发表级ggplot绘图流程和技巧 | 论文发表

 

2024年08月27日

好用的color theme：https://github.com/nanxstats/ggsci 

 

2023年10月18日

barplot截取y轴，非常规操作。

You could try, with library(scales):

+ scale_y_continuous(limits=c(2000,2500),oob = rescale_none)

 

2023年09月20日

Seurat的violin plot迅速加mean

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library(ggpubr) median.stat <- function(x){     out <- quantile(x, probs = c(0.5))     names(out) <- c("ymed")     return(out) } # single plot VlnPlot(clustered_colon, features = c("EZH2")) +     stat_summary(fun.y = median.stat, geom='point', size = 5, colour = "black", shape = 21) # or for multiple plots VlnPlot(clustered_colon, features = c("EZH2")) &     stat_summary(fun.y = median.stat, geom='point', size = 5, colour = "black", shape = 21)

 

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options(repr.plot.width=8, repr.plot.height=5) VlnPlot(GC.IM.seuset, features = c("JARID2_imputation", "EZH2_imputation","SOX9_imputation"), group.by = "source") &     stat_summary(fun.y = median.stat, geom='point', size = 5, colour = "black", shape = 21) &     stat_compare_means(comparisons = list(c("Normal","Cancer")), label.y = 0.20,                        label = "p.signif", method = "wilcox.test")

 

2023年05月05日

最常用的themes

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1 2	annotate("text", label = NES, parse = T, x = 10000, y = 0.2, size = 6,              colour = "black", fontface = "plain")

　　

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options(repr.plot.width=4, repr.plot.height=4) labs(x = "Log2 fold change",y = "-Log10(P-value)", title = my_title) + # geom_hline(aes(yintercept=1), colour="grey50", linetype="dashed", size=0.2) + # geom_vline(aes(xintercept=0.5), colour="red", linetype="dashed", size=0.2) + scale_color_manual(values=c("#619CFF", "lightgrey", "#F8766D")) + scale_fill_manual(values=c("blue", "lightgrey", "red")) + scale_y_continuous(position="left", breaks = seq(0, 15, by = 5), limits=c(0, 15)) +     geom_text_repel(data=mark_data, aes(label=gene), size=5, fontface="italic",                     arrow = arrow(ends="first", length = unit(0.01, "npc")), box.padding = 0.2,                     point.padding = 0.3, segment.color = 'black', segment.size = 0.3, force = 1, max.iter = 3e3) +     annotate("text", label = "1509 DOWN", x = -3.5, y = 14, size = 4, colour = "#619CFF", fontface = "bold")

　　

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cns_theme <- theme_classic() +             # theme_bw() +             # theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),             #      panel.border = element_rect(size=0.8, colour = "black")) +             theme(axis.title =element_text(size = 13, color = "black", face = "bold"),                   axis.text =element_text(size = 13, color = "black", face = "bold"),                   plot.title =element_text(hjust = 0.5, size = 16, color = "black", face = "bold")) +             # theme(legend.title=element_blank(), legend.key.size = unit(0.8, 'lines')) +             theme(legend.position = "none")

 

 

2022年09月01日

现在基本的图形已经得心应手了。

问题是如何配色，使图形更加fancy，组合图如何美观统一。【向CNS靠齐，最少也要以CNS子刊的标准来出图】

参考：

• 要想文章好，图片少不了，22个技能助您获得美图（带字幕视频） - AI教程 【字体线条一旦不统一，出图将会非常丑】

 

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我的私人库：https://github.com/leezx/FigureCode

The R Graph Gallery - R绘图代码库、案例大全

themes - https://www.r-graph-gallery.com/ggplot2-package.html#themes

 

要开始修饰以前的核心图片，准备发表论文了。

把之前比较raw的图修饰格式，统一生成高清晰图片，准备放入paper中。

会慢慢补充所有常见的绘图代码。

 

一个raw image的代码

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p1 <- ggplot(oxidation.df, aes(x=group, y=score, color=group)) +         geom_boxplot() +         geom_jitter(shape=16, position=position_jitter(0.2)) +         labs(title = "Fatty acid metabolism") p1

