OmniPath is a database of molecular biology prior knowledge. It combines data from over 100 resources and builds 5 integrated databases: signaling network (interactions), enzyme-PTM relationships, protein complexes, protein annotations (function, localization, tissue, disease, structure, etc), and intercellular communication roles (e.g. ligand, receptor; intercell). More information is available in our recent manuscript. The OmniPath databases are built by the Python module pypath and are distributed by the web service at omnipathdb.org. The web service can be queried by any HTTP client, or the R/Bioconductor package OmnipathR and a Python client. These clients also provide some convenient methods for post-processing and are also available in Bioconductor and pypi. In addition, the R package provides an integration to NicheNet, a method for inferring ligand-receptor interaction and downstream pathways from scRNA data. Furthermore, the R package contains interface to 20 further resources which are independent from OmniPath.
This is a collection of tutorials, guides and workflows, which aim to help the users to get to know the database contents, the query parameters and to present some applications. If you have questions about these tutorials or experience issues please open an issue on the github page of the relevant package (see above) or contact us by email@example.com.
A detailed workflow on how we run the first case study from the manuscript about SARS-CoV-2 infection of Calu3 cell line.
A detailed workflow on the second case study from the manuscript about communication between five cell types in IBD from scRNA-Seq data.
We use the OmniPath Python web service client together with the squidpy implementation of the CellPhoneDB permutation algorithm to infer ligand-receptor interactions specific for pairs of cell types. We use one built-in scRNA-Seq dataset from the scanpy module.
Here we show how to combine the annotations provided by OmniPath with network data using the OmnipathR R/Bioconductor package. We use tissue level expression data from Human Protein Atlas to build tissue specific networks.
In this workflow we show in detail how to build custom prior knowledge models for NicheNet from both OmniPath data and the resources used in the original NicheNet paper, using the methods in the OmnipathR R/Bioconductor package.
In this tutorial we show how to create intercellular communication networks from custom sets of network connections and intercellular annotations. We present the methods both in R (OmnipathR), by the Python web service client (omnipath Python module) and using the pypath database builder Python module.
OmniPath provides network data with various interaction types and in various datasets. This guide presents the options and gives examples both in R and Python.
Using OmnipathR to build networks downstream of drug targets.
Little code snippets with clear purpose but without detailed explanation.
Pypath is a database building tool responsible for building the OmniPath databases from the original resources. In addition to the APIs for the database it contains many useful utilities.
The pypath book is the most comprehensive guide for pypath, full of examples from various modules and insights about the design of the module.
Saez Lab &
Korcsmaros Lab &