# variancePartition: Interpreting drivers of variation in complex gene expression studies

## TL;DR

This paper introduces a new tool called variancePartition to help scientists understand what factors most influence gene expression in complex studies.

## Contribution

The novel contribution is a statistical framework and visualization tool for prioritizing sources of variation in gene expression data.

## Key findings

- VariancePartition provides genome-wide summaries of variation drivers in gene expression studies.
- It identifies genes that deviate from overall trends in variation.
- The tool is broadly applicable to various genomics assays.

## Abstract

As genomics studies become more complex and consider multiple sources of biological and technical variation, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation with a genome-wide summary, and identify genes that deviate from the genome-wide trend. variancePartition enables rapid interpretation of complex gene expression studies and is applicable to many genomics assays.