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

doi:10.1109/GENSIPS.2012.6507746

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 proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
Pages	130-134
Number of pages	5
DOIs	https://doi.org/10.1109/GENSIPS.2012.6507746
State	Published - 2012
Externally published	Yes
Event	2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012 - Washington, DC, United States Duration: Dec 2 2012 → Dec 4 2012

Publication series

Name	Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ISSN (Print)	2150-3001
ISSN (Electronic)	2150-301X

Conference

Conference	2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
Country/Territory	United States
City	Washington, DC
Period	12/2/12 → 12/4/12

ASJC Scopus subject areas

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/GENSIPS.2012.6507746

Cite this

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

Latent feature decompositions for integrative analysis of diverse high-throughput genomic data. / Gregory, Karl B.; Coombes, Kevin R.; Momin, Amin et al.
Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012. 2012. p. 130-134 6507746 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gregory, KB, Coombes, KR, Momin, A, Girard, L, Byers, LA, Lin, S, Peyton, M, Heymach, JV, Minna, JD & Baladandayuthapani, V 2012, Latent feature decompositions for integrative analysis of diverse high-throughput genomic data. in Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012., 6507746, Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 130-134, 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012, Washington, DC, United States, 12/2/12. https://doi.org/10.1109/GENSIPS.2012.6507746

Gregory KB, Coombes KR, Momin A, Girard L, Byers LA, Lin S et al. Latent feature decompositions for integrative analysis of diverse high-throughput genomic data. In Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012. 2012. p. 130-134. 6507746. (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). doi: 10.1109/GENSIPS.2012.6507746

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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.",

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AB - 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.

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

Abstract

Publication series

Conference

ASJC Scopus subject areas

UN SDGs

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