Faculty Collaboration Database

Multivariate approach for selecting sets of differentially expressed genes. Math Biosci 2002 Mar;176(1):59-69

Date

02/28/2002

Pubmed ID

11867084

DOI

10.1016/s0025-5564(01)00105-5

Scopus ID

2-s2.0-0036126862 (requires institutional sign-in at Scopus site)   34 Citations

Abstract

An important problem addressed using cDNA microarray data is the detection of genes differentially expressed in two tissues of interest. Currently used approaches ignore the multidimensional structure of the data. However it is well known that correlation among covariates can enhance the ability to detect less pronounced differences. We use the Mahalanobis distance between vectors of gene expressions as a criterion for simultaneously comparing a set of genes and develop an algorithm for maximizing it. To overcome the problem of instability of covariance matrices we propose a new method of combining data from small-scale random search experiments. We show that by utilizing the correlation structure the multivariate method, in addition to the genes found by the one-dimensional criteria, finds genes whose differential expression is not detectable marginally.

Author List

Chilingaryan A, Gevorgyan N, Vardanyan A, Jones D, Szabo A

Author

Aniko Szabo PhD Professor in the Data Science Institute department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Computer Simulation
Gene Expression Profiling
HT29 Cells
Humans
Multivariate Analysis
Oligonucleotide Array Sequence Analysis