A flexible two-stage procedure for identifying gene sets that are differentially expressed

Area of Science:

• Bioinformatics
• Genomics
• Statistical Genetics

Background:

• Microarray data analysis has evolved from single-gene to gene-set level testing.
• Gene-set analysis can detect subtle expression changes but requires further interpretation.
• Existing methods may not fully leverage gene-set findings.

Purpose of the Study:

• To develop a robust method for analyzing differential gene expression at both gene-set and individual-gene levels.
• To introduce and validate the overall false discovery rate (OFDR) for multi-level testing.
• To demonstrate the advantages of the proposed approach over traditional methods.

Main Methods:

• A two-stage approach: first testing differential expression at the gene-set level, followed by individual gene testing within significant gene sets.
• Implementation of the overall false discovery rate (OFDR) for controlling errors across multiple tests.
• Comparative analysis against methods focusing solely on gene sets or individual genes.

Main Results:

• The proposed method effectively integrates gene-set and individual-gene analyses.
• Controlling OFDR provides a more accurate error assessment for complex gene expression studies.
• The integrated approach offers enhanced power and interpretability compared to single-level analyses.

Conclusions:

• The proposed two-stage gene expression analysis framework improves the detection and interpretation of differential expression.
• OFDR is a suitable error metric for multi-level gene set and gene testing.
• This approach provides a more comprehensive understanding of gene expression changes in biological systems.