Skip to main content

fava FAVA: Functional Associations using Variational Autoencoders

PyPI version Documentation Status example workflow

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.

Screenshot 2023-08-17 at 10 14 20

Data availability

The Combined Network

Installation:

pip install favapy

favapy as Python library

Read the jupyter-notebook: How_to_use_favapy_in_a_notebook

favapy supports both AnnData objects and count/abundance matrices.

Command line interface

Run favapy from the command line as follows:

favapy <path-to-data-file> <path-to-save-output>

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.

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

-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.


If FAVA is useful for your research, consider citing FAVA BiorXiv.

Other Relevant publications:

The STRING database in 2023.

Release files for favapy 1.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for favapy 1.0.1
File Size Uploaded
favapy-1.0.1.tar.gz 10.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for favapy 1.0.1
File Interpreter ABI Platform
favapy-1.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 18.9 kB

Release files / favapy-1.0.1.tar.gz

Download URL favapy-1.0.1.tar.gz
Size 10.2 kB
Tags Source
SHA-256 checksum
How to use checksums
b72376dc851eb4bfd6be10a60427bc0f7fac1378dbacde87a3983b9722961bda
BLAKE2b-256 checksum
How to use checksums
f0ff5b35f6fb0431df6c6630d49572ab5ce0bc10b850b02c6ef19d0bc2091ec9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.9.19

Release files / favapy-1.0.1-py3-none-any.whl

Download URL favapy-1.0.1-py3-none-any.whl
Size 8.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
35099212c59ebf768557627a66f2cdb8096a7b652b34d5656d824aefe4dc94da
BLAKE2b-256 checksum
How to use checksums
70e47a06208e81b70e16c6717804b8cb313eaa66a63dc70cbedd16e07e8b64d8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.9.19

Release history Release notifications | RSS feed

This release

1.0.1 This release

2 release files

0.4.0

2 release files

0.3.9

2 release files

0.3.8

2 release files

0.3.7

2 release files

0.3.6

2 release files

0.3.5

2 release files

0.3.4

2 release files

0.3.3

2 release files

0.3.2

2 release files

0.3.1

2 release files

0.3.0

2 release files

0.2.1

2 release files

0.2.0

2 release files

0.1.1

1 release file

0.1.0

3 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page