RCMAT: a regularized covariance matrix approach to testing gene sets.
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<p" concerns could provide a real increase in power. However correlation structure is hard to estimate with typical genomics sample sizes. In this paper we present an extension of a classical multivariate procedure that confronts this challenge by the use of a regularized covariance matrix.


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.


CONCLUSIONS
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.
Yates
Phillip D
PD
Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298, USA. yatespd@vcu.edu
Reimers
Mark A
MA
D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012984:Software