Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• RNA-sequencing (RNA-seq) is crucial for gene expression profiling in cancer research.
• High costs limit RNA-seq experiments to small sample sizes.
• Meta-analysis of RNA-seq data is essential for identifying potential biomarkers.

Purpose of the Study:

• To develop a novel p-value combination method for meta-analysis of independent RNA-seq studies.
• To account for study sample size and gene expression direction in meta-analysis.
• To improve the discovery of differentially expressed genes (DEGs) in cancer.

Main Methods:

• A new p-value combination method for RNA-seq meta-analysis was proposed.
• The method integrates multiple independent RNA-seq studies.
• It considers sample size and gene expression direction from individual studies.

Main Results:

• The proposed method generalizes the inverse-normal method, enhancing DEG discovery.
• It effectively identifies DEGs even with conflicting expression directions across studies.
• Applied to glioblastoma (GBM), it identified RAD51 as an over-expressed tumor suppressor and novel regulators like TCF7L2 and MAPT.

Conclusions:

• The developed meta-analysis method offers a robust approach for analyzing RNA-seq data with conflicting gene expression signals.
• It facilitates the identification of potential biomarkers by establishing differential expression status across studies.
• This method holds promise for advancing cancer research and therapeutic target discovery.