Related Experiment Video
Updated: Jun 24, 2026
Precise, High-throughput Analysis of Bacterial Growth
Published on: September 19, 2017
Improved analysis of bacterial CGH data beyond the log-ratio paradigm
Lars Snipen1, Otto L Nyquist, Margrete Solheim
1Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no
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.
