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Related Experiment Video

Updated: May 8, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Hirak Sarkar^1,2, Uthsav Chitra¹, Julian Gold^1,3

¹Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

Summary

This summary is machine-generated.

Copulacci infers cell-cell interactions (CCIs) from spatially resolved transcriptomics (SRT) data. This new count-based model accurately identifies lowly expressed ligand-receptor interactions, outperforming existing methods.

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Last Updated: May 8, 2026

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Published on: July 6, 2022

Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Cell-cell interactions (CCIs) are crucial for biological functions.
Inferring CCIs from gene expression data is challenging due to low transcript counts in spatially resolved transcriptomics (SRT).

Purpose of the Study:

To develop a novel computational model for inferring CCIs from SRT data.
To address the challenge of low transcript counts in CCI inference.

Main Methods:

Introduced Copulacci, a count-based model utilizing Gaussian copula.
Modeled dependencies between ligand and receptor expression in nearby spatial locations.
Evaluated on simulated and real SRT datasets.

Main Results:

Copulacci outperforms existing methods (Spearman, Pearson) on simulated data.
Identified biologically meaningful, lowly expressed ligand-receptor interactions in real SRT data.
Discovered interactions missed by conventional CCI inference approaches.

Conclusions:

Copulacci provides a robust method for inferring CCIs from SRT data, especially with low expression.
The model enhances the discovery of complex cell-cell communication networks.