Description:

Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based 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:

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 non-negative 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

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 non-negative 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 non-negative 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 non-negative 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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