Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication)

Abstract

Article published by EDP Sciences and available at http://www.gse-journal.org or http://dx.doi.org/10.1051/gse:2007029 F. Jaffrézic et al.Abstract -A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies. quality control / differentially expressed genes / mastitis resistance / microarray data / normalisation

Integrating biological information into the statistical analysis and design of microarray experiments

“…differences among slides and dye labeling and Biologically informed microarray experiments spatial variation within slides), such that their statistical analysis generally requires a data preprocessing step, usually referred to as normalization, for removing non-biological effects contributing to variation on expression measures. A review and comparison of normalization techniques is provided, e.g., in Jaffrezic et al (2007).…”

“…Depending on the objective(s) of the study such analysis may involve, e.g., clustering, classification techniques or hypothesis testing for detection of differential expression (for a comparison of different approaches, see, e.g., Jaffrezic et al, 2007 andSorensen et al, 2007). In this manuscript, we will specifically discuss the comparison of expression profiles across groups or populations using ANOVA models.…”

“…In this manuscript, we will specifically discuss the comparison of expression profiles across groups or populations using ANOVA models. Such models (including those with random effects) are the most popular in the microarray literature (see, e.g., Jaffrezic et al, 2007), due to their flexibility and ease of use as well as the availability of software for their implementation, e.g. MAANOVA and LIMMA, both developed in R language and available at Bioconductor website (http://www.bioconductor.org/).…”

“…The statistical analysis of such data sets generally involves a data preprocessing step, which refers to the summarization of signal intensities into expression indexes, as well as data transformation and normalization, for removing non-biological systematic effects contributing to variation on expression measurements (Jaffrezic et al, 2007). After the microarray data has been normalized, the statistical analysis may involve, e.g., clustering techniques, discriminant analysis, or differential expression significance test (see, e.g., Jaffrezic et al, 2007;Sorensen et al, 2007).…”

“…After the microarray data has been normalized, the statistical analysis may involve, e.g., clustering techniques, discriminant analysis, or differential expression significance test (see, e.g., Jaffrezic et al, 2007;Sorensen et al, 2007). Subsequently, results are generally complemented with some sort of validation or interpretation using biological *This paper has been presented at the session ''Genomics selection and bioinformatics'' of the 59th Annual meeting of the European Association for Animal Production held in Vilnius (Lithuania), 24 to 27 August 2008.…”