A count-based model for delineating cell–cell interactions in spatial transcriptomics data

• Published in: Bioinformatics (Oxford, England) Vol. 40; no. Supplement_1; pp. i481 - i489
• Main Authors: Sarkar, Hirak, Chitra, Uthsav, Gold, Julian, Raphael, Benjamin J
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 28.06.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Availability; Cell Communication; Cell interactions; Computational Biology - methods; Correlation coefficient; Correlation coefficients; Gene expression; Gene Expression Profiling - methods; Gene sequencing; Homeostasis; Humans; Inference; Ligands; Receptors; Single-Cell Analysis - methods; Spatial data; Transcriptome - genetics; Transcriptomics
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btae219

Motivation Cell–cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand–receptor correlations can be computed between cells or spots that are physically close in the tissue. However, the transcript counts of individual ligands and receptors in SRT data are generally low, complicating the inference of CCIs from expression correlations. Results We introduce Copulacci, a count-based model for inferring CCIs from SRT data. Copulacci uses a Gaussian copula to model dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low. On simulated data, Copulacci outperforms existing CCI inference methods based on the standard Spearman and Pearson correlation coefficients. Using several real SRT datasets, we show that Copulacci discovers biologically meaningful ligand–receptor interactions that are lowly expressed and undiscoverable by existing CCI inference methods. Availability and implementation Copulacci is implemented in Python and available at https://github.com/raphael-group/copulacci.