Variance component testing for identifying differentially expressed genes in RNA-seq data.

Area of Science:

• Bioinformatics
• Genomics
• Statistical Genetics

Background:

• RNA sequencing (RNA-Seq) provides gene expression measurements at the isoform level.
• Traditional methods aggregating isoforms may miss differential expression due to varied isoform effects.

Purpose of the Study:

• To develop a novel statistical test for identifying differentially expressed (DE) genes using isoform-level RNA-Seq data.
• To specifically address scenarios where individual isoforms exhibit differential effects.

Main Methods:

• A variance component-based test is introduced to jointly analyze multiple isoforms of a single gene.
• Isoform expression data are modeled using a negative binomial distribution with baseline abundance and isoform effects as random terms.
• The global null hypothesis of no difference across any isoforms is tested.

Main Results:

• Simulation results indicate the proposed set test outperforms traditional algorithms in detecting DE genes.
• The method demonstrates near-optimal power, particularly when covariate variance is high.
• The approach effectively identifies DE genes with sparse or opposing isoform effects.

Conclusions:

• The developed variance component-based test offers a powerful supplement to existing methods for DE gene analysis.
• This approach enhances the identification of complex differential expression patterns involving multiple gene isoforms.
• The method is validated through simulations and application to real RNA-Seq data.