　　

第一步：修改df里的标签

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oxidation.df$group <- plyr::mapvalues(oxidation.df$group,                                       from = c("GFP- early","GFP+ early","GFP- late","GFP+ late"),                                       to = c("HhOFF early", "HhON early", "HhOFF late", "HhON late"))

　　

第二步：修改标签顺序

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1	oxidation.df$group <- factor(oxidation.df$group, levels = c("HhOFF early", "HhON early", "HhOFF late", "HhON late"))

　　

第三步：精修格式主题字体

主题

常用的主题：https://www.r-graph-gallery.com/ggplot2-package.html#themes

• theme_bw - 去掉了灰白背景，加了边框，最常用
• theme_classic - 只留下了加粗的左下边框，最经典，适合实验图
• egg::theme_article - 只有四周的边框，最适合发表文章，缺点：图例间隔太小
• theme_minimal - 只留下了grid，没有边框
• theme_minimal_hgrid - 只留下了hgrid
• theme_void - 只留下了图例，适合tSNE图

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1	theme_bw()

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1	theme_void()

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1 2	# remove grid theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

　

修改title

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1	labs(x = "", y = "Pathway score\n", title = "Fatty acid metabolism")

xy轴标签字体大小

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theme(axis.text.x  = element_text(face="plain", angle=30, size = 14, color = "black", vjust=0.6),         axis.text.y  = element_text(size = 10),         axis.title.y = element_text(size = 14))

去掉多余的图例

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1	theme(legend.position = "none")

 

填充颜色

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1	library(RColorBrewer)

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1 2	scale_fill_manual(values=brewer.pal(9,"Paired")) scale_color_manual(values=brewer.pal(9,"Paired")[c(3,4,5,6)])

 

其他细节

限制xy坐标范围

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1 2	scale_x_continuous(limits = c(0,2.5)) scale_y_continuous(limits = c(0,2.5))

　　

一组数据的比较

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1 2	library(ggpubr) stat_compare_means(label.y = 2.3, label.x = 1, size=5)

　　

多组数据的比较

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library(ggpubr) my_comparisons <- list(c("HhOFF early", "HhON early"), c("HhOFF late", "HhON late"))   stat_compare_means(method = "anova", label.y = 1.29) + # global stat_compare_means(comparisons = my_comparisons, label.y = 1, label = "p.signif") + # paired scale_y_continuous(limits = c(-0.52, 1.3))

　　

代码汇总

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tmp$group <- plyr::mapvalues(tmp$group, from = c("GFP- early","GFP+ early","GFP- late","GFP+ late"),                                       to = c("HhOFF early", "HhON early", "HhOFF late", "HhON late"))   tmp$group <- factor(tmp$group, levels = c("HhOFF early", "HhON early", "HhOFF late", "HhON late"))   library(ggpubr) my_comparisons <- list(c("HhOFF early", "HhON early"), c("HhOFF late", "HhON late"))   options(repr.plot.width=4, repr.plot.height=4) p1 <- ggplot(tmp, aes(x=group, y=score, color=group)) +         geom_boxplot() +         theme_bw() +         labs(x = "", y = "Pathway score\n", title = "Fatty acid metabolism") +         geom_jitter(shape=16, position=position_jitter(0.2)) +         theme(legend.position = "none") +         theme(axis.text.x  = element_text(face="plain", angle=30, size = 14, color = "black", vjust=0.6),         axis.text.y  = element_text(size = 10),         axis.title.y = element_text(size = 14)) +         # scale_fill_manual(values=brewer.pal(9,"Paired"))         scale_color_manual(values=brewer.pal(9,"Paired")[c(3,4,5,6)]) +         stat_compare_means(method = "anova", label.y = 1.29) + # global         stat_compare_means(comparisons = my_comparisons, label.y = 1, label = "p.signif") + # paired         scale_y_continuous(limits = c(-0.52, 1.3)) p1

　　

多图拼接

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1 2	options(repr.plot.width=8, repr.plot.height=9) cowplot::plot_grid(p1,p2,p3,p4,ncol = 2)

