Graphical Abstract
Highlights
Unsupervised feature extraction from proteogenomics data.
Pathway level integration of multi-omics data based on clinical features.
Abstract
Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.
Footnotes
Author contributions: W.L., S.H.P., S.M., and D.F. designed research; W.L. performed research; W.L. analyzed data; W.L., S.M., and D.F. wrote the paper.
↵* We would like to acknowledge funding by the National Cancer Institute (NCI) through CPTAC award U24 CA210972 and a contract 13XS068 from Leidos Biomedical Research, Inc., and by a grant from the Shifrin-Myers Breast Cancer Discovery Fund.
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This article contains supplemental Figures and Tables.
- Received March 12, 2019.
- Revision received June 1, 2019.
- © 2019 Liu et al.
Published under exclusive license by The American Society for Biochemistry and Molecular Biology, Inc.