Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer.
Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be inc...
| Published in: | BMC Medical Research Methodology Vol. 17; pp. 1 - 14 |
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| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
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BioMed Central
1/25/2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120970808&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120970808 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 1/25/2017 vid: 17 pid: 24147 pub: BioMed Central artinfo: ui: 120970808 120970808 NLM28122498 120970808 10.1186/s12874-017-0291-y NLM28122498 120970808 ppf: 1 ppct: 13 formats: tig: atl: Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer. aug: au: Zhai, Jing Hsu, Chiu-Hsieh Daye, Z. John affil: Epidemiology and Biostatistics Department, University of Arizona, Tucson, USA sug: subj: Bioinformatics Methods Linear Regression Breast Neoplasms Algorithms Genomics Methods Risk Assessment Neoplasm Grading Risk Factors Reproducibility of Results Computer Simulation Genes Breast Neoplasms Pathology Software ab: Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates.Methods: In this article, we propose the ridge-lasso hybrid estimator (ridle), a new penalized regression method that simultaneously estimates coefficients of mandatory covariates while allowing selection for others. The ridle provides a principled approach to mitigate effects of multicollinearity among the mandatory covariates and possible dependency between mandatory and optional variables. We provide detailed empirical and theoretical studies to evaluate our method. In addition, we develop an efficient algorithm for the ridle. Software, based on efficient Fortran code with R-language wrappers, is publicly and freely available at https://sites.google.com/site/zhongyindaye/software .Results: The ridle is useful when mandatory predictors are known to be significant due to prior knowledge or must be kept for additional analysis. Both theoretical and comprehensive simulation studies have shown that the ridle to be advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. A microarray gene expression analysis of the histologic grades of breast cancer has identified 24 genes, in which 2 genes are selected only by the ridle among current methods and found to be associated with tumor grade.Conclusions: In this article, we proposed the ridle as a principled sparse regression method for the selection of optional variables while incorporating mandatory ones. Results suggest that the ridle is advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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