ggpubr给科研绘图添加统计数据

在本文中，我们将描述如何轻松i）比较两个或多个群体的手段; ii）并自动将p值和显着性水平添加到GGPLOT（例如框图，点图，条形图和线图……）。

内容

必要条件

安装和加载需要R包

Required R package: ggpubr (version >= 0.1.3), for ggplot2-based publication ready plots.

Install from CRAN as follow:

install.packages("ggpubr")

Or, install the latest developmental version from GitHub as follow:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

Load ggpubr:

library(ggpubr)

GGPubr的官方文件可供选择: http://www.sthda.com/english/rpkgs/ggpubr

展示数据组

Data: ToothGrowth data sets.

data("ToothGrowth")
head(ToothGrowth)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

比较means的方法

The standard methods to compare the means of two or more groups in R, have been largely described at: comparing means in R.

比较手段的最常用方法包括:

Methods	R function	Description
T-test	t.test()	Compare two groups (parametric)
Wilcoxon test	wilcox.test()	Compare two groups (non-parametric)
ANOVA	aov() or anova()	Compare multiple groups (parametric)
Kruskal-Wallis	kruskal.test()	Compare multiple groups (non-parametric)

A practical guide to compute and interpret the results of each of these methods are provided at the following links:

Comparing one-sample mean to a standard known mean:
- One-Sample T-test (parametric)
- One-Sample Wilcoxon Test (non-parametric)
Comparing the means of two independent groups:
- Unpaired Two Samples T-test (parametric)
- Unpaired Two-Samples Wilcoxon Test (non-parametric)
Comparing the means of paired samples:
- Paired Samples T-test (parametric)
- Paired Samples Wilcoxon Test (non-parametric)
Comparing the means of more than two groups
- Analysis of variance (ANOVA, parametric):
  - One-Way ANOVA Test in R
  - Two-Way ANOVA Test in R
- Kruskal-Wallis Test in R (non parametric alternative to one-way ANOVA)

R添加p值功能

Here we present two new R functions in the ggpubr package:

compare_means(): easy to use solution to performs one and multiple mean comparisons.
stat_compare_means(): easy to use solution to automatically add p-values and significance levels to a ggplot.

compare_means()

As we’ll show in the next sections, it has multiple useful options compared to the standard R functions.

The simplified format is as follow:

compare_means(formula, data, method = "wilcox.test", paired = FALSE,
  group.by = NULL, ref.group = NULL, ...)

formula: a formula of the form x ~ group, where x is a numeric variable and group is a factor with one or multiple levels. For example, formula = TP53 ~ cancer_group. It’s also possible to perform the test for multiple response variables at the same time. For example, formula = c(TP53, PTEN) ~ cancer_group.
data: a data.frame containing the variables in the formula.
method: the type of test. Default is “wilcox.test”. Allowed values include:
- “t.test” (parametric) and “wilcox.test”” (non-parametric). Perform comparison between two groups of samples. If the grouping variable contains more than two levels, then a pairwise comparison is performed.
- “anova” (parametric) and “kruskal.test” (non-parametric). Perform one-way ANOVA test comparing multiple groups.
paired: a logical indicating whether you want a paired test. Used only in t.test and in wilcox.test.
group.by: variables used to group the data set before applying the test. When specified the mean comparisons will be performed in each subset of the data formed by the different levels of the group.by variables.
ref.group: a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). ref.group can be also “.all.”. In this case, each of the grouping variable levels is compared to all (i.e. base-mean).

stat_compare_means()

This function extends ggplot2 for adding mean comparison p-values to a ggplot, such as box blots, dot plots, bar plots and line plots.

