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A Discussion Concerning the Inclusion of Variety Effect when Analysis of Variance is Used to Detect Differentially Expressed Genes
Guri Feten,Are Halvor Aastveit,Lars Snipen,Trygve Alm?y
Gene Regulation and Systems Biology , 2007,
Abstract: In microarray studies several statistical methods have been proposed with the purpose of identifying differentially expressed genes in two varieties. A commonly used method is an analysis of variance model where only the effect of interaction between variety and gene is tested. In this paper we argue that in addition to the interaction effects, the main effect of variety should simultaneously also be taken into account when posting the hypothesis.
Detection of divergent genes in microbial aCGH experiments
Lars Snipen, Dirk Repsilber, Ludvig Nyquist, Andreas Ziegler, ?got Aakra, Are Aastveit
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-181
Abstract: We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity.The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae.When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.The genetic diversity among bacteria mirrors their lifestyles and physiological versatilities and evolves from adaptation to their niches and growth conditions. Many techniques have been used to obtain a picture of true microbial diversity. Microarray-based comparative genome hybridization (aCGH) is now a commonly used tool in comparative genomics. Compared to sequencing and comparing whole genomes, aCGH provides rapid genomotyping in bacteria [1,2].The majority of applications of aCGH is in cancer-research, where copy-number abberations is the primary focus [3,4]. Several methods have been suggested to analyze such data, e.g. [5-7].In microbial studies of genome diversity, usually one fully sequenced strain, called index strain, is compared to a set of unsequenced strains of the same or closely related bacterial species, called sample strains. In this setting it is of interest to characterize the sample strains with respect to the genes they have in common with the index strain, and those which are absent or highly divergent.In theory, every given gene is either present or divergent in th
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