CINS: Cell Interaction Network Inference from Single cell expression data in lungs of yound and aged mice

We report the development of a new computational method to assess differences in cell-cell interactions between conditions through utilizing single-cell RNA sequencing data. The pipeline, known as Cell Interaction Network Inference from Single-cell Expression data (CINS), combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie these interactions. Overall design: The CINS pipeline was constructed and tested on a human single-cell dataset from Adams et al., 2020 on idiopathic pulmonary fibrosis (IPF). Additionally, the pipeline was applied to a pilot single-cell RNA sequencing dataset focused on studying lung aging in FENDRR-floxxed mice.