　　

PDF出图

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1	ggsave(filename = "HhOFF HhON metabolic pathways.pdf", width = 8, height = 9)

　　

有些图不能这么保存，比如heatmap，这时就要用到pdf函数

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# traditional save pdf("manuscript/HSCR.cluster.heatmap.pdf", width=8, height=7) p dev.off()

　　

lnkscape里修改文字【对齐，上下标、斜体等等】

 

结果

OK, 一个准发表级的图就制作好了，可能还需要精修。

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其余细节

 

点的类型

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1 2	# change the border of point geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)

 

把点拟合成线

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1	stat_smooth(method = "loess", size = 1.1, se = F, span = 0.2)

 

在散点图边缘显示其分布，需要一个额外的包ggExtra，非常简单

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1	ggMarginal(p, type="histogram", fill="black", color=NA, size=7, xparams = list(bins=200), yparams = list(bins=200))

　　

散点图显示mean点

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1	stat_summary(fun.y=mean, geom="point", shape=20, size=7, color="black", fill="black") +

 

线

直线

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1	geom_hline(yintercept=20, linetype="dashed", color = "red", size=2)

线段

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1	geom_segment(data=seg_1, aes(x=x,y=y,xend=xend,yend=yend), arrow=arrow(length=unit(0.3,"cm")), size=1)

矩形

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1	geom_rect(data=AS_1, aes(xmin=EXONSTART, xmax=EXONEND,ymin= -0.1,ymax=0.1),fill="#4DAF4A")

 

图例，比如改legend title，改点大小，去掉图例

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1	labs(x = "\nTranscriptional level",y = "\nPost-transcriptional level", title = "", color = "Clinical score")

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1 2	# change legend dot size guides(colour = guide_legend(override.aes = list(size=10)))

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1 2	# ggplot remove legend title theme(legend.title = element_blank())

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1 2	# position theme(legend.text = element_text(size = 12), legend.position = c(0.8, 0.75))

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1 2	# remove legend background theme(legend.background=element_blank())

去掉legend的白色背景布，一步到位

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1 2	theme(legend.title = element_blank(), legend.text = element_text(size = 11), legend.position = c(0.85, 0.15),           legend.background = element_blank())

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1 2	# 改变图例元素的顺序 scale_fill_manual(values=brewer.pal(3,"Set1")[c(3,2,1)], breaks=c("H3K9Ac", "H4K8Ac", "H3K27Ac"))

 

标题格式，比如居中

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1	theme(plot.title = element_text(hjust = 0.5, size = 18))

　　

去掉边框，轴线，刻度；去掉右上边框

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# empty border, ticks, text theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_blank()) + labs(x = "",y = "", title = "") + theme(axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank())

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# remove top and right border theme(axis.line = element_line(colour = "black"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), panel.background = element_blank())

　

坐标轴，比如修改起点，范围

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1 2	# force y start from 0 scale_y_continuous(expand = c(0, 0), limits = c(0, NA))

　　

去掉画布中的网格线条

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1 2	# just remove inside grid theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

　　

添加文本

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1 2	# add text annotate("text", label = "Wilcoxon test\nP-value = 1.48e-12", x = 0.5, y =2, size = 6, colour = "black")

 

添加背景色

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1 2	# add background color to mark different region geom_rect(xmin=0, xmax=2.5, ymin=-2, ymax=-1, fill="#4DAF4A", alpha=1, color=NA)

　　

修改填充颜色

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1 2	# color scale_color_manual(values=brewer.pal(9,"Set1")[c(1:5,7:9)])

 

精准控制圈图的两种alpha，比如venn图

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1 2	scale_color_manual(values = sample.colors) + scale_fill_manual(values = alpha(sample.colors, .2))

　　

快速统计分析

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# quick statistic testing # Wilcoxon test wilcox.test(subset(time.df,GeneSet=="Common risk")$Time, subset(time.df,GeneSet=="L-HSCR specific")$Time, alternative = "two.sided")

 

