1. Umap图
原图
plot3 <- DimPlot(jushi_1, label = T, pt.size = 1)+#这后面可以不写
NoLegend()+labs(x = "UMAP1", y = "UMAP2",title = "Celltype") +
theme(panel.border = element_rect(fill=NA,color="black", linewidth=1, linetype="solid"),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
美化后
library(ggsci)
library(ggplot2)
#nature版本# 这个更好看一点，用这个！
plot4 = plot3 + scale_color_npg()
#science版本
plot5 = plot3 +scale_color_aaas()
plot_grid(plot4,plot5)
2.热图
原图
DoHeatmap(scRNA_0.1,features = top10$gene)+NoLegend()
美化后
DoHeatmap(markerdata,
features = as.character(unique(markers$markers)),
group.by = "celltype",
assay = 'RNA',
group.colors = c("#00BFC4","#AB82FF","#00CD00","#C77CFF"))+
scale_fill_gradientn(colors = c("white","grey","firebrick3"))
自定义cluster的顺序
DoHeatmap(markerdata,
features = as.character(unique(markers$markers)),
group.by = "celltype",
assay = 'RNA',
group.colors = c("#00BFC4","#AB82FF","#00CD00","#C77CFF"))+
scale_fill_gradientn(colors = c("white","grey","firebrick3"))
3. 小提琴图
install.packages("remotes")
remotes::install_github("lyc-1995/MySeuratWrappers")#通过链接安装library(MySeuratWrappers)
markers <- c(''CD3D'', ''S100A8'', ''S100A9'', ''CD79A'', ''CCL5'', ''NKG7'', ''GZMA'', ''IL32'', ''CD4'', ''CD8A'', ''LTB'', ''FCN1'', ''MS4A1'', ''SPON2'',''FCER1A'',''SERPINF1'', ''TMEM40'', ''CD3E'')
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'')
VlnPlot(PBMC, features = markers,
stacked=T,
pt.size=0,
cols = my36colors,#颜色
direction = "horizontal", #水平作图
x.lab = '''', y.lab = '''')#横纵轴不标记任何东西
+theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())#不显示坐标刻度
4. Dotplot图
DotPlot(PBMC, features = markers)+coord_flip()+theme_bw()+#去除背景，旋转图片
theme(panel.grid = element_blank(),
axis.text.x=element_text(angle=90,hjust = 1,vjust=0.5))+#文字90度呈现
scale_color_gradientn(values = seq(0,1,0.2),ccolours = c(''#330066'',''#336699'',''#66CC66'',''#FFCC33''))#颜色渐变设置
+labs(x=NULL,y=NULL)+guides(size=guide_legend(order=3))
5. Featureplot图
color <- c('lightgrey', 'blue','seagreen2')#设置颜色
plot8 <- FeaturePlot(scedata, features = 'ACKR1',cols = color, pt.size = 1)+
theme(panel.border = element_rect(fill=NA,color="black", size=1, linetype="solid"))#加边框
cols = c("gray", "coral2")#这个更好看一些，这个也可以参照顶刊用图，改颜色
plot9 <- FeaturePlot(scedata, features = 'ACKR1',cols = cols, pt.size = 1)+
theme(panel.border = element_rect(fill=NA,color="black", size=1, linetype="solid"))#加边框
plot_grid(plot8,plot9)
6. 堆积图
table(macro$orig.ident)#查看各组细胞数
prop.table(table(Idents(macro)))
table(Idents(macro), macro$orig.ident)#各组不同细胞群细胞数
Cellratio <- prop.table(table(macro$celltype, macro$orig.ident), margin = 2)#计算各组样本不同细胞群比例
Cellratio
Cellratio <- as.data.frame(Cellratio)
colourCount = length(unique(Cellratio$Var1))
library(ggplot2)
plot1 <- ggplot(Cellratio) +
geom_bar(aes(x =Var2, y= Freq, fill = Var1),stat = "identity",width = 0.7,size = 0.5,colour = '#123455')+
theme_classic() +
labs(x='Sample',y = 'Ratio')+
coord_flip()+
scale_fill_manual(values = c("#E64B35FF","4DBBD55FF",'#00A087FF','#3C5488FF','#F39B7FFF'))#nature配图
plot1
获取顶刊配色--**pal **
pal_npg(palette = c("nrc"), alpha = 1)(8)
[1] "#E64B35FF" "#4DBBD5FF" "#00A087FF" "#3C5488FF" "#F39B7FFF" "#8491B4FF" "#91D1C2FF" "#DC0000FF"
#其他期刊获取同理
pal_aaas()
pal_jama()(7)
pal_nejm()
pal_lancet()
...
使用 scales 包可视化颜色
install.packages("scales")
devtools::install_github("r-lib/scales")
这个包里面有个函数：show_col()
show_col(pal_npg(palette = c("nrc"), alpha = 1)(8))
这样就可以知道每个颜色是什么样的
