% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/high_var_fea.R
\name{high_var_fea}
\alias{high_var_fea}
\title{Identify High-Variance Features from Statistical Results}
\usage{
high_var_fea(
  result,
  target,
  name_padj = "padj",
  padj_cutoff = 1,
  name_logfc,
  logfc_cutoff = 0,
  n = 10,
  data_type = NULL
)
}
\arguments{
\item{result}{Data frame or tibble. Statistical results containing feature,
adjusted p-value, and logFC columns.}

\item{target}{Character. Column name of feature identifiers.}

\item{name_padj}{Character. Adjusted p-value column name. Default is `"padj"`.}

\item{padj_cutoff}{Numeric. Adjusted p-value threshold. Default is 1.}

\item{name_logfc}{Character. log2 fold-change column name.}

\item{logfc_cutoff}{Numeric. Absolute log2 fold-change threshold. Default is 0.}

\item{n}{Integer. Number of top up and top down features to select.
Default is 10.}

\item{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).
}
\description{
Selects top variable (up- and down-regulated) features based on adjusted
p-value and log fold-change thresholds.
}
\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
)
}
\author{
Dongqiang Zeng
}
