PT  - JOURNAL ARTICLE
AU  - Yuan, Ye
AU  - Cosme, Carlos
AU  - Adams, Taylor Sterling
AU  - Schupp, Jonas
AU  - Sakamoto, Koji
AU  - Xylourgidis, Nikos
AU  - Ruffalo, Matthew
AU  - Kaminski, Naftali
AU  - Bar-Joseph, Ziv
TI  - CINS: Cell Interaction Network inference from Single cell expression data
AID  - 10.1101/2021.02.22.432206
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.22.432206
4099  - http://biorxiv.org/content/early/2021/02/25/2021.02.22.432206.short
4100  - http://biorxiv.org/content/early/2021/02/25/2021.02.22.432206.full
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.