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 = "find_outlier_eset",
  plot_hculst = FALSE,
  show_plot = TRUE,
  index = NULL,
  save = TRUE
)

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. Project name for output folder. Default is `"find_outlier_eset"`.
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 `TRUE`.

Value

Character vector of sample names identified as potential outliers.

Author

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
outs <- find_outlier_samples(eset = eset_tme_stad, save = FALSE)

#> ℹ When yinter = -3
#> ℹ Potential outliers: "TCGA-BR-4253-01A", "TCGA-BR-6709-01A", "TCGA-CD-8531-01A", "TCGA-RD-A8N2-01A", and "TCGA-VQ-AA69-01A"
print(outs)
#> [1] "TCGA-BR-4253-01A" "TCGA-BR-6709-01A" "TCGA-CD-8531-01A" "TCGA-RD-A8N2-01A"
#> [5] "TCGA-VQ-AA69-01A"