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PLASMA: Partial LeAst Squares for Multiomics Analysis
Kyoko Yamaguchi1, Salma Abdelbaky1, Lianbo Yu2
1Division of Hematology, Department of Internal Medicine, Ohio State University, Columbus, OH 43210, USA.
Cancers
|January 25, 2025
Summary
We developed PLASMA, a novel supervised method for integrating multiomics data to predict patient survival outcomes. This algorithm effectively identifies high-risk and low-risk patients, outperforming existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- High-throughput "omics" technologies generate vast datasets, necessitating advanced methods for multi-omics data integration.
- Supervised learning methods for multi-omics integration, particularly for survival prediction, are less developed than unsupervised approaches.
Purpose of the Study:
- To introduce PLASMA, the first supervised algorithm capable of predicting time-to-event outcomes from multi-omics data.
- To address challenges in multi-omics data integration, including handling datasets with missing omics assays for some samples.
Main Methods:
- PLASMA employs a two-layer partial least squares (PLS) approach to identify components correlating with outcomes.
- It constructs a joint Cox proportional hazards model using these selected components.
- The algorithm can learn from multi-omics data even when samples have been assayed on only a subset of omics data.
Main Results:
- PLASMA successfully predicted risk stratification in stomach adenocarcinoma (STAD) and esophageal adenocarcinoma (ESCA) patient data.
- The model demonstrated superior performance compared to individually trained omics models and the unsupervised Multi-Omics Factor Analysis (MOFA) method.
- A negative comparison using dissimilar ESCA squamous cell carcinomas showed no significant risk separation (p = 0.57), validating PLASMA's specificity.
Conclusions:
- PLASMA provides a powerful supervised approach for multi-omics data integration and survival prediction.
- The identified predictive factors within the PLASMA model are biologically justifiable.
- This method advances the field of personalized medicine by enabling more accurate patient risk stratification.
