GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis

Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
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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.

Original languageEnglish
Article number39
Pages (from-to)1-5
Number of pages5
JournalJournal of Machine Learning Research
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed


  • Bayesian latent variable modelling
  • biclustering
  • data integration
  • factor analysis
  • multi-view learning

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