N1PAS

N1PAS identifies patient-specific alternative splicing events and tests pathway-level enrichment using paired-sample RNA-Seq isoform expression data.

Key Features:

• Individualized Analysis: Analyzes single-subject RNA-Seq isoform expression from paired comparisons such as tumor versus non-tumor or pre-treatment versus during-therapy to detect unique alternative splicing events overlooked by cohort-based studies.
• Pathway Aggregation: Aggregates subject-specific alternatively spliced genes (ASGs) within pathways to provide insights into disease mechanisms and potential survival predictions.
• Quantitative Metrics: Employs Hellinger distances to quantify alternative splicing variation followed by a two-stage clustering process to determine pathway enrichment and reports odds ratios and statistical significance measures.
• Validation and Power: Validated through computational experiments and Monte Carlo studies that demonstrate control of false discovery rates and selection of statistically significant pathways (p < 0.05) across datasets.
• Clinical Applications: Detects highly heterogeneous, subject-unique alternative splicing patterns and can predict cancer survival with reported false discovery rate (FDR) below 20%.

Scientific Applications:

• Precision Medicine: Enables single-subject pathway enrichment analyses to aid identification of personalized biomarkers and therapeutic targets from transcriptome data.
• Oncology — Survival Prediction: Applied in cancer studies to uncover individual splicing events and pathways associated with survival outcomes and candidate targets for intervention.

Methodology:

Accepts paired-sample RNA-Seq isoform expression data with pathway annotations; quantifies alternative splicing using Hellinger distances; employs a two-stage clustering approach to identify enriched pathways and reports statistical significance and odds ratios.