦自己，方便他人”的精神，干脆整成一个小函数得了，这样小伙伴就不用考虑中间乱七八糟的过程了！
我们先看看函数主体： 视频解说参考B站：
函数中部分如果需调整，自行修改即可，比如检验方式：首先看看两组：
ks_VlnExp(object = human_data, group="group",group_order=c("BM","GM"), features="ANXA1",comparisons=list(c("GM", "BM")))
颜色可自定义：
ks_VlnExp(object = human_data, group="group",group_order=c("BM","GM"), features="ANXA1",comparisons=list(c("GM", "BM")), cols=c("#E22C28","#0D6EBA"))
image.png
多组比较可视化也是没有问题的：
ks_VlnExp(object = uterus, group="orig.ident", group_order=c("HC","AEH","EEC"), features="ANXA1",comparisons=c(my_comparisons1,my_comparisons2,my_comparisons3))
image.png
没毛病，非常完美！希望对你有所帮助！
