# Bioconductor Qvalue

This package takes a list of p-values resulting from the simultaneous testing of many...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Testing
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-qvalue -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-qvalue -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-qvalue -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-qvalue -g -a gemini-cli -y`

## Description

This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# qvalue

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page, reproduce in any R environment.

- **Version:** 2.44.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Imports:** ggplot2, reshape2
- **Install:** `BiocManager::install("qvalue")`

## When to Use
- **False Discovery Rate Estimation**: When analyzing thousands of features (e.g., differential gene expression) to estimate q-values and control the false discovery rate using `qvalue`.
- **Estimating True Nulls**: When you need to estimate the overall proportion of true null hypotheses ($\pi_0$) from a distribution of p-values using `pi0est`.
- **Local FDR Calculation**: When you want to calculate the empirical Bayesian posterior probability that a null hypothesis is true, conditional on the observed p-value, using `lfdr`.
- **Empirical P-value Calculation**: When you have observed test-statistics and simulated/resampled null statistics and need to compute p-values using `empPvals`.

## When NOT to Use
- **Dependent Test Statistics**: When tests are strongly dependent or have uncorrected batch effects (indicated by a U-shaped p-value histogram), use the `sva` package first to correct the data.
- **Small-scale hypothesis testing**: For experiments with very few tests, use standard Benjamini-Hochberg adjustment because the estimation of $\pi_0$ is highly unstable with small sample sizes.

## Data Requirements
- A numeric vector of p-values strictly bounded between 0 and 1, OR a vector of observed statistics and a matrix of empirical null statistics.
- P-values must follow a Uniform(0,1) distribution under the null hypothesis (the right tail of the p-value histogram should be fairly flat).

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/general/qvalue/SKILL.md