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.
Bibliographical noteFunding Information:
* 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. □S This article contains supplemental Figures and Tables.
© 2019 Liu et al.
PubMed: MeSH publication types
- Journal Article