bioFAM/MOFA2

Multi-Omics Factor Analysis

What it solves

It provides a general framework for the unsupervised integration of multi-omic data sets, allowing researchers to discover patterns across different biological data types.

How it works

MOFA uses a factor analysis model to integrate multiple data sets in an unsupervised manner, identifying common patterns (factors) that explain variance across the different omics layers.

Who it’s for

Bioinformaticians and biological researchers who need to integrate and analyze multi-omic data sets.

Highlights

  • Unsupervised integration of multi-omic data.
  • General framework for factor analysis.
  • Available as an R package via Bioconductor and has a Python implementation (mofapy2).

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