Latent feature decompositions for integrative analysis of diverse high-throughput genomic data

Karl B. Gregory, Kevin R. Coombes, Amin Momin, Luc Girard, Lauren A. Byers, Steven Lin, Michael Peyton, John V. Heymach, John D. Minna, Veerabhadran Baladandayuthapani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A general method for regressing a continuous response upon large groups of diverse genetic covariates via dimension reduction is developed and exemplified. It is shown that allowing latent features derived from different covariate groups to interact aids in prediction when interactions subsist among the original covariates. A means of selecting a subset of relevant covariates from the original set is proposed, and a simulation study is performed to demonstrate the effectiveness of the procedure for prediction and variable selection. The procedure is applied to a high-dimensional lung cancer data set to model the effects of gene expression, copy number variation, and methylation on a drug response.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
Pages130-134
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012 - Washington, DC, United States
Duration: Dec 2 2012Dec 4 2012

Other

Other2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
CountryUnited States
CityWashington, DC
Period12/2/1212/4/12

    Fingerprint

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Signal Processing
  • Biomedical Engineering

Cite this

Gregory, K. B., Coombes, K. R., Momin, A., Girard, L., Byers, L. A., Lin, S., ... Baladandayuthapani, V. (2012). Latent feature decompositions for integrative analysis of diverse high-throughput genomic data. In Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics (pp. 130-134). [6507746] https://doi.org/10.1109/GENSIPS.2012.6507746