Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Area of Science:

• Computational biology
• Genomics
• Systems biology

Background:

• Single-subject transcriptome analysis is crucial for precision medicine but faces challenges due to high dimensionality and noise.
• Existing methods struggle to extract meaningful biological signals from individual transcriptomic data.

Purpose of the Study:

• To develop and validate a robust computational method for analyzing single-subject transcriptome response data.
• To enable the identification of functional attributes and gene set process regulation in individual subjects.

Main Methods:

• A mixture model approach for transcript fold-change clustering from isogenically paired samples.
• Integration with Gene Ontology Biological Processes (GO-BP) for dimension reduction and functional attribute identification.
• Development of functional signing metrics incorporating negative regulatory relationships from GO-BP.

Main Results:

• Reproducible and biologically meaningful signals were identified from single-subject transcriptome analysis.
• The method demonstrated high robustness and reproducibility across multiple genotypes and replicates (median AUC=0.96).
• Validation against established methods (limma+FET, SAM+FET, GSEA) confirmed the approach's reliability.

Conclusions:

• The proposed method effectively addresses the unmet need for single-subject transcriptome analysis in precision medicine.
• This approach facilitates transcriptomic studies with limited subject numbers or resources.
• The findings provide confidence in applying this validated method for individual-level transcriptomic analyses.