Multicriteria gene screening for analysis of differential expression with DNA microarrays

  • Authors:

  • Alfred O. Hero;Gilles Fleury;Alan J. Mears;Anand Swaroop

  • Affiliations:

  • Departments of Electrical Engineering and Computer Science, Biomedical Engineering, and Statistics, University of Michigan, Ann Arbor, MI;Service des Mesures, Ecole Supérieure d'Electricité, Gif-sur-Yvette, France;Departments of Ophthalmology and Visual Sciences, and Human Genetics, University of Michigan Medical School, Ann Arbor, MI and University of Ottawa Eye Institute, Ottawa Health Research Institute, ...;Departments of Ophthalmology and Visual Sciences, and Human Genetics, University of Michigan Medical School, Ann Arbor, MI

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2004

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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 t 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 P 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.