The simplified format is as follow:

stat_compare_means(mapping = NULL, comparisons = NULL hide.ns = FALSE,
                   label = NULL,  label.x = NULL, label.y = NULL,  ...)

mapping: Set of aesthetic mappings created by aes().
comparisons: A list of length-2 vectors. The entries in the vector are either the names of 2 values on the x-axis or the 2 integers that correspond to the index of the groups of interest, to be compared.
hide.ns: logical value. If TRUE, hide ns symbol when displaying significance levels.
label: character string specifying label type. Allowed values include “p.signif” (shows the significance levels), “p.format” (shows the formatted p value).
label.x,label.y: numeric values. coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.
…: other arguments passed to the function compare_means() such as method, paired, ref.group.

比较两个独立的团体

Perform the test:

compare_means(len ~ supp, data = ToothGrowth)

## # A tibble: 1 x 8
##     .y. group1 group2      p  p.adj p.format p.signif   method
##                       
## 1   len     OJ     VC 0.0645 0.0645    0.064       ns Wilcoxon

By default method = “wilcox.test” (non-parametric test). You can also specify method = “t.test” for a parametric t-test.

Returned value is a data frame with the following columns:

.y.: the y variable used in the test.
p: the p-value
p.adj: the adjusted p-value. Default value for p.adjust.method = “holm”
p.format: the formatted p-value
p.signif: the significance level.
method: the statistical test used to compare groups.

Create a box plot with p-values:

p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco",
          add = "jitter")
#  Add p-value
p + stat_compare_means()
# Change method
p + stat_compare_means(method = "t.test")

Note that, the p-value label position can be adjusted using the arguments: label.x, label.y, hjust and vjust.

The default p-value label displayed is obtained by concatenating the method and the p columns of the returned data frame by the function compare_means(). You can specify other combinations using the aes() function.

For example,

aes(label = ..p.format..) or aes(label = paste0(“p =”, ..p.format..)): display only the formatted p-value (without the method name)
aes(label = ..p.signif..): display only the significance level.
aes(label = paste0(..method.., “\n”, “p =”, ..p.format..)): Use line break (“\n”) between the method name and the p-value.

As an illustration, type this:

p + stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 40)

If you prefer, it’s also possible to specify the argument label as a character vector:

p + stat_compare_means( label = "p.signif", label.x = 1.5, label.y = 40)

比较两个配对样本

Perform the test:

compare_means(len ~ supp, data = ToothGrowth, paired = TRUE)

## # A tibble: 1 x 8
##     .y. group1 group2       p   p.adj p.format p.signif   method
##                         
## 1   len     OJ     VC 0.00431 0.00431   0.0043       ** Wilcoxon

Visualize paired data using the ggpaired() function:

ggpaired(ToothGrowth, x = "supp", y = "len",
         color = "supp", line.color = "gray", line.size = 0.4,
         palette = "jco")+
  stat_compare_means(paired = TRUE)

比较超过两个的组

Global test:

# Global test
compare_means(len ~ dose,  data = ToothGrowth, method = "anova")

## # A tibble: 1 x 6
##     .y.        p    p.adj p.format p.signif method
##                     
## 1   len 9.53e-16 9.53e-16  9.5e-16     ****  Anova

绘制添加全局的p值图:

# Default method = "kruskal.test" for multiple groups
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means()
# Change method to anova
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means(method = "anova")

Pairwise comparisons. 如果分组变量包含两个以上级别，则将自动执行成对测试。默认方法是“wilcox.test”。您可以将其更改为“t.test”。

# Perorm pairwise comparisons
compare_means(len ~ dose,  data = ToothGrowth)

## # A tibble: 3 x 8
##     .y. group1 group2        p    p.adj p.format p.signif   method
##                           
## 1   len    0.5      1 7.02e-06 1.40e-05  7.0e-06     **** Wilcoxon
## 2   len    0.5      2 8.41e-08 2.52e-07  8.4e-08     **** Wilcoxon
## 3   len      1      2 1.77e-04 1.77e-04  0.00018      *** Wilcoxon

# Visualize: Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+ 
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 50)     # Add global p-value

