GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Eemeli Lepp\"aaho, Muhammad Ammad-ud-din, Samuel Kaski

TL;DR
GFA is an R package that performs exploratory factor analysis on multiple co-occurring data sources, revealing dependencies and interpretable biclusters through sparse priors.
Contribution
The paper introduces GFA, a comprehensive R package for multi-source data factor analysis with dependency learning and sparse priors for interpretability.
Findings
Successfully identifies dependencies between data sources
Produces interpretable biclusters of multi-source data
Provides a complete analysis pipeline in R
Abstract
The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the multi-source data
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Taxonomy
TopicsGene expression and cancer classification · Bayesian Methods and Mixture Models · Neural Networks and Applications
