Identify Outlier Samples in Gene Expression Data — find_outlier

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

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)
}
#> ℹ When yinter = -3
#> ℹ Potential outliers: 
#> character(0)