FARMS: a generative framework for microarray data processing
Sprache des Vortragstitels:
International Biometric Conference 2012
Sprache des Tagungstitel:
Cost-effective oligonucleotide arrays are the predominant technique to measure high-dimensional genomic data like expression levels of genes or DNA copy number variations (CNVs). We present a latent variable model for summarizing microarray data called "FARMS: Factor Analysis for Robust Microarray Summarization". FARMS is based on a multiplicative latent variable model, which accounts for linear dependencies in the data, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques. In contrast to previous methods FARMS supplies model-based signal intensity values and a novel criterion for unsupervised feature selection named ?I/NI-Calls?. In our feature selection we propose to exclude all probe sets where a variation of the latent variable cannot reliably be detected by a maximum a posteriori optimization that combines and trades-off noise and signal likelihood. In this session we will present: (a) a generative model for summarizing microarray data which additionally allows to filter out genes according to their information content; (b) a rigorous assessment with 130 competitors and (c) results of detecting copy number variations (CNVs) using genotyping microarrays and an assessment with the most prevalent methods.