Metadata-Version: 2.1
Name: favapy
Version: 0.3.8.2
Summary: Infer Functional Associations using Variational Autoencoders on -Omics data.
Home-page: https://github.com/mikelkou/VAE_Functional_associations
Author: Mikaela Koutrouli
Author-email: mikaela.koutrouli@cpr.ku.dk
Project-URL: Bug Tracker, https://github.com/mikelkou/VAE_Functional_associations/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# FAVA: Functional Associations using Variational Autoencoders
![Fava](https://user-images.githubusercontent.com/81096946/177743627-2e6a7447-3fc1-48a8-a6bb-003a3ace223a.png)

Protein networks are commonly used for understanding the interplay between proteins in the cell as well as for visualizing omics data. Unfortunately, most existing high-quality networks are heavily biased by data availability, in the sense that well-studied proteins have many more interactions than understudied proteins. To create networks that can help elucidate functions for the latter, we must start from data that are not affected by this literature bias, in other words, from omics data such as single cell RNA-seq (scRNA-seq) and proteomics. While networks can be inferred from such data through simple co-expression analysis, this approach does not work well due to high sparseness (many transcripts/proteins are not consistently observed in each cell/sample) and redundancy (many similar cells/samples are analyzed) of such data. We have therefore developed FAVA, Functional Associations using Variational Autoencoders, which deals with both issues by compressing these high-dimensional data into a dense, low-dimensional latent space. We demonstrate that calculating correlations in this latent space results in much improved networks compared to the original representation for large-scale scRNA-seq and proteomics data from the Human Protein Atlas, and from PRIDE, respectively. We show that these networks, which given the nature of the input data should be free of literature bias, indeed have much better coverage of understudied proteins than existing networks.


## Data availability
#### The Combined Network: https://doi.org/10.5281/zenodo.6803472 

#### Relevant publications:
FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data
https://doi.org/10.1101/2022.07.06.499022

The STRING database in 2023
https://doi.org/10.1093/nar/gkac1000


## Installation:
`pip install favapy`

## favapy as Python library
Read the jupyter-notebook: How_to_use_favapy_in_a_notebook
It supports both AnnData objects and counts matrices without any preprocessing setps.


## 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'.

`-c` The cut-off on the Pearson Correlation scores. Default value = 0.7.

`-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.





