Open Access

Multicriteria Gene Screening for Analysis of Differential Expression with DNA Microarrays

  • Alfred O Hero1Email author,
  • Gilles Fleury2,
  • Alan J Mears3, 4 and
  • Anand Swaroop3
EURASIP Journal on Advances in Signal Processing20042004:754506

https://doi.org/10.1155/S1110865704310036

Received: 10 May 2003

Published: 21 January 2004

Abstract

This paper introduces a statistical methodology for the identification of differentially expressed genes in DNA microarray experiments based on multiple criteria. These criteria are false discovery rate (FDR), variance-normalized differential expression levels (paired statistics), and minimum acceptable difference (MAD). The methodology also provides a set of simultaneous FDR confidence intervals on the true expression differences. The analysis can be implemented as a two-stage algorithm in which there is an initial screen that controls only FDR, which is then followed by a second screen which controls both FDR and MAD. It can also be implemented by computing and thresholding the set of FDR values for each gene that satisfies the MAD criterion. We illustrate the procedure to identify differentially expressed genes from a wild type versus knockout comparison of microarray data.

Keywords

bioinformatics gene filtering gene profiling multiple comparisons familywise error rates

Authors’ Affiliations

(1)
Departments of Electrical Engineering and Computer Science, Biomedical Engineering, and Statistics, University of Michigan
(2)
Service des Mesures, Ecole Supérieure d'Electricité
(3)
Departments of Ophthalmology and Visual Sciences, and Human Genetics, University of Michigan Medical School
(4)
University of Ottawa Eye Institute, Ottawa Health Research Institute

Copyright

© Hero et al. 2004