Unsupervised Bayesian linear unmixing of gene expression microarrays.

ABSTRACT:

Background

This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sa

SUBMITTER: Bazot C 

PROVIDER: S-EPMC3681645 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature