Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina GrĂ¼n, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Meta-Statistics for Variable Selection: The R Package BioMark | Wehrens | Journal of Statistical Software
Authors: Ron Wehrens, Pietro Franceschi
Title: Meta-Statistics for Variable Selection: The R Package BioMark
Abstract: Biomarker identification is an ever more important topic in the life sciences. With the advent of measurement methodologies based on microarrays and mass spectrometry, thousands of variables are routinely being measured on complex biological samples. Often, the question is what makes two groups of samples different. Classical hypothesis testing suffers from the multiple testing problem; however, correcting for this often leads to a lack of power. In addition, choosing ? cutoff levels remains somewhat arbitrary. Also in a regression context, a model depending on few but relevant variables will be more accurate and precise, and easier to interpret biologically.
We propose an R package, BioMark, implementing two meta-statistics for variable selection. The first, higher criticism, presents a data-dependent selection threshold for significance, instead of a cookbook value of ? = 0.05. It is applicable in all cases where two groups are compared. The second, stability selection, is more general, and can also be applied in a regression context. This approach uses repeated subsampling of the data in order to assess the variability of the model coefficients and selects those that remain consistently important. It is shown using experimental spike-in data from the field of metabolomics that both approaches work well with real data. BioMark also contains functionality for simulating data with specific characteristics for algorithm development and testing.

Page views:: 4079. Submitted: 2012-02-17. Published: 2012-11-13.
Paper: Meta-Statistics for Variable Selection: The R Package BioMark     Download PDF (Downloads: 4134)
Supplements:
BioMark_0.4.1.tar.gz: R source package Download (Downloads: 471; 913KB)
v51i10.R: R example code from the paper Download (Downloads: 460; 6KB)

DOI: 10.18637/jss.v051.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.