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CellPhoneDB addresses the challenges of studying cell-cell communication in scRNA-seq and spatial data. It allows researchers to move beyond just measuring gene expression and delve into the complicated cellular communication world. By analyzing the scRNA-seq or spatial data through the lens of CellPhoneDB, researchers can identify potential signaling pathways and communication networks between different cell types within the sample. Partek^® Flow^® wrapped the statistical analysis pipeline (method 2) from CellPhoneDB v5 [1][2] for this purpose. 

The authors state that, "a distinctive feature of CellphoneDB is that the subunit architecture of either ligands and receptors is taken into account, representing heteromeric complexes accurately. This is crucial, as cell communication relies on multi-subunit protein complexes that go beyond the binary representation used in most databases and studies. CellphoneDB also incorporates biosynthetic pathways in which we use the last representative enzyme as a proxy of ligand abundance, by doing so, we include interactions involving non-peptidic molecules. CellphoneDB includes only manually curated & reviewed molecular interactions with evidenced role in cellular communication." [2]

How to use CellPhoneDB in Partek Flow

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The interactions come from the CellphoneDB database. It is manually curated repository using reviewed molecular interactions with demonstrated evidence for a role in cellular communication. [5] 

References

1. Troule, etc (2023). CellPhoneDB v5: Inferring cell-cell communication from single cell multiomics data. https://arxiv.org/pdf/2311.04567.pdf
2. https://github.com/ventolab/CellphoneDB 
3. https://github.com/ventolab/CellphoneDB/blob/master/docs/RESULTS-DOCUMENTATION.md 
4. https://cellphonedb.readthedocs.io/en/latest/RESULTS-DOCUMENTATION.html#why-values-of-clustera-clusterb-are-different-to-the-values-of-clusterb-clustera 
5. https://github.com/ventolab/cellphonedb-data 

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