# packages
# significance
https://github.com/const-ae/ggsignif

数据的基本处理

分组计算，如取mean，单列

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# quick data process # get group mean weather %>% group_by(city) %>% summarise(mean_temperature = mean(temperature))

 

分组取mean，多列　　

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d <- read.table(text= 'Name     Month  Rate1     Rate2 Aira       1      12        23 Aira       2      18        73 Aira       3      19        45 Ben        1      53        19 Ben        2      22        87 Ben        3      19        45 Cat        1      22        87 Cat        2      67        43 Cat        3      45        32', header=TRUE)   aggregate(d[, 3:4], list(d$Name), mean)     Group.1    Rate1    Rate2 1    Aira 16.33333 47.00000 2     Ben 31.33333 50.33333 3     Cat 44.66667 54.00000

　　

小数点保留，科学计数法

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# format decimals formatC(0.46, format = "e", digits = 1) library(scales) scientific(0.46, digits = 2)

　　

查看默认的颜色 - 画图的结果数据

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# see the colors in ggplot # To see what colors are used to make your plot you can use function ggplot_build() and then look at data part of this object (in column colour are codes). ggplot_build(p)$data

　　

其他图种

柱状图barplot

改bar的顺序，以及添加边框

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1	geom_bar(stat="identity", alpha=1, position = position_fill(reverse = TRUE), color = "grey50") + # position="fill",

 

配对的箱线图、柱状图、折线图 - 用于比较case和control

data

aes里加一个fill就可以把x轴分组（颜色填充默认就会分组），非常适合case和control的比较。

这里想加点需要用另一个函数geom_dotplot

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lineage lineage.sub stage   S.Score G2M.Score   cc.score     <chr> <fct> <chr> <dbl> <dbl> <dbl> ctrl_AAACCTGAGACATAAC   NP  NPlate  Control -0.8162696  -0.98076576 -0.8162696 ctrl_AAACCTGCAAGTAATG   BP  BP  Control 0.3118349   -0.05584626 0.3118349 ctrl_AAACCTGCATGCTAGT   GP  GP  Control 0.4443853   0.27702244  0.4443853

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# http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/76-add-p-values-and-significance-levels-to-ggplots/ library(ggpubr)   options(repr.plot.width=5, repr.plot.height=4) p <- ggplot(cc.df, aes(x=lineage.sub, y=cc.score, fill=stage)) +   geom_boxplot(position=position_dodge(1)) +   geom_dotplot(binaxis='y', stackdir='center', position = "dodge", dotsize=0.15, binwidth=1/25, binpositions="all") +   theme_bw() +   labs(x = "", y = "Proliferation score\n", title = "") +   theme(axis.text.x  = element_text(face="plain", angle=0, size = 14, color = "black", vjust=0.6),         axis.text.y  = element_text(size = 10),         axis.title.y = element_text(size = 14)) +   scale_fill_manual(values=c("blue","red")) +   stat_compare_means(aes(group = stage), label = "p.signif", label.y = 4) +   theme(legend.title = element_blank()) p

　　

封装好的函数

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ggbarplot(ToothGrowth, x = "dose", y = "len", add = "mean_se",           color = "supp", palette = "jco",           position = position_dodge(0.8))+   stat_compare_means(aes(group = supp), label = "p.signif", label.y = 29) ggline(ToothGrowth, x = "dose", y = "len", add = "mean_se",           color = "supp", palette = "jco")+   stat_compare_means(aes(group = supp), label = "p.signif",                      label.y = c(16, 25, 29))

　　

热图 - 最直观

# heatmap
https://jokergoo.github.io/ComplexHeatmap-reference/book/

热图骚操作

聚类热图怎么按自己的意愿调整分支的顺序？ 

 

平滑热图 - smooth heatmap 

monocle里面的一种热图，很多顶刊都在用，也确实很漂亮。对应函数：plot_pseudotime_heatmap

问题是不够灵活，需要用monocle处理后才行，需要自定义一个处理函数。

 

 

小提琴图marker - 分布

stacked violin plot for visualizing single-cell data in Seurat

参见：mouse/singleCell/case/Kif7_ENCC/Kif7-integration/integration_public_and_Kif7.ipynb 

