Title

Order-restricted inference for ordered gene expression (ORIOGEN) data under heteroscedastic variances

Authors

Susan J. Simmons^* and Shyamal D. Peddada

Affiliation

Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403; Biostatistics Branch, NIEHS (NIH), RTP, NC - 27709

Email

simmonssj@uncw.edu; * Corresponding author

Article Type

Prediction Model

Date

      received August 08, 2006; accepted January 15, 2007; published online April 10, 2007

Abstract

This article extends the order restricted inference approach for time-course or dose-response gene expression microarray data, introduced by Peddada and colleagues (2003) for the case when gene expression is heteroscedastic over time or dose.  The new methodology uses an iterative algorithm to estimate mean expression at various times/doses when mean expression is subject to pre-defined patterns or profiles, known as order-restrictions.  Simulation studies reveal that the resulting bootstrap-based methodology for gene selection maintains the false positive rate at the nominal level while competing well with ORIOGEN in terms of power. The proposed methodology is illustrated using a breast cancer cell-line data analyzed by Peddada and colleagues (2003).

Keywords

ordered gene expression; heteroscedastic variances; restricted inference; iterative algorithm

Citation

Simmons & Peddada., Bioinformation 1(10): 414-419 (2007)

Edited by

Susmita Datta

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.