如何在小提琴图上显示胡须和点?

编程入门行业动态更新时间:2024-10-25 15:22:06

本文介绍了如何在小提琴图上显示胡须和点?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！问题描述

我有一个包含以下数据的数据框df.我想在A和B两组之间绘制基因的logCPM表达.

I have a dataframe df with the following data. I want to plot the logCPM expression of the gene between two groups A and B.

Samples Type GeneA Sample1 B 14.82995162 Sample2 B 12.90512275 Sample3 B 9.196524783 Sample4 A 19.42866012 Sample5 A 19.70386922 Sample6 A 16.22906914 Sample7 A 12.48966785 Sample8 B 15.53280377 Sample9 A 9.345795955 Sample10 B 9.196524783 Sample11 B 9.196524783 Sample12 B 9.196524783 Sample13 A 9.434355615 Sample14 A 15.27604692 Sample15 A 18.90867329 Sample16 B 11.71503095 Sample17 B 13.7632545 Sample18 A 9.793864295 Sample19 B 9.196524783 Sample20 A 14.52562066 Sample21 A 13.85116605 Sample22 A 9.958492229 Sample23 A 17.57075876 Sample24 B 13.04499079 Sample25 B 15.33577937 Sample26 A 13.95849295 Sample27 B 9.196524783 Sample28 A 18.20524388 Sample29 B 17.7058873 Sample30 B 14.0199393 Sample31 A 16.21499069 Sample32 A 14.171432 Sample33 B 9.196524783 Sample34 B 9.196524783 Sample35 B 15.16648035 Sample36 B 12.9435081 Sample37 B 13.81971106 Sample38 B 15.82901231

我尝试使用ggviolin制作小提琴图.

I tried making a violin plot using ggviolin.

library("ggpubr") pdf("eg.pdf", width = 5, height = 5) p <- ggviolin(df, x = "Type", y = "GeneA", fill = "Type", color = "Type", palette = c("#00AFBB", "#FC4E07"), add="boxplot",add.params = list(fill="white"), order = c("A", "B"), ylab = "GeneA (logCPM)", xlab = "Groups") ggpar(p, ylim = c(5,25)) dev.off()

我得到了这样的小提琴图.

I got the violin plot like this .

1)在此我看不到任何胡须和小提琴上的任何点.

1) In this I don't see any whiskers and any points on violin.

2)有没有一种方法可以显示哪个点是哪个样本?就像给该点赋予不同的颜色(例如:我对示例10感兴趣.我想给该点赋予不同的颜色，因为我希望看到该颜色的表达)

2) Is there a way to show which point is which sample? like giving a different color to the point (for eg: I'm interested in Sample 10. I want to give different color to that point because I'm interested to see the expression of that)

谢谢

推荐答案

我可以建议使用 elephant/raincloud 或 hybrid boxplot 图代替了吗?

May I suggest using elephant/raincloud or hybrid boxplot plots instead?

来自上面链接的博客文章:

From the blog post linked above:

小提琴图完全无趣/无信息地反映了数据密度，只是为了视觉美观而重复相同的精确信息.

Violin plots mirror the data density in a totally uninteresting/uninformative way, simply repeating the same exact information for the sake of visual aesthetic.

在Raincloud图中，我们基本上得到了所需的一切:眼动的统计推断，数据分布的评估(用于检查假设)以及原始数据本身显示异常值和基础模式.

In raincloud plot, we get basically everything we need: eyeballed statistical inference, assessment of data distributions (useful to check assumptions), and the raw data itself showing outliers and underlying patterns.

library(tidyverse) library(ggrepel) df <- read_table2(txt) # create new variable for coloring & labeling `Sample10` pts df <- df %>% mutate(colSel = ifelse(Samples == 'Sample10', '#10', 'dummy'), labSel = ifelse(Samples == 'Sample10', '#10', '')) # create summary statistics sumld <- df %>% group_by(Type) %>% summarise( mean = mean(GeneA, na.rm = TRUE), median = median(GeneA, na.rm = TRUE), sd = sd(GeneA, na.rm = TRUE), N = n(), ci = 1.96 * sd/sqrt(N), lower95 = mean - ci, upper95 = mean + ci, lower = mean - sd, upper = mean + sd) %>% ungroup() sumld #> # A tibble: 2 x 10 #> Type mean median sd N ci lower95 upper95 lower upper #> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 A 14.7 14.5 3.54 17 1.68 13.0 16.3 11.1 18.2 #> 2 B 12.4 12.9 2.85 21 1.22 11.2 13.6 9.54 15.2

雨云图

## get geom_flat_violin function ## gist.github/benmarwick/b7dc863d53e0eabc272f4aad909773d2 ## mirror: pastebin/J9AzSxtF devtools::source_gist("2a1bb0133ff568cbe28d", filename = "geom_flat_violin.R") pos <- position_jitter(width = 0.15, seed = 1) p0 <- ggplot(data = df, aes(x = Type, y = GeneA, fill = Type)) + geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .8) + guides(fill = FALSE) + guides(color = FALSE) + scale_color_brewer(palette = "Dark2") + scale_fill_brewer(palette = "Dark2") + theme_classic() # raincloud plot p1 <- p0 + geom_point(aes(color = Type), position = pos, size = 3, alpha = 0.8) + geom_boxplot(width = .1, show.legend = FALSE, outlier.shape = NA, alpha = 0.5) p1

# coloring Sample10 p0 + geom_point(aes(color = colSel), position = pos, size = 3, alpha = 0.8) + geom_text_repel(aes(label = labSel), point.padding = 0.25, direction = 'y', position = pos) + geom_boxplot(width = .1, show.legend = FALSE, outlier.shape = NA, alpha = 0.5) + scale_color_manual(values = c('dummy' = 'grey50', '#10' = 'red'))

# errorbar instead of boxplot p0 + geom_point(aes(color = colSel), position = pos, size = 3, alpha = 0.8) + geom_point(data = sumld, aes(x = Type, y = mean), position = position_nudge(x = 0.3), size = 3.5) + geom_text_repel(aes(label = labSel), point.padding = 0.25, direction = 'y', position = pos) + geom_errorbar(data = sumld, aes(ymin = lower95, ymax = upper95, y = mean), position = position_nudge(x = 0.3), width = 0) + guides(fill = FALSE) + guides(color = FALSE) + scale_color_manual(values = c('dummy' = 'grey50', '#10' = 'red')) + scale_fill_brewer(palette = "Dark2") + theme_classic()

混合箱线图/p>

## stackoverflow/a/49338481/ library(ggpol) half_box <- ggplot(df) + geom_boxjitter(aes(x = Type, y = GeneA, fill = Type, color = Type), jitter.shape = 21, jitter.color = NA, jitter.height = 0, jitter.width = 0.04, outlier.color = NA, errorbar.draw = TRUE) + scale_color_brewer(palette = "Dark2") + scale_fill_brewer(palette = "Dark2") + theme_classic() half_box

奖金:您还可以从 ggbeeswarm 包. 此处是一个示例.

Bonus: you can also replace geom_point() with geom_quasirandom() from the ggbeeswarm package. Here is one example.