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


CONCLUSIONS
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.
Snipen
Lars
L
Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no
Nyquist
Otto L
OL
Solheim
Margrete
M
Aakra
Agot
A
Nes
Ingolf F
IF
D055028:Comparative Genomic Hybridization; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D016680:Genome, Bacterial; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012372:ROC Curve