title: Statistical mechanics approach to the sample deconvolution problem.
abstract: In a multicellular organism different cell types express a gene in different amounts. Samples from which gene expression levels can be measured typically contain a mixture of different cell types; the resulting measurements thus give only averages over the different cell types present. Based on fluctuations in the mixture proportions from sample to sample it is in principle possible to reconstruct the underlying expression levels of each cell type: to deconvolute the sample. We use a statistical mechanics approach to the problem of deconvoluting such partial concentrations from mixed samples, explore this approach using Markov chain Monte Carlo simulations, and give analytical results for when and how well samples can be unmixed.
authors.affiliation: Institut für Theoretische Physik, University of Cologne - Zülpicher Strasse 77, 50937 Köln, Germany Sybacol, University of Cologne, Germany. nriedel@thp.uni-koeln.de
authors.forename: N
authors.initials: N
authors.lastname: Riedel
authors.forename: J
authors.initials: J
authors.lastname: Berg
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005786:Gene Expression Regulation; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method