Bayesian integrated modeling of expression data: a case study on RhoG

BMC Bioinformatics

Research Article

Rashi Gupta¹  Elja Arjas²  Petri Auvinen³  Dario Greco³ 

[1] Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FIN-00014, Helsinki, Finland;Institute of Biotechnology, University of Helsinki, P.O. Box 56, FIN-00014, Helsinki, Finland;Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FIN-00014, Helsinki, Finland;National Institute for Health and Welfare (THL), Mannerheimintie 166, 00300, Helsinki, Finland;Institute of Biotechnology, University of Helsinki, P.O. Box 56, FIN-00014, Helsinki, Finland;

关键词: Posterior Distribution;    Bayesian Hierarchical Model;    Latent Intensity;    Multiple Scan;    High Level Analysis;   

DOI  :  10.1186/1471-2105-11-295

 received in 2009-06-04, accepted in 2010-06-01,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundDNA microarrays provide an efficient method for measuring activity of genes in parallel and even covering all the known transcripts of an organism on a single array. This has to be balanced against that analyzing data emerging from microarrays involves several consecutive steps, and each of them is a potential source of errors. Errors tend to accumulate when moving from the lower level towards the higher level analyses because of the sequential nature. Eliminating such errors does not seem feasible without completely changing the technologies, but one should nevertheless try to meet the goal of being able to realistically assess degree of the uncertainties that are involved when drawing the final conclusions from such analyses.ResultsWe present a Bayesian hierarchical model for finding differentially expressed genes between two experimental conditions, proposing an integrated statistical approach where correcting signal saturation, systematic array effects, dye effects, and finding differentially expressed genes, are all modeled jointly. The integration allows all these components, and also the associated errors, to be considered simultaneously. The inference is based on full posterior distribution of gene expression indices and on quantities derived from them rather than on point estimates. The model was applied and tested on two different datasets.ConclusionsThe method presents a way of integrating various steps of microarray analysis into a single joint analysis, and thereby enables extracting information on differential expression in a manner, which properly accounts for various sources of potential error in the process.

【 授权许可】

CC BY   
© Gupta et al; licensee BioMed Central Ltd. 2010