Finding differentially expressed genes: pattern generation using Q-values

Authors:
Osman Abul;Reda Alhajj;Faruk Polat;Ken Barker
Affiliations:
University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;Middle East Technical University, Ankara, Turkey;University of Calgary, Calgary, Alberta, Canada
Venue:
Proceedings of the 2004 ACM symposium on Applied computing
Year:
2004

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Cited 2

Data management research at the Middle East Technical University

ACM SIGMOD Record
A Powerful Approach for Effective Finding of Significantly Differentially Expressed Genes

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

In this paper, we consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) [7] can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided.