Order-restricted inference for ordered gene expression (ORIOGEN) data under heteroscedastic variances.
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).
Simmons
Susan J
SJ
Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403; Biostatistics Branch, NIEHS (NIH), RTP, NC 27709, USA. simmonssj@uncw.edu
Peddada
Shyamal D
SD