IEEE J Biomed Health Inform - A bicluster-based Bayesian principal component analysis method for microarray missing value estimation.

Tópicos

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Resumo

Data generated from microarray experiments often suffer from missing values. As most downstream analyses need full matrices as input, these missing values have to be estimated. Bayesian principal component analysis (BPCA) is a well-known microarray missing value estimation method, but its performance is not satisfactory on datasets with strong local similarity structure. A bicluster-based BPCA (bi-BPCA) method is proposed in this paper to fully exploit local structure of the matrix. In a bicluster, the most correlated genes and experimental conditions with the missing entry are identified, and BPCA is conducted on these biclusters to estimate the missing values. An automatic parameter learning scheme is also developed to obtain optimal parameters. Experimental results on four real microarray matrices indicate that bi-BPCA obtains the lowest normalized root-mean-square error on 82.14% of all missing rates.

Resumo Limpo

data generat microarray experi often suffer miss valu downstream analys need full matric input miss valu estim bayesian princip compon analysi bpca wellknown microarray miss valu estim method perform satisfactori dataset strong local similar structur biclusterbas bpca bibpca method propos paper fulli exploit local structur matrix biclust correl gene experiment condit miss entri identifi bpca conduct biclust estim miss valu automat paramet learn scheme also develop obtain optim paramet experiment result four real microarray matric indic bibpca obtain lowest normal rootmeansquar error miss rate

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