QuEStVar

Quantitative Exploration of Stability and Variability through statistical hypothesis testing

QuEStVar is a testing utility for paired equivalence and difference testing in quantitative omics data. It labels each measured feature as equivalent, not significant, or different using CV filtering, Welch's t-test, TOST equivalence testing, and multiple testing correction.

Quick links

• Quick Start Tutorial -- 5 minute walkthrough with demo data
• Power Analysis Tutorial -- experiment planning and design sweeps
• Configuration Guide -- YAML config, CLI flags, all parameters
• Plotting Guide -- Antler's plot, summary figure, power profile
• API Reference -- full function and class documentation
• Contributing -- development setup and PR workflow

Installation

pip install questvar[plot,yaml]

For the core engine only (no plotting, no YAML):

pip install questvar

Quick example

import questvar as qv
import polars as pl

df = pl.read_csv("data/demo_realistic.tsv", separator="\t")
result = qv.test(df, cond_1=["c1_0","c1_1","c1_2"], cond_2=["c2_0","c2_1","c2_2"])
print(result.summary())
result.plot()