spfa 0.7.0

Probabilistic factor analysis model with covariate guided factors

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.