FAVA, which stands for Functional Associations using Variational Autoencoders, 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.
pip install favapy
Read the jupyter-notebook: How_to_use_favapy_in_a_notebook
favapy supports both AnnData objects and count/abundance matrices.
Run favapy from the command line as follows:
favapy <path-to-data-file> <path-to-save-output>
-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 Pearson 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.