Document Type


Original Publication Date


Journal/Book/Conference Title

BMC Bioinformatics



DOI of Original Publication



Originally published at

Date of Submission

September 2014


Background Gene sets are widely used to interpret genome-scale data. Analysis techniques that make better use of the correlation structure of microarray data while addressing practical "n

Results We evaluated our testing procedure using both simulated data and a widely analyzed diabetes data set. We compared our approach to another popular multivariate test for both sets of data. Our results suggest an increase in power for detecting gene set differences can be obtained using our approach relative to the popular multivariate test with no increase in the false positive rate.

Conclusion Our regularized covariance matrix multivariate approach to gene set testing showed promise in both real and simulated data comparisons. Our findings are consistent with the recent literature in gene set methodology.


© 2009 Yates and Reimers; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Biostatistics Publications

1471-2105-10-300-s1.doc (81 kB)
Summary statistics of the RCMAT nominal p-values under the simulated non-null conditions. Under each of 36 select conditions (the number of variables/genes defined in the gene set, the sample size of each phenotype, the amount of nonzero separation as a multiple of an eigenvector representing the variance/correlation structure within the gene set, the separation occurs on either the major or a minor axis of variation) 100 simulation experiments were performed and permutation p-values obtained. For each condition various percentiles for the p-values obtained are listed.

1471-2105-10-300-s2.doc (217 kB)
Comparison of RCMAT with the procedure of Kong et al. For each of the gene sets from Mootha et al. [3] both the RCMAT and the method of Kong et al. were applied. Nominal (unadjusted) permutation p-values for each of the two procedures are given. The number of genes in the pathway is also provided.