Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis

Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) . Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.


Sparse Latent Factor Model

slfm is a set of tools to find coherent patterns in microarray data using a Bayesian sparse latent factor model. Considerable effort has been put into making slfm fast and memory efficient, turning it an interesting alternative to simpler methods in terms of execution time.

News

slfm 0.2.2

  • changing chain indicator used in alpha estimator

slfm 0.2.1

  • adding % significant column to slfm_list output

  • using S/N/I notation to slfm output

slfm 0.2.0

  • alpha inference now uses just the interested part of the chain

  • user can now choose which type of mixture to use (degenerate TRUE or FALSE)

  • user can now select a lag for MCMC chain

  • new plot_matrix function

slfm 0.1.0

  • new slfm function which fits a model to a matrix of microarray data

  • new slfm_list function which fits models to a set of matrices contained in a folder

  • new process_matrix function which pre-process a set of matrices contained in a folder

Reference manual

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install.packages("slfm")

1.0.1 by Vinicius Mayrink, a year ago


Browse source code at https://github.com/cran/slfm


Authors: Vinicius Mayrink [aut, cre] , Joao Duarte [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp, coda, lattice

Linking to Rcpp, RcppArmadillo


See at CRAN