Bayesian biclustering of gene expression data

Abstract

Background: Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models.

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

“…However, this method works only in one dimension, considering either samples or variables, and not if both directions are to be considered simultaneously, as we wish to do here. On the other hand, biclustering or block clustering (24)(25)(26)(27)(28) is an approach to simultaneously cluster row and column variables, deriving local subgroups within a data matrix. Many biclustering methods also may be adapted so that not all samples and variables need to be grouped (only ones that are ''similar enough'' according to some predefined measure).…”

“…These approaches have been used previously in discovering local patterns in gene expression data. (25,26) Based on a Bayesian statistical model framework proposed by Gu and Liu, (28) but with modifications to increase the robustness and efficiency of the model for dealing with the more massive GWAS data, we developed a fast and accurate algorithm to identify block clusters of single-nucleotide polymorphisms (SNP) and traits in data arising from GWAS. (29) We applied this method to results from bone and muscle traits from the Framingham Osteoporosis Study.…”

Simultaneous Clustering: A Survey

“…cMonkey [29] is a generative approach which models biclusters by Markov chain processes. Gu and Liu [19] generalized the plaid models proposed in [22] to fully generative models called Bayesian BiClustering model (BBC). The latter models introduced in [6] and [19] are generative models which have the advantage that they select models using well-understood model selection techniques such as maximum likelihood.…”

Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

“…A primeira depende de análise biológica e interpretação por especialistas da área (Prelić et al, 2006). Os especialistas usam métodos de visualização (e.g., gráficos de coordenadas paralelas e mapas de calor da matriz de dados X) e dependem de um prévio conhecimento sobre os genes e condições biológi-cas da base de dados (Nepomuceno et al, 2007;Santamaría et al, 2008;Gu e Liu, 2008;Cheng et al, 2008;Dharan e Nair, 2009). Esse método é frequentemente acompanhado de outras abordagens devido à sua natureza subjetiva e é inviável quando vários algoritmos são comparados (Prelić et al, 2006).…”

“…O algoritmo bbc (Gu e Liu, 2008) encontrou apenasḂ 2 eḂ 3 , mas a Tabela 6.12 mostra que várias medidas avaliaram a solução do algoritmo bbc como perfeita, o que pode ser novamente explicado pela ausência da propriedade de cobertura dada pela Def. 2.…”