find_markers_in_bulk: Identify Marker Features in Bulk Expression Data

In IOBR: Immune Oncology Biological Research

Identify Marker Features in Bulk Expression Data

Description

Identifies informative marker features across groups from bulk gene expression or signature score matrices using Seurat workflows. Performs feature selection, scaling, PCA, clustering, and marker discovery.

Usage

find_markers_in_bulk(
  pdata,
  eset,
  group,
  id_pdata = "ID",
  nfeatures = 2000,
  top_n = 20,
  thresh.use = 0.25,
  only.pos = TRUE,
  min.pct = 0.25,
  npcs = 30
)

Arguments

pdata	Data frame. Sample metadata.
eset	Matrix. Gene expression or signature score matrix.
group	Character. Column name in pdata specifying grouping variable.
id_pdata	Character. Column name for sample IDs. Default is "ID".
nfeatures	Integer. Number of top variable features to select. Default is 2000.
top_n	Integer. Number of top markers to retain per cluster. Default is 20.
thresh.use	Numeric. Threshold for marker selection. Default is 0.25.
only.pos	Logical. Whether to retain only positive markers. Default is TRUE.
min.pct	Numeric. Minimum expression percentage threshold. Default is 0.25.
npcs	Integer. Number of principal components to use. Default is 30.

Value

List with components: 'sce' (Seurat object), 'markers' (all markers), 'top_markers' (top markers per group).

Examples

if (requireNamespace("Seurat", quietly = TRUE) && requireNamespace("Matrix", quietly = TRUE)) {
  # Simulate data
  set.seed(123)
  sim_eset <- matrix(abs(rnorm(100 * 30)), 100, 30)
  rownames(sim_eset) <- paste0("Gene", 1:100)
  colnames(sim_eset) <- paste0("Sample", 1:30)
  
  sim_pdata <- data.frame(
    ID = paste0("Sample", 1:30),
    TMEcluster = rep(c("A", "B", "C"), each = 10)
  )
  
  res <- find_markers_in_bulk(
    pdata = sim_pdata, eset = sim_eset,
    group = "TMEcluster", npcs = 5
  )
  if (!is.null(res)) head(res$top_markers)
}

IOBR documentation built on May 30, 2026, 5:07 p.m.