FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data.

Mikaela Koutrouli Katerina C NastouPau Piera LíndezRobbin BouwmeesterSimon RasmussenLennart MartensLars Juhl Jensen

Published in: Bioinformatics (Oxford, England) (2024)

Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI <pip install favapy> as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

Keyphrases

climate change
electronic health record
human health
genome wide
single cell
rna seq
big data
machine learning
gene expression
risk assessment