Convenience wrapper for Wilcoxon rank-sum test — compute

Performs Wilcoxon rank-sum tests (Mann-Whitney U test) comparing two groups for each row (or column) of a tidymatrix. This is a non-parametric alternative to the t-test that doesn't assume normal distribution.

Usage

compute_wilcox(
  .data,
  group_col,
  control = NULL,
  treatment = NULL,
  log2 = TRUE,
  adjust = "fdr",
  add_to_data = FALSE,
  prefix = NULL,
  return_tibble = TRUE,
  ...
)

Arguments

.data: A tidymatrix object with rows or columns active
group_col: Character. Name of column in metadata containing group labels
control: Character. Label for the control group. If NULL, it is inferred: when group_col is a factor, the first level present in the data; otherwise the first value in sorted order. If only treatment is given, control is the other group.
treatment: Character. Label for the treatment group. If NULL, the group that is not control. When either group is inferred, a message reports the comparison being made. Set both explicitly to silence it.
log2: Logical. If TRUE (default), computes log2 fold change. If FALSE, computes raw fold change
adjust: Character. Method for p-value adjustment. Default "fdr". See ?p.adjust for options. Use "none" for no adjustment
add_to_data: Logical. If TRUE, adds results to row_data/col_data and returns modified tidymatrix. If FALSE (default), returns data.frame
prefix: Character. Optional prefix for result column names when add_to_data = TRUE
return_tibble: Logical. If TRUE (default), returns tibble. If FALSE, returns data.frame. Only applies when add_to_data = FALSE
...: Additional arguments passed to wilcox.test()

Value

A data.frame/tibble with columns: identifiers, p.value, log2fc (or fc), median_diff, and p.adj (if adjustment applied)

Examples

mat <- matrix(rnorm(100, mean = 10), nrow = 10, ncol = 10)
col_data <- data.frame(
  sample = paste0("S", 1:10),
  condition = rep(c("Control", "Treatment"), each = 5)
)
row_data <- data.frame(gene = paste0("Gene", 1:10))
tm <- tidymatrix(mat, row_data, col_data)

# Wilcoxon test
results <- tm |>
  activate(rows) |>
  compute_wilcox(group_col = "condition")
#> Comparing condition: treatment = 'Treatment' vs control = 'Control'