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United States Department of Agriculture

Agricultural Research Service

Research Project: Linking Foods, Behavior and Metabolism to Promote a Healthy Body Weight

Location: Obesity and Metabolism Research Unit

Title: Structured variable selection with q-values

Authors
item Garcia, Tanya -
item Muller, Samuel -
item Carroll, Raymond -
item Dunn, Tamara -
item Thomas, Anthony -
item Adams, Sean
item Pillai, Suresh -
item Walzem, Rosemary -

Submitted to: Biostatistics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 4, 2013
Publication Date: April 10, 2013
Citation: Garcia, T.P., Muller, S., Carroll, R.J., Dunn, T.N., Thomas, A.P., Adams, S.H., Pillai, S.D., Walzem, R.L. 2013. Structured variable selection with q-values. Biostatistics. 23580317.

Interpretive Summary: When considering scientific results and high-content datasets containing many variables, some of which can both be influenced by and influence other variables used in statistical analysis, the already challenging problem of selecting variables when the number of covariates exceeds the sample size becomes more difficult. To address this problem, statistical approaches are needed that take these factors into account while also correcting for potential error do to multiple comparisons. An illustrative example is a metabolic study in mice that has diet groups and gut microbial percentages which may affect changes in multiple phenotypes related to body weight regulation. The dataset has more variables than observations and diet is known to act directly on the phenotypes as well as on some or potentially all of the microbial percentages. Interest lies in determining which gut microflora influence the phenotypes while accounting for the direct relationship between diet and the other variables. A new methodology for variable selection in this context is presented that links the concept of q-values from multiple hypothesis testing to the recently developed weighted Lasso.

Technical Abstract: When some of the regressors can act on both the response and other explanatory variables, the already challenging problem of selecting variables when the number of covariates exceeds the sample size becomes more difficult. A motivating example is a metabolic study in mice that has diet groups and gut microbial percentages which may affect changes in multiple phenotypes related to body weight regulation. The data has more variables than observations and diet is known to act directly on the phenotypes as well as on some or potentially all of the microbial percentages. Interest lies in determining which gut microflora influence the phenotypes while accounting for the direct relationship between diet and the other variables. A new methodology for variable selection in this context is presented that links the concept of q-values from multiple hypothesis testing to the recently developed weighted Lasso.

Last Modified: 4/17/2014
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