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

Background: DNA 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. Results: We 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,...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Rashi Gupta, Dario Greco, Petri Auvinen, Elja Arjas
• Keywords: Bayesian Hierarchical Model, BMCBI 2010, Gene Expression, Gene Expression Indices