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

• Main Authors: Aakra Ågot, Solheim Margrete, Nyquist Otto L, Snipen Lars, Nes Ingolf F
• Format: Article
• Language: English
• Published: BMC 2009-03-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/10/91

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>