{"id":34085695,"url":"https://github.com/mikelkou/fava","last_synced_at":"2026-03-12T05:31:44.635Z","repository":{"id":57428761,"uuid":"469679951","full_name":"mikelkou/fava","owner":"mikelkou","description":"Functional Associations using Variational Autoencoders","archived":false,"fork":false,"pushed_at":"2026-01-19T22:03:35.000Z","size":184182,"stargazers_count":43,"open_issues_count":3,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-20T05:19:27.090Z","etag":null,"topics":["co-expression","deep-learning","networks","networks-biology","proteomics","single-cells"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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alt=\"fava\" width=\"150\"/\u003e FAVA: Functional Associations using Variational Autoencoders\n\n\n[![PyPI version](https://badge.fury.io/py/favapy.svg)](https://badge.fury.io/py/favapy)\n[![Documentation Status](https://readthedocs.org/projects/fava/badge/?version=latest)](https://fava.readthedocs.io/en/latest/?badge=latest)\n![example workflow](https://github.com/github/docs/actions/workflows/test.yml/badge.svg)\n![scverse](https://img.shields.io/badge/scverse-ecosystem-black.svg?logo=data:image/svg%2bxml;base64,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)\n\n\u003c!-- ![Fava](https://user-images.githubusercontent.com/81096946/177743627-2e6a7447-3fc1-48a8-a6bb-003a3ace223a.png) --\u003e\nFAVA 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.\nRead the [documentation](https://fava.readthedocs.io/en/latest/).\n\n\n![Screenshot 2023-08-17 at 10 14 20](https://github.com/mikelkou/fava/assets/81096946/416deeeb-ce89-4ed7-8e2a-6703616552ab)\n\n\n## Data availability\n[The Combined Network](https://doi.org/10.5281/zenodo.6803472)\n\n## Installation:\n```\npip install favapy\n```\n\n## favapy as Python library\nRead 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/).\nRelevant parameters for [fava.cook](https://fava.readthedocs.io/en/latest/API.html#fava.cook).\n\nfavapy supports both AnnData objects and count/abundance matrices.\n\n\n## Command line interface\nRun favapy from the command line as follows:\n```\nfavapy \u003cpath-to-data-file\u003e \u003cpath-to-save-output\u003e\n```\n\n#### Optional parameters:\n```\n\n-t Type of input data ('tsv' or 'csv'). Default value = 'tsv'.\n\n-n The number of interactions in the output file (with both directions, proteinA-proteinB and proteinB-proteinA). Default value = 100000.\n\n-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.\n\n-d The dimensions of the intermediate\\hidden layer. Default value depends on the input size.\n\n-l The dimensions of the latent space. Default value depends on the size of the hidden layer.\n\n-e The number of epochs. Default value = 50.\n\n-b The  batch size. Default value = 32.\n\n-cor Type of correlation method ('pearson' or 'spearman'). Default value = 'pearson'\n\n\n```\n\nIf FAVA is useful for your research, consider citing [FAVA BiorXiv](https://doi.org/10.1101/2022.07.06.499022).\n\n#### Other Relevant publications:\n[The STRING database in 2023](https://doi.org/10.1093/nar/gkac1000)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikelkou%2Ffava","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmikelkou%2Ffava","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikelkou%2Ffava/lists"}