#cellcellinteraction — Public Fediverse posts

Has anyone looked at CINS (https://doi.org/10.1371/journal.pcbi.1010468)?

Learns a Bayesian network of cell type dependencies from changes in cell type proportions (#scRNAseq) in case-control studies. Causal ligands from cell-cell edges are predicted by LASSO regression of #ligand and predicted response genes (using NicheNet's ligand-reponse network).
Cool idea, weird lack of validation in their paper... But I guess that's the challenge in #CellCellInteraction prediction - validation is _hard_

Finally read the REMI paper by Alice Yu (aliceomics@twitter) et al. Calculating the partial correlation structure of #ligand #receptor interactions across #scRNAseq samples (cancer, in their case) to _more_specifically_ identify context-dependent interactions. #CellCellInteraction prediction has a specificity problem, and this method outperforms NicheNet, which uses predicted transcriptional response to improve specificity of predictions.

https://twitter.com/PlevritisLab/status/1509196799520620544?s=20&t=6jB2saz7rcO2u-4Gh54C4Q

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010715

I reviewed this paper, and was pretty excited about it. I don't do any #spatial #scRNAseq, but the tools they've developed for #CellCellInteraction inference in C. elegans (stereotyped cellular loactions + published scRNAseq atlas = genius test-bed for spatial inference methods) are powerful and neato.

Author's tweet: https://twitter.com/eagut/status/1593334779134386176?s=20&t=abTEVMJ2ngD0IXF5RC2Bpg

#scTensor detects many-to-many cell–cell interactions from #single #cell #RNA-sequencing data.

https://doi.org/10.1101/2022.12.07.519225

#ManyToMany #CellCellInteraction #scRNAseq