Covariate adjustment in the analysis of microarray data from clinical studies

Functional & Integrative Genomics
Debashis Ghosh, Arul M Chinnaiyan

Abstract

There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for follow-up validation studies. We develop two approaches to the analysis of microarray data in non-randomized clinical settings. The first is an extension of the current significance analysis of microarray procedures, where other covariates are taken into account. The second is a novel covariate-adjusted regression modelling based on the receiver operating characteristic (ROC) curve for the analysis of gene expression data. The ideas are illustrated using data from a prostate cancer molecular profiling study.

References

Jul 14, 1998·Nature Medicine·J KononenO P Kallioniemi
Jul 19, 2001·Journal of the National Cancer Institute·M S PepeY Yasui
Aug 24, 2001·Nature·S M DhanasekaranA M Chinnaiyan
Aug 10, 2002·Seminars in Oncology·John A Thompson
Aug 31, 2002·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·S Eva SingletaryFrederick L Greene
Oct 11, 2002·Nature·Sooryanarayana VaramballyArul M Chinnaiyan
May 24, 2003·Biometrics·Margaret Sullivan PepeMichel Schummer

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Citations

Mar 9, 2007·Biometrical Journal. Biometrische Zeitschrift·Debashis Ghosh, Arul M Chinnaiyan
Aug 30, 2008·Genomics·Debashis Ghosh, Laila M Poisson
Jun 22, 2019·Metabolomics : Official Journal of the Metabolomic Society·Valentina L KouznetsovaIgor F Tsigelny

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