https://github.com/mikelkou/fava

Functional Associations using Variational Autoencoders
https://github.com/mikelkou/fava

co-expression deep-learning networks networks-biology proteomics single-cells

Last synced: 3 months ago
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Functional Associations using Variational Autoencoders

• Host: GitHub
• URL: https://github.com/mikelkou/fava
• Owner: mikelkou
• License: mit
• Created: 2022-03-14T10:15:43.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2026-01-19T22:03:35.000Z (5 months ago)
• Last Synced: 2026-01-20T05:19:27.090Z (5 months ago)
• Topics: co-expression, deep-learning, networks, networks-biology, proteomics, single-cells
• Language: Jupyter Notebook
• Homepage:
• Size: 176 MB
• Stars: 43
• Watchers: 1
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
#  FAVA: Functional Associations using Variational Autoencoders

[![PyPI version](https://badge.fury.io/py/favapy.svg)](https://badge.fury.io/py/favapy)

[![Documentation Status](https://readthedocs.org/projects/fava/badge/?version=latest)](https://fava.readthedocs.io/en/latest/?badge=latest)

![example workflow](https://github.com/github/docs/actions/workflows/test.yml/badge.svg)

![scverse](https://img.shields.io/badge/scverse-ecosystem-black.svg?logo=data:image/svg%2bxml;base64,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)

FAVA is a method used to construct protein networks based on omics data such as single-cell RNA sequencing (scRNA-seq) and proteomics. Existing protein networks are often biased towards well-studied proteins, limiting their ability to reveal functions of understudied proteins. FAVA addresses this issue by leveraging omics data that are not influenced by literature bias.

Read the [documentation](https://fava.readthedocs.io/en/latest/).

![Screenshot 2023-08-17 at 10 14 20](https://github.com/mikelkou/fava/assets/81096946/416deeeb-ce89-4ed7-8e2a-6703616552ab)

## Data availability

[The Combined Network](https://doi.org/10.5281/zenodo.6803472)

## Installation:

```

pip install favapy

```

## favapy as Python library

Read the [How_to_use_favapy_in_a_notebook](https://github.com/mikelkou/fava/blob/main/How_to_use_favapy_in_a_notebook.ipynb) or/and the [documentation](https://fava.readthedocs.io/en/latest/).

Relevant parameters for [fava.cook](https://fava.readthedocs.io/en/latest/API.html#fava.cook).

favapy supports both AnnData objects and count/abundance matrices.

## Command line interface

Run favapy from the command line as follows:

```

favapy  

```

#### Optional parameters:

```

-t Type of input data ('tsv' or 'csv'). Default value = 'tsv'.

-n The number of interactions in the output file (with both directions, proteinA-proteinB and proteinB-proteinA). Default value = 100000.

-c The cut-off on the Correlation scores.The scores can range from 1 (high correlation) to -1 (high anti-correlation). This option overwrites the number of interactions. Default value = None.

-d The dimensions of the intermediate\hidden layer. Default value depends on the input size.

-l The dimensions of the latent space. Default value depends on the size of the hidden layer.

-e The number of epochs. Default value = 50.

-b The  batch size. Default value = 32.

-cor Type of correlation method ('pearson' or 'spearman'). Default value = 'pearson'

```

If FAVA is useful for your research, consider citing [FAVA BiorXiv](https://doi.org/10.1101/2022.07.06.499022).

#### Other Relevant publications:

[The STRING database in 2023](https://doi.org/10.1093/nar/gkac1000)