Methodology article

Improved analysis of bacterial CGH data beyond the log-ratio paradigm

Lars Snipen¹ , Otto L Nyquist² , Margrete Solheim² , Ågot Aakra² and Ingolf F Nes²

¹  Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway

²  Laboratory of Microbial Gene Technology, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway

author email corresponding author email

BMC Bioinformatics 2009, 10:91doi:10.1186/1471-2105-10-91

Published:	19 March 2009

Abstract

Background

Existing methods for analyzing bacterial CGH data from two-color arrays are based on log-ratios only, a paradigm inherited from expression studies. We propose an alternative approach, where microarray signals are used in a different way and sequence identity is predicted using a supervised learning approach.

Results

A data set containing 32 hybridizations of sequenced versus sequenced genomes have been used to test and compare methods. A ROC-analysis has been performed to illustrate the ability to rank probes with respect to Present/Absent calls. Classification into Present and Absent is compared with that of a gaussian mixture model.

Conclusion

The results indicate our proposed method is an improvement of existing methods with respect to ranking and classification of probes, especially for multi-genome arrays.