seq2pathway

seq2pathway maps sequence-level genomic measurements from NGS data to gene and pathway scores to enable sample-level functional pathway analysis.

Key Features:

• seq2gene component: maps sequence-level coordinates such as single nucleotide polymorphisms (SNPs) and point mutation coordinates to gene-level scores.
• gene2path component: aggregates gene-level scores into pathway-scores for individual samples.
• FAIME-inspired scoring: computes rank-weighted mechanism scores for individual samples using the FAIME (Functional Analysis of Individual Microarray Expression) approach.
• Sample-level mechanism profiles: generates personal mechanism signatures for individual patients without requiring prior group assignment and supports analysis of continuous phenotypes such as survival time and tumor volume.
• Method comparison: reports precision and recall comparable to cohort-wide methods such as Gene Set Enrichment Analysis (GSEA).

Scientific Applications:

• Tumor versus control discrimination: applied to head and neck squamous cell carcinoma (HNSCC) datasets to discriminate tumor and control tissues with reported F-accuracy values of 100% and 97% across multiple datasets.
• Survival stratification: used to stratify recurrence-free survival in independent patient cohorts.
• Personalized mechanism discovery: generates individual mechanism signatures from gene expression arrays for mechanistic interpretation and comparison across datasets.
• Continuous phenotype analysis: applied to continuous clinical outcomes such as survival time and tumor volume.

Methodology:

Sequence coordinates are mapped to genes via the seq2gene component; gene scores are aggregated to pathway-scores via gene2path; mechanism scores are computed per sample using FAIME rank-weighted gene expression, and results are compared against cohort-wide methods such as GSEA.