If you want to specify the precise y location of bars, use the argument label.y:

ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+ 
  stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+
  stat_compare_means(label.y = 45)

(Adding bars, connecting compared groups, has been facilitated by the ggsignif R package )

Multiple pairwise tests against a reference group:

# Pairwise comparison against reference
compare_means(len ~ dose,  data = ToothGrowth, ref.group = "0.5",
              method = "t.test")

## # A tibble: 2 x 8
##     .y. group1 group2        p    p.adj p.format p.signif method
##                         
## 1   len    0.5      1 6.70e-09 6.70e-09  6.7e-09     **** T-test
## 2   len    0.5      2 1.47e-16 2.94e-16  < 2e-16     **** T-test

# Visualize
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means(method = "anova", label.y = 40)+      # Add global p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = "0.5")                    # Pairwise comparison against reference

Multiple pairwise tests against all (base-mean):

# Comparison of each group against base-mean
compare_means(len ~ dose,  data = ToothGrowth, ref.group = ".all.",
              method = "t.test")

## # A tibble: 3 x 8
##     .y. group1 group2        p    p.adj p.format p.signif method
##                         
## 1   len  .all.    0.5 1.24e-06 3.73e-06  1.2e-06     **** T-test
## 2   len  .all.      1 5.67e-01 5.67e-01     0.57       ns T-test
## 3   len  .all.      2 1.37e-05 2.74e-05  1.4e-05     **** T-test

# Visualize
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means(method = "anova", label.y = 40)+      # Add global p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = ".all.")                  # Pairwise comparison against all

A typical situation, where pairwise comparisons against “all” can be useful, is illustrated here using the myeloma data set available on Github.

We’ll plot the expression profile of the DEPDC1 gene according to the patients’ molecular groups. We want to know if there is any difference between groups. If yes, where the difference is?

To answer to this question, you can perform a pairwise comparison between all the 7 groups. This will lead to a lot of comparisons between all possible combinations. If you have many groups, as here, it might be difficult to interpret.

Another easy solution is to compare each of the seven groups against “all” (i.e. base-mean). When the test is significant, then you can conclude that DEPDC1 is significantly overexpressed or downexpressed in a group xxx compared to all.

# Load myeloma data from GitHub
myeloma <- read.delim("https://raw.githubusercontent.com/kassambara/data/master/myeloma.txt")
# Perform the test
compare_means(DEPDC1 ~ molecular_group,  data = myeloma,
              ref.group = ".all.", method = "t.test")

## # A tibble: 7 x 8
##      .y. group1           group2        p   p.adj p.format p.signif method
##                                   
## 1 DEPDC1  .all.       Cyclin D-1 0.149690 0.44907  0.14969       ns T-test
## 2 DEPDC1  .all.       Cyclin D-2 0.523143 1.00000  0.52314       ns T-test
## 3 DEPDC1  .all.     Hyperdiploid 0.000282 0.00169  0.00028      *** T-test
## 4 DEPDC1  .all. Low bone disease 0.005084 0.02542  0.00508       ** T-test
## 5 DEPDC1  .all.              MAF 0.086107 0.34443  0.08611       ns T-test
## 6 DEPDC1  .all.            MMSET 0.576291 1.00000  0.57629       ns T-test
## # ... with 1 more rows

# Visualize the expression profile
ggboxplot(myeloma, x = "molecular_group", y = "DEPDC1", color = "molecular_group", 
          add = "jitter", legend = "none") +
  rotate_x_text(angle = 45)+
  geom_hline(yintercept = mean(myeloma$DEPDC1), linetype = 2)+ # Add horizontal line at base mean
  stat_compare_means(method = "anova", label.y = 1600)+        # Add global annova p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = ".all.")                      # Pairwise comparison against all

From the plot above, we can conclude that DEPDC1 is significantly overexpressed in proliferation group and, it’s significantly downexpressed in Hyperdiploid and Low bone disease compared to all.

