scMetaIntegrator: a meta-analysis approach to paired single-cell differential expression analysis

Kalani Ratnasiri , Sara N Mach , Catherine A Blish

Biorxiv : the Preprint Server for Biology +

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June 12, 2025

View abstract on PubMed

Summary

New single-cell MetaIntegrator analysis accounts for biological replicates and cell variability in paired RNA-sequencing data, improving accuracy for complex study designs.

Area Of Science

Genomics
Bioinformatics
Computational Biology

Background

Single-cell RNA sequencing (scRNA-seq) presents unique analytical challenges for paired and matched cohort designs.
Traditional differential gene expression methods often treat cells as independent, inflating false positive rates.
Existing pseudobulk methods overlook intra-sample variability, increasing false negatives.

Purpose Of The Study

To develop a novel meta-analysis approach for paired scRNA-seq data.
To accurately account for biological replicates and cellular variability in complex experimental designs.
To improve the reliability of differential gene expression analysis in scRNA-seq.

Main Methods

Developed a meta-analysis framework, single-cell MetaIntegrator, specifically for paired scRNA-seq data.
Incorporated methods to handle biological replicates and intra-sample cell variability.
Validated the approach using both real-world and synthetic datasets.

Main Results

The proposed meta-analysis approach demonstrates robust effect size estimation.
Achieved reproducible p-values, enhancing statistical power and reliability.
Outperformed traditional methods in handling paired and matched cohort designs.

Conclusions

Single-cell MetaIntegrator offers a superior method for analyzing complex scRNA-seq data structures.
The approach effectively addresses limitations of existing methods, reducing false positives and negatives.
Provides a powerful tool for reproducible and accurate differential gene expression analysis in scRNA-seq studies.