high_var_fea: Identify High-Variance Features from Statistical Results

In IOBR: Immune Oncology Biological Research

Identify High-Variance Features from Statistical Results

Description

Selects top variable (up- and down-regulated) features based on adjusted p-value and log fold-change thresholds.

Usage

high_var_fea(
  result,
  target,
  name_padj = "padj",
  padj_cutoff = 1,
  name_logfc,
  logfc_cutoff = 0,
  n = 10,
  data_type = NULL
)

Arguments

result	Data frame or tibble. Statistical results containing feature, adjusted p-value, and logFC columns.
target	Character. Column name of feature identifiers.
name_padj	Character. Adjusted p-value column name. Default is '"padj"'.
padj_cutoff	Numeric. Adjusted p-value threshold. Default is 1.
name_logfc	Character. log2 fold-change column name.
logfc_cutoff	Numeric. Absolute log2 fold-change threshold. Default is 0.
n	Integer. Number of top up and top down features to select. Default is 10.
data_type	Character or 'NULL'. If '"survival"', adjusts logFC interpretation. Default is 'NULL'.

Value

Character vector of selected feature names (combined up and down sets).

Author(s)

Dongqiang Zeng

Examples

result_data <- data.frame(
  gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"),
  padj = c(0.01, 0.02, 0.05, 0.001, 0.03),
  logfc = c(-2, 1.5, -3, 2.5, 0.5)
)
high_var_fea(
  result = result_data,
  target = "gene",
  name_padj = "padj",
  name_logfc = "logfc",
  n = 2,
  padj_cutoff = 0.05,
  logfc_cutoff = 1.5
)

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