 

Venn韦恩图/UpSetR图 - 交集

R绘制韦恩图 | Venn图 | UpSetR图

 

Beeswarm Plot 蜂群图 - 序列数据展开

https://github.com/eclarke/ggbeeswarm

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#With different beeswarm point distribution priority dat <- data.frame(x=rep(1:3,c(20,40,80))) dat$y <- rnorm(nrow(dat),dat$x) dat$z <- 1

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ggplot(dat, aes(z,y)) +     geom_beeswarm(size=2,priority='descending', cex=3) +     ggtitle('Descending') +     scale_x_continuous(expand=expansion(add=c(0.5, 0.5)))

　　

我的代码

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set.seed(49) library(ggplot2) library(ggbeeswarm)   pca_HSCR2$z <- 1 pca_HSCR2$pseudotime <- -pca_HSCR2$X2   options(repr.plot.width=6, repr.plot.height=4) ggplot(pca_HSCR2, aes(x=z, y=pseudotime, fill=severity, color=severity)) +     geom_beeswarm(size=1.2,priority='ascending', cex=1.4) +     # ggtitle('ascending') + # Descending     scale_x_continuous(expand=expansion(add=c(0.5, 0.5))) +     coord_flip() +     theme_void() +     scale_color_manual(values=severity.colors)

　　

基因模块在pseudotime表达的line图

参见：mouse/singleCell/case/Kif7_ENCC/Kif7/Kif7_basic_analysis.ipynb

 

火山图/对角线图 - 特殊散点图　　

参考：mouse/singleCell/case/Kif7_ENCC/Kif7-integration/Ezh2_analysis.ipynb

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# prepare data log2FC <- data.frame(gene=rownames(HSCR.DEG.log2FC.df.final), S_log2FC=S.log2FC, L_log2FC=L.log2FC)   # add color label log2FC$color <- "none" log2FC[log2FC$gene %in% c("HDAC1"),]$color <- "red"   # the genes want to be labeled label.genes <- c('RAMP2', 'HEY1', 'STAMBP', 'CCNB1IP1', 'LMOD3', 'NUP107', 'HEY2', 'FOXO1', 'CRLF1', 'ZFP36L2', 'NR2F2', 'TUBB3',                 'ZNF385A', 'TMEM14C', 'FLNA', 'TFAP2A', 'SOX11', 'HDAC1', 'GLI3', 'BCL11A') label.df <- subset(log2FC, gene %in% label.genes)   options(repr.plot.width=4.5, repr.plot.height=5) library(ggplot2) library("ggrepel") # Basic scatter plot ggplot(log2FC, aes(x=S_log2FC, y=L_log2FC, color=color)) + # , color=coregene     geom_hline(yintercept=0) +     geom_vline(xintercept=0) +     geom_abline(intercept = 0, slope = 1, color="black", linetype="dashed", size=1) +     geom_point(size=0.5) +     geom_point(data = label.df, size=2, color = "red") +     theme_bw() +     labs(x = "\nLog2FC in S-HSCR",y = "Log2FC in L-HSCR", title = "") +     theme(legend.title=element_blank()) +     # Change fontface. Allowed values : 1(normal), 2(bold), 3(italic), 4(bold.italic)     geom_text_repel(data=label.df, aes(label = gene), size = 3.5, fontface=3, color="red",                     box.padding = 0.4, max.overlaps = Inf) +     theme(legend.position = "none",         axis.text  = element_text(size = 10),         # axis.text.y  = element_text(size = 10),         axis.title = element_text(size = 16, face="plain")) +     scale_x_continuous(limits = c(-8, 8)) +     scale_y_continuous(limits = c(-8, 8)) +     scale_color_manual(values=c("grey","red"))

　　

点的文本标记

geom_text_repel，基本用法

 

 

 

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进阶篇 - 风格统一

为什么顶刊的图那么的赏心悦目？而自己的图拼到一起却那么的不和谐，都被自己丑哭了，却不知从何下手。

 

这里有几个教程还不错：

• 文章用图的修改和排版