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Pseudobulk DESeq2: pairwise contrasts

pseudobulk_pairwise.Rd

For each ordered pair of contrast levels in vals_test, fits a DESeq2 model on just those two groups of pseudobulks and returns the foreground-vs-background coefficient. This is more conservative than one-vs-all because a marker has to differentiate the foreground from every other group, not just the average background. Cost grows as O(N^2) in the number of levels, so subset to the levels of interest first.

Usage

pseudobulk_pairwise(
  dge_formula,
  counts_df,
  meta_data,
  contrast_var,
  vals_test,
  verbose,
  min_counts_per_sample,
  present_in_min_samples
)

Arguments

dge_formula

One-sided formula such as ~cluster + donor. The first term is treated as the contrast variable in "one_vs_all" and "pairwise" modes, or as the split variable in "within" mode (in which case the second term becomes the contrast variable). Additional terms are kept as covariates in the DESeq2 design.

counts_df

Feature-by-pseudobulk integer count matrix. Rows are features; columns must align with rows of meta_data.

meta_data

data.frame of pseudobulk metadata. One row per pseudobulk; should contain only the variables used in dge_formula.

contrast_var

Name of the contrast column in meta_data (the first term of dge_formula).

vals_test

Character vector of contrast levels to test. If NULL (default), every level of the contrast variable is tested.

verbose

Logical. Print progress messages. Default TRUE.

min_counts_per_sample

Minimum count per pseudobulk for a gene to be considered expressed in that pseudobulk. Default 10.

present_in_min_samples

Minimum number of pseudobulks in which a gene must reach min_counts_per_sample to be retained. Default 5.

Value

data.frame of DESeq2 results with columns group1, group2, feature, baseMean, log2FoldChange, lfcSE, stat, pvalue, padj. Each row reports the test where pseudobulks of group1 are the foreground and pseudobulks of group2 are the background.

Details

Most users should call pseudobulk_deseq2() with mode = "pairwise" rather than this function directly. The companion summarize_dge_pairs() collapses the directional pairs to a single row per (gene, group).

See also

pseudobulk_deseq2(), pseudobulk_one_vs_all(), pseudobulk_within(), summarize_dge_pairs()