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:

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

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