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Published on: February 24, 2023
Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.
Jacob T Mitchell1,2,3,4,5, Orian Stapleton1,2,3,4,6, Kavita Krishnan6,7,8
1Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, United States.
The dominoSignal software introduces statistical testing for differential cell signaling, enabling robust analysis of cell-cell communication networks from single-cell data. This tool enhances comparisons between conditions and identifies significant signaling changes in biological systems.
Area of Science:
- Computational Biology
- Single-cell Analysis
- Systems Biology
Background:
- Ligand-receptor network inference algorithms are crucial for estimating cell-cell communication from single-cell data.
- Existing methods often lack statistical rigor for identifying differential signaling between conditions.
- The Domino algorithm previously integrated gene expression with transcription factor activity for cell communication inference.
Purpose of the Study:
- To develop and introduce dominoSignal, a software package that extends Domino's capabilities.
- To enable statistically rigorous testing of differential cellular signaling.
- To facilitate the analysis of cell-cell communication in response to various perturbations.
Main Methods:
- Development of the dominoSignal software incorporating the Differential Cell Signaling Test (DCST).
- Compilation of active signals into linkages across multiple subjects and testing condition-dependent signaling.
- Application of simulation studies to benchmark data requirements for accurate differential linkage identification.
- Utilized single-cell data from cancer studies to investigate effects of phenotypes and immunotherapy on tumor microenvironment communication.
Main Results:
- dominoSignal enables the compilation and statistical testing of active signaling linkages from multi-subject single-cell datasets.
- The software accommodates datasets with biological or bootstrapped replicates, crucial for datasets with few or pooled subjects.
- Demonstrated the utility of DCST in dominoSignal for inferring communication network changes in cancer and immunotherapy contexts.
Conclusions:
- dominoSignal provides a statistically robust framework for analyzing differential cell signaling.
- The software is broadly applicable across diverse biological systems to understand communication network alterations.
- Facilitates the interpretation of cell-cell communication changes induced by therapeutic or experimental interventions.
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