Tools for Fitting Sparse Latent Factor Model

Set of tools to find coherent patterns in microarray data using a Bayesian sparse latent factor model (Duarte and Mayrink 2015 - http://link.springer.com/chapter/10.1007%2F978-3-319-12454-4_15). 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. It implements versions of the SLFM using both type of mixtures: using a degenerate distribution or a very concentrated normal distribution for the spike part of the mixture. It also implements additional functions to help pre-process the data and fit the model for a large number of arrays.


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")

0.2.2 by Joao Duarte, 4 years ago


https://github.com/jdanielnd/slfm


Report a bug at https://github.com/jdanielnd/slfm/issues


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


Authors: Joao Duarte and Vinicius Mayrink


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp, coda, lattice

Linking to Rcpp, RcppArmadillo


See at CRAN