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Open Access Highly Accessed Open Badges Methodology article

Analysis of high dimensional data using pre-defined set and subset information, with applications to genomic data

Wenge Guo1, Mingan Yang2, Chuanhua Xing3 and Shyamal D Peddada4*

Author affiliations

1 Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA

2 Department of Mathematics, Central Michigan University, Mt. Pleasant, MI 48858, USA

3 Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA

4 Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA

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Citation and License

BMC Bioinformatics 2012, 13:177  doi:10.1186/1471-2105-13-177

Published: 24 July 2012



Based on available biological information, genomic data can often be partitioned into pre-defined sets (e.g. pathways) and subsets within sets. Biologists are often interested in determining whether some pre-defined sets of variables (e.g. genes) are differentially expressed under varying experimental conditions. Several procedures are available in the literature for making such determinations, however, they do not take into account information regarding the subsets within each set. Secondly, variables (e.g. genes) belonging to a set or a subset are potentially correlated, yet such information is often ignored and univariate methods are used. This may result in loss of power and/or inflated false positive rate.


We introduce a multiple testing-based methodology which makes use of available information regarding biologically relevant subsets within each pre-defined set of variables while exploiting the underlying dependence structure among the variables. Using this methodology, a biologist may not only determine whether a set of variables are differentially expressed between two experimental conditions, but may also test whether specific subsets within a significant set are also significant.


The proposed methodology; (a) is easy to implement, (b) does not require inverting potentially singular covariance matrices, and (c) controls the family wise error rate (FWER) at the desired nominal level, (d) is robust to the underlying distribution and covariance structures. Although for simplicity of exposition, the methodology is described for microarray gene expression data, it is also applicable to any high dimensional data, such as the mRNA seq data, CpG methylation data etc.