Autocompute P-value Positions For Plotting Significance

Source: R/get_pvalue_position.R

Compute p-value x and y positions for plotting significance levels. Many examples are provided at :

get_y_position(
  data,
  formula,
  fun = "max",
  ref.group = NULL,
  comparisons = NULL,
  step.increase = 0.12,
  y.trans = NULL,
  stack = FALSE,
  scales = c("fixed", "free", "free_y")
)

add_y_position(
  test,
  fun = "max",
  step.increase = 0.12,
  data = NULL,
  formula = NULL,
  ref.group = NULL,
  comparisons = NULL,
  y.trans = NULL,
  stack = FALSE,
  scales = c("fixed", "free", "free_y")
)

add_x_position(test, x = NULL, group = NULL, dodge = 0.8)

add_xy_position(
  test,
  x = NULL,
  group = NULL,
  dodge = 0.8,
  stack = FALSE,
  fun = "max",
  step.increase = 0.12,
  scales = c("fixed", "free", "free_y"),
  ...
)

Arguments

data

a data.frame containing the variables in the formula.

formula

a formula of the form x ~ group where x is a numeric variable giving the data values and group is a factor with one or multiple levels giving the corresponding groups. For example, formula = TP53 ~ cancer_group.

fun

summary statistics functions used to compute automatically suitable y positions of p-value labels and brackets. Possible values include: "max", "mean", "mean_sd", "mean_se", "mean_ci", "median", "median_iqr", "median_mad".

For example, if fun = "max", the y positions are guessed as follow:

  • 1. Compute the maximum of each group (groups.maximum)

  • 2. Use the highest groups maximum as the first bracket y position

  • 3. Add successively a step increase for remaining bracket y positions.

When the main plot is a boxplot, you need the option fun = "max", to have the p-value bracket displayed at the maximum point of the group.

In some situations the main plot is a line plot or a barplot showing the mean+/-error bars of the groups, where error can be SE (standard error), SD (standard deviation) or CI (confidence interval). In this case, to correctly compute the bracket y position you need the option fun = "mean_se", etc.

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).

comparisons

A list of length-2 vectors specifying the groups of interest to be compared. For example to compare groups "A" vs "B" and "B" vs "C", the argument is as follow: comparisons = list(c("A", "B"), c("B", "C"))

step.increase

numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.

y.trans

a function for transforming y axis scale. Value can be log2, log10 and sqrt. Can be also any custom function that can take a numeric vector as input and returns a numeric vector, example: y.trans = function(x){log2(x+1)}

stack

logical. If TRUE, computes y position for a stacked plot. Useful when dealing with stacked bar plots.

scales

Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_y")?. This option is considered only when determining the y position. If the specified value is "free" or "free_y", then the step increase of y positions will be calculated by plot panels. Note that, using "free" or "free_y" gives the same result. A global step increase is computed when scales = "fixed".

test

an object of class rstatix_test as returned by t_test(), wilcox_test(), sign_test(), tukey_hsd(), dunn_test().

x

variable on x axis.

group

group variable (legend variable).

dodge

dodge width for grouped ggplot/test. Default is 0.8. Used only when x specified.

...

other arguments to be passed to the function t.test.

Functions

  • get_y_position(): compute the p-value y positions

  • add_y_position(): add p-value y positions to an object of class rstatix_test

  • add_x_position(): compute and add p-value x positions.

  • add_xy_position(): compute and add both x and y positions.

Examples

# Data preparation
#::::::::::::::::::::::::::::::::::::
df <- ToothGrowth
df$dose <- as.factor(df$dose)
df$group <- factor(rep(c(1, 2), 30))
head(df)
#>    len supp dose group
#> 1  4.2   VC  0.5     1
#> 2 11.5   VC  0.5     2
#> 3  7.3   VC  0.5     1
#> 4  5.8   VC  0.5     2
#> 5  6.4   VC  0.5     1
#> 6 10.0   VC  0.5     2

# Stat tests
#::::::::::::::::::::::::::::::::::::
stat.test <- df %>%
  t_test(len ~ dose)
stat.test
#> # A tibble: 3 × 10
#>   .y.   group1 group2    n1    n2 statistic    df        p    p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl>    <dbl> <chr>       
#> 1 len   0.5    1         20    20     -6.48  38.0 1.27e- 7 2.54e- 7 ****        
#> 2 len   0.5    2         20    20    -11.8   36.9 4.4 e-14 1.32e-13 ****        
#> 3 len   1      2         20    20     -4.90  37.1 1.91e- 5 1.91e- 5 ****        

# Add the test into box plots
#::::::::::::::::::::::::::::::::::::
stat.test <- stat.test %>%
  add_y_position()
# \donttest{
 if(require("ggpubr")){
   ggboxplot(df, x = "dose", y = "len") +
     stat_pvalue_manual(stat.test, label = "p.adj.signif", tip.length = 0.01)
  }

# }