Prediction with dimension reduction of multiple molecular data sources for patient Survival

Adam Kaplan, Eric Lock

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA). However, the application of PCA is not straightforward for multisource data, wherein multiple sources of ‘omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE), an extension of PCA for multisource data, for prediction of differing response types. We conduct illustrative simulations to illustrate the practical advantages and interpretability of our approach. As an application example, we consider predicting survival for patients with glioblastoma multiforme from 3 data sources measuring messenger RNA expression, microRNA expression, and DNA methylation. We also introduce a method to estimate JIVE scores for new samples that were not used in the initial dimension reduction and study its theoretical properties; this method is implemented in the R package R.JIVE on CRAN, in the function jive.predict.

Original languageEnglish (US)
JournalCancer Informatics
Volume16
DOIs
StatePublished - 2017

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institutes of Health (grant ULI RR033183/KL2 RR0333182).

Funding Information:
FunDIng: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institutes of Health (grant ULI RR033183/KL2 RR0333182).

Publisher Copyright:
© The Author(s) 2017.

Keywords

  • Batch effects
  • High dimensional
  • Joint and individual variation explained
  • Multisource data
  • New sample prediction
  • Principal component analysis
  • Survival analysis

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