Journal article
Identification of gene pairs through penalized regression subject to constraints
BMC bioinformatics, Vol.18(1), pp.466-466
11/03/2017
DOI: 10.1186/s12859-017-1872-9
PMCID: PMC5670721
PMID: 29100492
Abstract
Background: This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically. Results: In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen. Conclusion: Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.
Details
- Title: Subtitle
- Identification of gene pairs through penalized regression subject to constraints
- Creators
- Rex Shen - The Blake School, Minneapolis, USALan Luo - University of MichiganHui Jiang - University of Michigan
- Resource Type
- Journal article
- Publication Details
- BMC bioinformatics, Vol.18(1), pp.466-466
- DOI
- 10.1186/s12859-017-1872-9
- PMID
- 29100492
- PMCID
- PMC5670721
- NLM abbreviation
- BMC Bioinformatics
- ISSN
- 1471-2105
- eISSN
- 1471-2105
- Publisher
- BioMed Central
- Grant note
- 5P50CA186786 / ; 4P30CA046592 / ;
- Language
- English
- Date published
- 11/03/2017
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257605002771
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