A Composite interaction Score: Prioritizing cell–cell interactions from single-cell RNA-seq with application to pre-menopausal epithelial barriers

• Developed the Composite Interaction Score (CIS) , a ranking framework that integrates outputs from multiple CCI inference tools. • CIS achieves higher precision at early ranks than naïve consensus approaches, improving prioritization of biologically relevant CCIs. • Enables generalizable metanalysis across tissues , enhancing interpretability of CCI. • predictions in epithelial barriers. Cell-cell interactions (CCIs) govern tissue homeostasis, immune regulation, and barrier defense. To study these processes, multiple computational tools have been developed to infer CCIs from single-cell RNA-sequencing (scRNA-seq) data, but each tool relies on distinct scoring metrics, making their results difficult to reconcile. Consequently, no standard framework exists for prioritizing predicted CCIs. Simple consensus strategies, such as averaging ranks, treat agreement across the entire spectrum equally and often fail to highlight the most biologically meaningful CCIs. We aimed to develop a ranking strategy that emphasizes reproducibility and biological relevance of CCIs. We then apply this approach to CCIs in epithelial barrier tissues as a model system. We introduce the Composite Interaction Score (CIS), a consensus metric that integrates predictions from six established cell–cell inference (CCI) methods via the LIgand-receptor ANAlysis (LIANA) package. CIS employs ranked-biased precision to weight agreement among tools, prioritizing concordance at the top of ranked lists. We benchmarked CIS against a naïve average-rank baseline using perturbed dataset with artificially overexpressed interactions, assessing performance through precision and recall analyses. CIS consistently outperformed the average-rank baseline, recovering true overexpressed CCIs with higher sensitivity and specificity. When we applied CIS to mine CCIs from scRNA-seq datasets from the intestine, skin, and uterus, CIS highlighted both conserved and tissue-specific CCIs. MIF–CD74 and APP–CD74 emerged as top conserved interactions across epithelial, immune, and stromal compartments. In contrast, GUCA2A/GUCA2B–GUCY2C mediated intestinal epithelial-endocrine crosstalk, HLA–KIR3DL1 ranked highly between keratinocytes and NK cells in skin, and SPP1–PTGER4 signaling between ciliated epithelial and myeloid cells suggested anti-inflammatory regulation in uterine tissue. CIS provides a generalizable framework for prioritizing CCIs from scRNA-seq data, outperforming naïve consensus strategies by emphasizing reproducibility at the top of ranked lists. Its application to epithelial barriers establishes a reference resource that distinguishes conserved from tissue-specific communication networks, offering new insights into barrier tissue and pre-menopausal biology.