slfm_package: slfm: Fitting a Bayesian Sparse Latent Factor Model in Gene...

In slfm: Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis

Description

Set of tools to find coherent patterns in gene expression (possibly 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 slfm fast and memory efficient, 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. Additional functions are also available to handle data pre-processing procedures and to fit the model for a large number of probesets or genes. It includes functions to:

Details

* pre-process a set of matrices; * fit the available models to a set of matrices; * provide a detailed summarization of the model fit results.

slfm documentation built on March 26, 2020, 7:37 p.m.