Comparison of alternative mixture model methods to analyze bacterial CGH experiments with multi-genome arrays

BMC Research Notes

Francisco Rodrigues Pinto³  Marília Antunes²  Mário Ramirez¹  Cláudia Elvas Suissas³  Liliana Sofia Cardoso¹ 

[1] Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal;Centro de Estatística e Aplicações, DEIO, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal;Centro de Química e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

关键词: Data analysis;    Comparative genomic hybridization;    Microarray;   

Others  :  1134278
DOI  :  10.1186/1756-0500-7-148

 received in 2013-03-22, accepted in 2014-03-10,  发布年份 2014

【 摘 要 】

Background

Microarray-based comparative genomic hybridization (aCGH) is used for rapid comparison of genomes of different bacterial strains. The purpose is to evaluate the distribution of genes from sequenced bacterial strains (control) among unsequenced strains (test). We previously compared the use of single strain versus multiple strain control with arrays covering multiple genomes. The conclusion was that a multiple strain control promoted a better separation of signals between present and absent genes.

Findings

We now extend our previous study by applying the Expectation-Maximization (EM) algorithm to fit a mixture model to the signal distribution in order to classify each gene as present or absent and by comparing different methods for analyzing aCGH data, using combinations of different control strain choices, two different statistical mixture models, with or without normalization, with or without logarithm transformation and with test-over-control or inverse signal ratio calculation. We also assessed the impact of replication on classification accuracy. Higher values of accuracy have been achieved using the ratio of control-over-test intensities, without logarithmic transformation and with a strain mix control. Normalization and the type of mixture model fitted by the EM algorithm did not have a significant impact on classification accuracy. Similarly, using the average of replicate arrays to perform the classification does not significantly improve the results.

Conclusions

Our work provides a guiding benchmark comparison of alternative methods to analyze aCGH results that can impact on the analysis of currently ongoing comparative genomic projects or in the re-analysis of published studies.

【 授权许可】

   
2014 Cardoso et al.; licensee BioMed Central Ltd.

【 图 表 】

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