py-qvalue 0.1.1

A Python package providing ports of statistical methods originally implemented in R.

py-qvalue

A Python package for estimating q-values and controlling the False Discovery Rate (FDR), based on the functionalities of the renowned R package qvalue.

Overview

py-qvalue brings key methods from the R qvalue package to the Python ecosystem. It is designed for researchers and analysts who perform multiple hypothesis testing and need robust ways to estimate the proportion of true null hypotheses ($\pi_0$), calculate q-values, and estimate local false discovery rates (lfdr).

This package currently provides Python implementations of the core qvalue and pi0est functions, along with the necessary lfdr calculation, allowing for straightforward FDR control within your Python workflows.

Installation

You can install py-qvalue using pip:

pip install py-qvalue

Quick Start

A basic example demonstrating how to use py-qvalue to calculate q-values from a set of p-values:

import numpy as np
from py_qvalue import qvalue

# Parameters
n_tests = 1000
effect_size = 0.3
null_proportion = 0.9

# Generate null (uniform) and signal (skewed low) components
n_null = int(n_tests * null_proportion)
n_signal = n_tests - n_null

null_p = np.random.uniform(0, 1, n_null)
signal_p = np.random.beta(1, 15, n_signal)  # Stronger signal = more left skew

# Combine and shuffle
p_values = np.concatenate([null_p, signal_p])
np.random.shuffle(p_values)

# Calculate q-values
qvalue_results = qvalue(p_values)

# Access the q-values
q_values = qvalue_results['qvalues']
print("P-values:", p_values[0:10]) # Just the first ten
print("Q-values:", q_values[0:10])

# Access the estimated proportion of true null hypotheses (pi0)
pi0_estimate = qvalue_results['pi0']
print("Estimated pi0:", pi0_estimate)

# Get significant results at a specific FDR level (e.g., 10%)
results_at_fdr = qvalue(p_values, fdr_level=0.10)
significant_mask = results_at_fdr['significant']
print("Significant at 10% FDR:", significant_mask)

Implemented Functions

As a port of qvalue, we use the same core functions includes:

• qvalue(p, fdr_level=None, pfdr=False, lfdr_out=True, pi0=None, **kwargs): Estimates q-values and related FDR quantities from a set of p-values.
• pi0est(p, lambda_=None, pi0_method="smoother", smooth_df=3, smooth_log_pi0=False, **kwargs): Estimates the proportion of true null hypotheses ($\pi_0$).
• lfdr(p, pi0, trunc=True, monotone=True, transf="probit", adj=1.5, eps=1e-8, **kwargs): Estimates local false discovery rates (lfdr).

Users can import qvalue and pi0est from this package.

from py_qvalue import pi0est, qvalue

Relationship to the R qvalue Package

This package is a Python port inspired by and aiming to replicate the functionality of the original qvalue package for R, developed by John D. Storey and colleagues. The R qvalue package is a widely cited and respected tool for false discovery rate control in multiple hypothesis testing.

For more information on the statistical methods and the original R implementation, please refer to:

• The original R qvalue package documentation and resources.
• Relevant publications by John D. Storey and Robert Tibshirani on false discovery rates and q-values.
  • Statistical significance for genomewide studies
  • A direct approach to false discovery rates
  • The positive false discovery rate: a Bayesian interpretation and the q-value

Contributing

We welcome contributions to py-qvalue! If you find a bug, have a feature request, or want to contribute code, please feel free to open an issue or submit a pull request on the GitHub repository.

License

This project is licensed under the GPL v3 License. See the LICENSE file for details.