title: A bicluster-based Bayesian principal component analysis method for microarray missing value estimation.
abstract: 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.
authors.forename: Fanchi
authors.initials: F
authors.lastname: Meng
authors.forename: Cheng
authors.initials: C
authors.lastname: Cai
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*; D015003:Yeasts / Q000235:genetics / Q000378:metabolism