Preferable metric to infer cell-to-cell communication

[JoshuaCumming]

Dear Saeyslab team,
Thanks for this helpful package!

I am currently using your package to analyze a mouse model of cancer which is treated with a vehicle control and a drug. I am first interested to see whether the drug disrupts or promotes communication between cell populations. I have made two-by-two sender to receiver comparisons between cell populations in each condition. I then plan to look at the LFC between conditions for each comparison. I was wondering if using the cumulative AUPR_corrected could serve as a reasonable metric for inferring cell-to-cell communication? The more ligands in the sender population with higher predictive capacity in the receiver population the more likely they are to be communicating with the receiver population? If you think this is incorrect or have any idea of a more suitable metric it would be great to have some feedback!

This is just a jumping off point to then drill down in cell populations which are likely to show differential interaction between conditions where I will use the approaches laid out by your vignettes.

Thanks for any help!

[Eisuan]

Dear Joshua,
thank you for using our tool.

I believe the approach you would like to utilize resembles the classical data-driven exploration for the inference of sender-receiver behaviours, such as the one proposed in the CellChat frameworks. Unfortunately, we currently do not provide filtering criteria to select a specific set of "presumably high-confidence" ligand-receptor interactions. This has partially to do with the variability observed in the AUPR scores among different datasets.

However, NicheNet and CellChat are complementary, because of the different strategies utilized to prioritize ligand-receptor interactions. I would approach the analysis by analyzing the communication dynamics using CellChat to clearly identify putative sender/receiver relationships. Then I would tailor my NicheNet analysis accordingly and I would examine the top-ranked ligands according to NicheNet and compare them with CellChat results.