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TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data.

ABSTRACT: A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids.Software https://github.com/doraadong/TraSig .

SUBMITTER: Li D

PROVIDER: S-EPMC8900372 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data.

Li Dongshunyi D Velazquez Jeremy J JJ Ding Jun J Hislop Joshua J Ebrahimkhani Mo R MR Bar-Joseph Ziv Z

Genome biology 20220307 1

A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained uniq ...[more]

PMID: 35255944

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