high_var_fea: Extract most variable features form statistical results

In IOBR/IOBR: Immune Oncology Biological Research

Extract most variable features form statistical results

Description

This function is designed to identify high variability features (genes or markers) based on certain criteria from a given dataset. It takes as input the dataset and several parameters, such as the significance cutoff for adjusted p-values, the fold change cutoff for log-fold changes, the number of top variables to select, and the data type. The function then filters the dataset based on these criteria and selects the top variable features that meet the specified conditions.

Usage

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

Arguments

result	a tibble or data frame
target	The column name of the target variable in the dataset.
name_padj	The column name representing the adjusted p-value in the dataset. Default is "padj".
padj_cutoff	The significance cutoff for adjusted p-values. Values below this threshold are considered significant. Default is 1.
name_logfc	he column name representing the log-fold change in the dataset. Default is NULL.
logfc_cutoff	The fold change cutoff for log-fold changes. Values above or below this threshold are considered significant. Default is 0.
n	The number of top variable features to select. Default is 10.
data_type	The type of data being analyzed. Options include "survival" and other relevant data types. Default is NULL.

Author(s)

Dongqiang Zeng

IOBR/IOBR documentation built on May 5, 2024, 2:34 p.m.