slfm

Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis

Vinicius Mayrink, Joao Daniel Nunes Duarte

Last update: 28 November 2023

Copyright license: GNU General Public License, version 2.0

Software version identifier: 1.0.1, 0.1, 0.2.0, 0.2.1, 0.2.2, 0.2.3, 1.0.0, 1.0.2

Source code repository: https://github.com/cran/slfm

Description

Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <doi:10.1007/978-3-319-12454-4_15>. 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.

Cites work

Factor Analysis with Mixture Modeling to Evaluate Coherent Patterns in Microarray Data

This page was built for software: slfm