Probabilistic factor analysis model with covariate guided factors
Project description
spFA
Introduction
Here we present semi-supervised probabilistic Factor Analysis (spFA), a multi-omics integration method, which infers a set of low dimensional latent factors that represent the main sources of variability. spFA enables the discovery of primary sources of variation while adjusting for known covariates and simultaneously disentangling variation that is shared between multiple omics modalities and specific to single modalities. The spFA method is implemented in python using the pyro framework for probabilistic programming.
Installation
To install spfa
first create Python 3.8
environment e.g. by
conda create --name spfa-env python=3.8
conda activate spfa-env
and install the package using
pip install spfa
How to use spfa
for multi-omics analyses
A detailed manual with examples and how to use spfa
can be found here https://tcapraz.github.io/spFA/index.html.
Project details
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