
 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. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters.
FABIA is an R package while the code is written in C.
Applications:
 microarray: genes that are diffentially expressed in certain samples form a bicluster with these samples, e.g. genes of a pathway that is activated in certain samples.

genetics: researchers want to identify haplotypes shared by different individuals due to >identity by descent<. Especially rare variants in next generation sequencing that form an identity by descent block are identified by fabia (spfabia).
 Changes to previous version:
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
 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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