About:
FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows.
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.
Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.
Changes:
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
|
RSS Feed - Blog
