A Single-Subject Method to Detect Pathways Enriched With Alternatively Spliced Genes.

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• RNA-Sequencing (RNA-Seq) data holds potential for precision medicine, but current methods often overlook alternative splicing.
• Identifying and interpreting patient-specific alternative splicing patterns within biological pathways remains a challenge.

Purpose of the Study:

• To develop and validate a novel methodology, N-of-1-pathways Alternatively Spliced (N1PAS), for analyzing subject-specific alternative splicing patterns within pathways.
• To test the hypothesis that pathway aggregation of alternatively spliced genes (ASGs) can reveal disease mechanisms and predict patient survival.

Main Methods:

• The N1PAS method utilizes paired-sample RNA-Seq isoform expression data and pathway annotations.
• It quantifies alternative splicing using Hellinger distances and employs two-stage clustering for pathway enrichment analysis.
• Pathway enrichment is quantified using an odds ratio and statistical significance.

Main Results:

• N1PAS successfully identified relevant pathways in clinical samples (p < 0.05 in 4/6 datasets).
• Extensive simulations demonstrated N1PAS's power in detecting pathway enrichment of ASGs while controlling false discovery rates.
• The method revealed significant heterogeneity in single-subject alternative splicing patterns, often missed by cohort-based approaches.

Conclusions:

• N1PAS provides a powerful tool for analyzing individual patient alternative splicing, uncovering disease mechanisms, and predicting survival.
• The patient-specific approach offers a more nuanced understanding of transcriptome data for clinical applications.
• This methodology facilitates the translation of transcriptome data into actionable clinical diagnostics and personalized treatment strategies.