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Multi-input and Multi-variable systems

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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Adaptive Multiview Nonnegative Matrix Factorization Algorithm for Integration of Multimodal Biomedical Data.

Bisakha Ray¹, Wenke Liu¹, David Fenyö¹

¹Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY, USA.

Cancer Informatics

|August 25, 2017

Summary

This summary is machine-generated.

Integrating multimodal tumor data is key for cancer research. A new multiview nonnegative matrix factorization (NNMF) method effectively reduces dimensionality and integrates diverse data types for improved cancer biology insights.

Keywords:

Multimodal data dimensionality reduction nonnegative matrix factorization phenotype prediction proteogenomics

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Area of Science:

Computational biology
Bioinformatics
Cancer research

Background:

Multimodal tumor data is rapidly increasing, yet effective integration methods lag behind data generation capabilities.
Understanding cancer biology and personalizing treatment requires integrating diverse data types like proteomics and genomics.
Existing data integration methods need enhancement to handle complex, heterogeneous tumor data.

Purpose of the Study:

To introduce an extension of multiview nonnegative matrix factorization (NNMF) for multimodal tumor data integration and dimensionality reduction.
To compare the predictive performance of the novel method using unimodal versus multimodal data.
To evaluate the proposed multiview approach against current data integration techniques.

Main Methods:

Developed an extension to a multiview nonnegative matrix factorization (NNMF) algorithm.
Applied the method to large-scale quantitative protein, phosphoprotein, exome sequencing, and RNA-Seq tumor data from CPTAC and TCGA.
Performed comparative analysis of unimodal and multimodal data for predictive modeling.

Main Results:

For breast cancer, transcript levels best predicted receptor status; for ovarian and colon cancers, protein/phosphoprotein levels were most predictive.
Multiview NNMF applied to multimodal data did not show statistically significant improvement over unimodal data for outcome prediction.
Proteomics data may offer more predictive information on tumor phenotypes than transcript levels due to proteins being direct functional products.

Conclusions:

The proposed multiview NNMF extension provides an efficient method for dimensionality reduction and integration of heterogeneous tumor data.
While not always statistically superior, the approach highlights the potential of proteomics data in understanding tumor functional states.
The method is generally applicable to the dimensionality reduction and joint analysis of any multimodal data types.