
 Description:
Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a modelbased technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic nonGaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C++ based on Rcpp package.
 Changes to previous version:
New option nL: maximal number of biclusters per row element; Sort biclusters according to information content; Improved and extended preprocessing; Update to R2.13
 BibTeX Entry: Download
 Corresponding Paper BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Platform Independent
 Data Formats: Any Format Supported By R
 Tags: Bioinformatics, Clustering, Bioconductor, Matrix Factorization, Sparse Learning, Variational Inference, Biclustering, Gene Expression
 Archive: download here
Other available revisons

Version Changelog Date 2.8.0 CHANGES IN VERSION 2.8.0
NEW FEATURES
o rescaling of lapla o extractPlot does not plot sorted matrices
CHANGES IN VERSION 2.4.0
o spfabia bugfixes
CHANGES IN VERSION 2.3.1
NEW FEATURES
o Getters and setters for class Factorization
2.0.0:
 spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
 fabia for nonnegative factorization (parameter: non_negative);
 changed to C and removed dependencies to Rcpp;
 improved update for lambda (alpha should be smaller, e.g. 0.03);
 introduced maximal number of row elements (lL);
 introduced cycle bL when upper bounds nL or lL are effective;
 reduced computational complexity;
 bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;
1.4.0:
 New option nL: maximal number of biclusters per row element;
 Sort biclusters according to information content;
 Improved and extended preprocessing;
 Update to R2.13
October 18, 2013, 10:14:57 2.4.0 CHANGES IN VERSION 2.4.0
o spfabia bugfixes
CHANGES IN VERSION 2.3.1
NEW FEATURES
o Getters and setters for class Factorization
2.0.0:
 spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
 fabia for nonnegative factorization (parameter: non_negative);
 changed to C and removed dependencies to Rcpp;
 improved update for lambda (alpha should be smaller, e.g. 0.03);
 introduced maximal number of row elements (lL);
 introduced cycle bL when upper bounds nL or lL are effective;
 reduced computational complexity;
 bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;
1.4.0:
 New option nL: maximal number of biclusters per row element;
 Sort biclusters according to information content;
 Improved and extended preprocessing;
 Update to R2.13
December 20, 2012, 14:20:58 2.0.0 2.0.0:
 spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
 fabia for nonnegative factorization (parameter: non_negative);
 changed to C and removed dependencies to Rcpp;
 improved update for lambda (alpha should be smaller, e.g. 0.03);
 introduced maximal number of row elements (lL);
 introduced cycle bL when upper bounds nL or lL are effective;
 reduced computational complexity;
 bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;
1.4.0:
 New option nL: maximal number of biclusters per row element;
 Sort biclusters according to information content;
 Improved and extended preprocessing;
 Update to R2.13
November 10, 2011, 17:09:15 1.4.0 New option nL: maximal number of biclusters per row element; Sort biclusters according to information content; Improved and extended preprocessing; Update to R2.13
July 15, 2011, 14:42:00 1.0.0 Initial Announcement on mloss.org.
July 28, 2010, 17:17:16
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