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bioRxiv · 10.1101/2023.03.10.532096

plasma: Partial LeAst Squares for Multiomics Analysis

Yamaguchi, K.·Abdelbaky, S.·Yu, L.·Oakes, C. C.·Coombes, K. R.
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Abstract

Recent growth in the number and applications of high-throughput "omics" technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. We present a novel algorithm, plasma, to learn models to predict time-to-event outcomes from multiomics data sets. Plasma uses two layers of existing partial least squares algorithms to first select components that covary with the outcome and then construct a joint Cox proportional hazards model. We apply plasma to the stomach adenocarcinoma (STAD) data from The Cancer Genome Atlas. We validate the model both by splitting the STAD data into training and test sets and by applying it to the subset of esophageal cancer (ESCA) containing adenocarcinomas. We use the other half of the ESCA data, which contains squamous cell carcinomas dissimilar to STAD, as a negative control. Our model successfully separates both the STAD test set (p = 2.73 x 10-8) and the independent ESCA validation data (p = 0.025) into high risk and low risk patients. It does not separate the negative control data set (ESCA squamous cell carcinomas, p = 0.57). The performance of the joint multiomics model is superior to that of the individually trained models. It is also superior to the performance of an unsupervised method (Multi Omics Factor Analysis; MOFA) that finds latent factors to be used as putative predictors in a post-hoc survival analysis. Many of the factors that contribute strongly to the plasma model can be justified from the biological literature. SignificanceTo fill the unmet need for supervised multiomics methods, we introduce plasma, an algorithm based on partial least squares that integrates multiomics features into biologically relevant "components" that can predict patient outcomes. Availability and ImplementationThe plasma R package can be obtained from The Comprehensive R Archive Network (CRAN) athttps://CRAN.R-project.org/package=plasma. The latest version of the package can always be obtained from R-Forge at https://r-forge.r-project.org/R/?group_id=1746. Source code and data for the analysis presented here can be obtained from GitLab, at https://gitlab.com/krcoombes/plasma. ContactEmail: kcoombes@augusta.edu Supplementary InformationSupplementary material is available from the journal web site.

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Yamaguchi, K., Abdelbaky, S., Yu, L., Oakes, C. C., Coombes, K. R.. 2023-03-12. plasma: Partial LeAst Squares for Multiomics Analysis. https://doi.org/10.1101/2023.03.10.532096

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