Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Differential expression (DE) and gene set enrichment (GSE) analyses are standard in single-cell RNA sequencing (scRNA-seq).
• Existing methods often analyze DE and GSE separately, potentially limiting insights.

Purpose of the Study:

• To develop an integrative and scalable computational method, iDEA, for joint DE and GSE analysis in scRNA-seq.
• To improve the power and consistency of DE analysis and the accuracy of GSE analysis.

Main Methods:

• Developed iDEA, a hierarchical Bayesian framework for integrated DE and GSE analysis.
• iDEA utilizes DE summary statistics as input, compatible with various DE methods.
• Method validated through extensive simulations and application to three scRNA-seq datasets.

Main Results:

• iDEA demonstrated significant power gains: up to five-fold over existing GSE methods and up to 64% over existing DE methods.
• The integrated approach identified numerous biological pathways missed by conventional methods.
• Simulations confirmed the benefits of joint analysis.

Conclusions:

• iDEA offers a powerful and accurate approach for analyzing scRNA-seq data by integrating DE and GSE.
• This method enhances the discovery of biological pathways, advancing scRNA-seq data interpretation.