MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion. Intuitively, MOFA can be viewed as a versatile and …

Training a model with mofapy2: a Jupyter notebook demonstrating how to train a MOFA model using simple simulated data. It uses stand-alone mofapy2 to train the model and mofax for …

The core of MOFA is implemented in the Python package mofapy2, but we recommend to use the R package MOFA2 which provides an interface to train a MOFA model with R and run the …

The MOFA factors capture the global sources of variability in the data. Mathematically, each factor ordinates cells along a one-dimensional axis centered at zero.

MEFISTO was integrated into the MOFA framework and you can follow our installation instructions for MOFA, which provides details on the R and Python package installation as well …

MOFA training can be massively speed up by using GPUs. We have implemented all computations usin CuPy, an open-source array library for GPU-accelerated computing with …

MOFA Multi-Omics Factor Analysis Home Installation Tutorials Interactive web server FAQ Troubleshooting MEFISTO MOFAcell News GPU training Documentation Contact GitHub R …

The MOFA package was deprecated and replaced by MOFA2 1.0.0: The following new features are now available: Multi-group functionality: intuitively, this functionality breaks the assumption …

Multicellular factor analysis (MOFAcell) repurposes MOFA to estimate cross-condition multicellular programs from single-cell data. These multicellular programs represent …

I get the following error when running run_mofa AttributeError: 'module' object has no attribute 'core.entry_point Error in py_module_import(module, convert = convert) : …