ArticlePLoS computational biology2022
CINS: Cell Interaction Network inference from Single cell expression data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Unraveling cell-cell communication with NicheNet by inferring active ligands from transcriptomics data.Nature protocols · 2025Review
- scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding.PLoS computational biology · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data.BMC bioinformatics · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
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 mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.
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Registered trials
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