Clustering Gene Expression Data using a Posterior Split‐Merge‐Birth Procedure

Saraiva, Erlandson Ferreira; Milan, Luís Aparecido

doi:10.1111/j.1467-9469.2011.00765.x

Cited by 14 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…, T . See for example, Baldi and Long (2001), Fox and Dimmic (2006), Kim et al (2013), Saraiva and Milan (2012), Louzada et al (2014), and Oh (2015), among others.…”

Section: Hierarchical Bayesian Modelmentioning

confidence: 99%

Identifying differentially expressed genes using the Polya urn scheme

Saraiva

Suzuki

Milan

2017

CSAM

View full text Add to dashboard Cite

A common interest in gene expression data analysis is to identify genes that present significant changes in expression levels among biological experimental conditions. In this paper, we develop a Bayesian approach to make a gene-by-gene comparison in the case with a control and more than one treatment experimental condition. The proposed approach is within a Bayesian framework with a Dirichlet process prior. The comparison procedure is based on a model selection procedure developed using the discreteness of the Dirichlet process and its representation via Polya urn scheme. The posterior probabilities for models considered are calculated using a Gibbs sampling algorithm. A numerical simulation study is conducted to understand and compare the performance of the proposed method in relation to usual methods based on analysis of variance (ANOVA) followed by a Tukey test. The comparison among methods is made in terms of a true positive rate and false discovery rate. We find that proposed method outperforms the other methods based on ANOVA followed by a Tukey test. We also apply the methodologies to a publicly available data set on Plasmodium falciparum protein.

show abstract

“…, T . See for example, Baldi and Long (2001), Fox and Dimmic (2006), Kim et al (2013), Saraiva and Milan (2012), Louzada et al (2014), and Oh (2015), among others.…”

Section: Hierarchical Bayesian Modelmentioning

confidence: 99%

Identifying differentially expressed genes using the Polya urn scheme

Saraiva

Suzuki

Milan

2017

CSAM

View full text Add to dashboard Cite

show abstract

“…Bayesian MC Monte Carlo (MCMC)‐based clustering methods typically employing random split/merge/move‐type search operators (e.g. Dawson and Belkhir, ; Corander et al ., ; Saraiva and Milan, ) represent a class of possible approaches to traverse the space of partitions to obtain the MAP estimate. However, such operators easily become numerically inefficient for larger state spaces, requiring impractically long simulations to be pursued, and therefore, one can alternatively use more efficient search operators that make intelligent data‐driven proposals as discussed in, for example, Tu & Zhu (), Corander & Marttinen () and Corander et al .…”

Section: Inference For Sparse Markov Chain Modelsmentioning

confidence: 99%

Sparse Markov Chains for Sequence Data

Jääskinen

Xiong

Corander

et al. 2013

Scandinavian J Statistics

View full text Add to dashboard Cite

Finite memory sources and variable‐length Markov chains have recently gained popularity in data compression and mining, in particular, for applications in bioinformatics and language modelling. Here, we consider denser data compression and prediction with a family of sparse Bayesian predictive models for Markov chains in finite state spaces. Our approach lumps transition probabilities into classes composed of invariant probabilities, such that the resulting models need not have a hierarchical structure as in context tree‐based approaches. This can lead to a substantially higher rate of data compression, and such non‐hierarchical sparse models can be motivated for instance by data dependence structures existing in the bioinformatics context. We describe a Bayesian inference algorithm for learning sparse Markov models through clustering of transition probabilities. Experiments with DNA sequence and protein data show that our approach is competitive in both prediction and classification when compared with several alternative methods on the basis of variable memory length.

show abstract

“…Recentemente, este tipo de modelagem tem sido utilizada em diversas aplicações, tais como, em análises genômicas (BROËT et al, 2008;SARAIVA e MILAN, 2012), análises epidemiológicas (DIEBOLT e ROBERT, 1994;GREEN e RICHARDSON, 2002), análises econômicas (JEDIDI et al, 1997;ALLENBY et al, 1998), análise de finanças (LAMOREUX e LATSRAPS, 1994;ROBERT et al, 2000), segmentação de imagens (ZHOU e ZHU, 2018), entre outras.…”

Section: Introductionunclassified

Untitled

Zucareli¹,

Saraiva²,

Suzuki³

2018

Rev. Bras. Biom.

View full text Add to dashboard Cite

RESUMO: Nosúltimos anos houve um crescente interesse em modelagens utilizando modelos de mistura de distribuições devidoàs suas flexibilidades. Neste artigo, apresentamos um tutorial sobre modelos de mistura e o método de estimação baseado na função de verossimilhança. Como uma das principais questões no procedimento de estimação dos parâmetros de um modelo de misturaé a determinação do número de componentes k, apresentamos uma discussão sobre a determinação de k utilizando os critérios de seleção de modelos AIC, AICc e BIC. Para verificar a performance dos critérios de seleção de modelos na determinação de k, desenvolvemos um estudo de simulação Monte Carlo considerando os modelos de mistura de distribuições normal, Poisson e Gama. Os resultados obtidos mostram uma complementaridade entre os critérios e que estes devem ser utilizados com certa cautela, pois apresentam porcentagens de acerto inferiores a 70% na maioria dos casos estudados. Aplicamos o método de estimação e os critérios de seleção de modelos a três conjuntos de dados reais. PALAVRAS-CHAVE: Modelo de mistura; estimador de Máxima verossimilhança; algoritmo EM estocástico; seleção de modelos.

show abstract

Clustering Gene Expression Data using a Posterior Split‐Merge‐Birth Procedure

Cited by 14 publications

References 28 publications

Identifying differentially expressed genes using the Polya urn scheme

Identifying differentially expressed genes using the Polya urn scheme

Sparse Markov Chains for Sequence Data

Untitled

Contact Info

Product

Resources

About