Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: João Daniel N. Duarte, Vinícius D. Mayrink
Title: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis
Abstract: The development of simulation-based methods, such as Markov chain Monte Carlo (MCMC), has contributed to an increased interest in the Bayesian framework as an alternative to deal with factor models. Many studies have used Bayesian factor analysis to explore gene expression data. We are particularly interested in the application of a sparse latent factor model (SLFM) based on sparsity priors (mixtures) to assess the significance of factors. The SLFM measures how strong the observed coherent expression pattern is in the data, which is an important source of information to evaluate gene activity. In the literature, this type of model has shown better results than other approaches intended for identification of patterns and metagene groups related to the underlying biology. However, a full Bayesian factor model relying on MCMC algorithms has an expensive computational cost, which makes it unattractive for general users. In this paper, we present the package slfm which uses C++ implementation via Rcpp to improve the computational performance of the SLFM within the widely used statistical tool R. We investigate real and simulated microarray data related to breast cancer.

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Paper: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis     Download PDF (Downloads: 502)
slfm_1.0.0.tar.gz: R source package Download (Downloads: 30; 10KB) Replication materials Download (Downloads: 31; 7MB)

DOI: 10.18637/jss.v090.i09

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.