Batch Wilcoxon Rank-Sum Test Between Two Groups

Performs Wilcoxon rank-sum tests (Mann-Whitney U tests) to compare the distribution of specified features between two groups. Computes p-values, adjusts for multiple testing, and ranks features by significance.

Usage

batch_wilcoxon(
  data,
  target = "group",
  feature = NULL,
  feature_manipulation = FALSE
)

Arguments

data

Data frame containing the dataset for analysis.

target

Character string specifying the column name of the grouping variable. Default is `"group"`.

feature

Character vector specifying feature names to analyze. If `NULL`, prompts for selection (interactive mode only). Default is `NULL`.

feature_manipulation

Logical indicating whether to apply feature manipulation filtering. Default is `FALSE`.

Value

Tibble with columns:

sig_names

Feature name

p.value

Raw p-value

statistic

Difference in means between groups

p.adj

Adjusted p-value (Benjamini-Hochberg)

log10pvalue

Negative log10-transformed p-value

stars

Significance stars: **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05, + p<0.5

group1, group2

Mean values for each group

Author

Dongqiang Zeng

Examples

# Create small example data
set.seed(123)
test_data <- data.frame(
  Gender = rep(c("Male", "Female"), each = 50),
  Signature1 = rnorm(100),
  Signature2 = rnorm(100)
)
# Compare features by gender
res <- batch_wilcoxon(
  data = test_data,
  target = "Gender",
  feature = c("Signature1", "Signature2")
)
#> ℹ Groups: "Male" and "Female"
#> ℹ Features: 2
#> ✔ Wilcoxon test complete
head(res)
#> # A tibble: 2 × 8
#>   sig_names  p.value Female    Male statistic p.adj log10pvalue stars
#>   <chr>        <dbl>  <dbl>   <dbl>     <dbl> <dbl>       <dbl> <fct>
#> 1 Signature2   0.128 0.0388 -0.254      0.293 0.257       0.891 +    
#> 2 Signature1   0.484 0.146   0.0344     0.112 0.484       0.315 +