ArticleBiostatistics (Oxford, England)2022
Individualized multi-omic pathway deviation scores using multiple factor analysis.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- SOPA and SIMPA: normalized single-sample integrated multiomics pathway analysis of tumor heterogeneity in solid cancers.Briefings in bioinformatics · 2026Article
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- Benchmarking single-sample gene set scoring methods for application in precision medicine.Briefings in bioinformatics · 2025Article
- Integrated Multi-Omics Analysis of Cerebrospinal Fluid in Postoperative Delirium.Biomolecules · 2024Article
- Systematic assessment of prognostic molecular features across cancers.Cell genomics · 2023Article
- A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.
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