variancePartition: interpreting drivers of variation in complex gene expression studies.

Area of Science:

• Genomics
• Computational Biology
• Statistical Genetics

Background:

• Large-scale gene expression studies are increasingly common.
• Understanding biological and technical variation is crucial for disease biology and regulatory genetics.

Purpose of the Study:

• To introduce variancePartition, a statistical and visualization framework.
• To prioritize drivers of variation in gene expression data.
• To identify genes with expression patterns deviating from genome-wide trends.

Main Methods:

• Utilizes a linear mixed model approach.
• Quantifies variation attributable to various factors including disease status, sex, cell/tissue type, ancestry, genetic background, experimental stimulus, and technical variables.
• Applies a genome-wide summary for prioritization.

Main Results:

• variancePartition effectively prioritizes variation drivers.
• Identifies genes with significant deviations from the genome-wide expression trend.
• Demonstrates reproducible patterns of biological and technical variation across four large-scale transcriptome datasets.

Conclusions:

• The open-source variancePartition software facilitates rapid interpretation of complex gene expression studies.
• Enables analysis of other high-throughput genomics assays.
• Available via Bioconductor for broad accessibility.