RT Journal Article
SR Electronic
T1 CINS: Cell Interaction Network inference from Single cell expression data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.22.432206
DO 10.1101/2021.02.22.432206
A1 Yuan, Ye
A1 Cosme, Carlos
A1 Adams, Taylor Sterling
A1 Schupp, Jonas
A1 Sakamoto, Koji
A1 Xylourgidis, Nikos
A1 Ruffalo, Matthew
A1 Kaminski, Naftali
A1 Bar-Joseph, Ziv
YR 2021
UL http://biorxiv.org/content/early/2021/02/25/2021.02.22.432206.abstract
AB Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging human dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.