find_outlier_samples: Identify Outlier Samples in Gene Expression Data

In IOBR: Immune Oncology Biological Research

Identify Outlier Samples in Gene Expression Data

Description

Analyzes gene expression data to identify potential outlier samples using connectivity analysis via the WGCNA package. Calculates normalized adjacency and connectivity z-scores for each sample, generates connectivity plots, and optionally performs hierarchical clustering.

Usage

find_outlier_samples(
  eset,
  yinter = -3,
  project = NULL,
  plot_hculst = FALSE,
  show_plot = TRUE,
  index = NULL,
  save = FALSE
)

Arguments

eset	Numeric matrix. Gene expression data with genes as rows and samples as columns.
yinter	Numeric. Z-score threshold for identifying outliers. Default is -3.
project	Character or 'NULL'. Output directory path for saving plots. Required if 'save = TRUE'. Default is 'NULL'.
plot_hculst	Logical. Whether to plot hierarchical clustering. Default is 'FALSE'.
show_plot	Logical. Whether to display the connectivity plot. Default is 'TRUE'.
index	Integer or 'NULL'. Index for output file naming. Default is 'NULL'.
save	Logical. Whether to save plots to files. Default is 'FALSE'.

Value

Character vector of sample names identified as potential outliers.

Author(s)

Dongqiang Zeng

Examples

# Simulate data
set.seed(123)
sim_eset <- matrix(rnorm(100 * 10), 100, 10)
rownames(sim_eset) <- paste0("Gene", 1:100)
colnames(sim_eset) <- paste0("Sample", 1:10)

# Add one extreme outlier
sim_eset[, 10] <- sim_eset[, 10] + 50

# Identify outliers
if (requireNamespace("WGCNA", quietly = TRUE)) {
  outs <- find_outlier_samples(eset = sim_eset, show_plot = FALSE)
  print(outs)
}

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