Note that, if you want to hide the ns symbol, specify the argument hide.ns = TRUE.

# Visualize the expression profile
ggboxplot(myeloma, x = "molecular_group", y = "DEPDC1", color = "molecular_group", 
          add = "jitter", legend = "none") +
  rotate_x_text(angle = 45)+
  geom_hline(yintercept = mean(myeloma$DEPDC1), linetype = 2)+ # Add horizontal line at base mean
  stat_compare_means(method = "anova", label.y = 1600)+        # Add global annova p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = ".all.", hide.ns = TRUE)      # Pairwise comparison against all

Multiple grouping variables

Two independent sample comparisons after grouping the data by another variable:

Perform the test:

compare_means(len ~ supp, data = ToothGrowth, 
              group.by = "dose")

## # A tibble: 3 x 9
##    dose   .y. group1 group2       p  p.adj p.format p.signif   method
##                         
## 1   0.5   len     OJ     VC 0.02319 0.0464    0.023        * Wilcoxon
## 2   1.0   len     OJ     VC 0.00403 0.0121    0.004       ** Wilcoxon
## 3   2.0   len     OJ     VC 1.00000 1.0000    1.000       ns Wilcoxon

In the example above, for each level of the variable “dose”, we compare the means of the variable “len” in the different groups formed by the grouping variable “supp”.

Visualize (1/2). Create a multi-panel box plots facetted by group (here, “dose”):

# Box plot facetted by "dose"
p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco",
          add = "jitter",
          facet.by = "dose", short.panel.labs = FALSE)
# Use only p.format as label. Remove method name.
p + stat_compare_means(label = "p.format")

# Or use significance symbol as label
p + stat_compare_means(label =  "p.signif", label.x = 1.5)

To hide the ‘ns’ symbol, use the argument hide.ns = TRUE.

Visualize (2/2). Create one single panel with all box plots. Plot y = “len” by x = “dose” and color by “supp”:

p <- ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "supp", palette = "jco",
          add = "jitter")
p + stat_compare_means(aes(group = supp))

# Show only p-value
p + stat_compare_means(aes(group = supp), label = "p.format")

# Use significance symbol as label
p + stat_compare_means(aes(group = supp), label = "p.signif")

Paired sample comparisons after grouping the data by another variable:

Perform the test:

compare_means(len ~ supp, data = ToothGrowth, 
              group.by = "dose", paired = TRUE)

## # A tibble: 3 x 9
##    dose   .y. group1 group2      p  p.adj p.format p.signif   method
##                        
## 1   0.5   len     OJ     VC 0.0330 0.0659    0.033        * Wilcoxon
## 2   1.0   len     OJ     VC 0.0191 0.0572    0.019        * Wilcoxon
## 3   2.0   len     OJ     VC 1.0000 1.0000    1.000       ns Wilcoxon

Visualize. Create a multi-panel box plots facetted by group (here, “dose”):

# Box plot facetted by "dose"
p <- ggpaired(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco", 
          line.color = "gray", line.size = 0.4,
          facet.by = "dose", short.panel.labs = FALSE)
# Use only p.format as label. Remove method name.
p + stat_compare_means(label = "p.format", paired = TRUE)

Other plot types

Bar and line plots (one grouping variable):

# Bar plot of mean +/-se
ggbarplot(ToothGrowth, x = "dose", y = "len", add = "mean_se")+
  stat_compare_means() +                                         # Global p-value
  stat_compare_means(ref.group = "0.5", label = "p.signif",
                     label.y = c(22, 29))                   # compare to ref.group
# Line plot of mean +/-se
ggline(ToothGrowth, x = "dose", y = "len", add = "mean_se")+
  stat_compare_means() +                                         # Global p-value
  stat_compare_means(ref.group = "0.5", label = "p.signif",
                     label.y = c(22, 29))

Bar and line plots (two grouping variables):

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))