PT  - JOURNAL ARTICLE
AU  - Dongshunyi Li
AU  - Jeremy J. Velazquez
AU  - Jun Ding
AU  - Joshua Hislop
AU  - Mo R. Ebrahimkhani
AU  - Ziv Bar-Joseph
TI  - Inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data
AID  - 10.1101/2021.07.28.454054
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.07.28.454054
4099  - http://biorxiv.org/content/early/2021/07/29/2021.07.28.454054.short
4100  - http://biorxiv.org/content/early/2021/07/29/2021.07.28.454054.full
AB  - A major advantage of single cell RNA-Sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. To date, such ordering was mainly used to group cells and to infer interactions within cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies. Unlike prior methods that only focus on the average expression levels of genes in clusters or cell types, TraSig fully 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. As we show, using the ordering information allows TraSig to obtain 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 organoid.Competing Interest StatementM.R.E and J.J.V. have a patent (WO2019237124) for the organoid technology used in this publication.