Functional Prediction: Gene Filtering Based on Multivariate Techniques

Background: Gene expression data is available on several organisms of interest in publicly available data-bases and knowledge on gene functions can be extracted from gene expression profiles by pattern comparison between genes when analyzed with multivariate techniques. Nevertheless, gene expression data is very noisy and those patterns are often difficult to detect with classical multivariate analysis. Objective: This work proposes using classical multivariate methods in order to detect and/or predict a subset of potential genes that could belong to a functional class of interest. Method: In order to achieve confident results (low error in the classification of genes with known function), strong filtering on the original data set is proposed here. The methodology is applied on three time course microarray data sets that compare healthy and pathogen inoculated plants, in order to illustrate methodology. Results: Results when focusing on prediction of unknown immunity genes show that the here proposed methodology is suitable for functional gene prediction. Conclusion: Moreover, the methodology is suitable for other organisms and microarray data sets from which gene expression profiles can be extracted.