FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in...

Area of Science:

• Computational Biology
• Single-cell Genomics
• Systems Biology

Background:

• Understanding cell-cell communications (CCCs) is crucial for deciphering multicellular organism function.
• Existing methods for CCC detection in single-cell transcriptomics often face scalability and robustness challenges.

Purpose of the Study:

• To introduce FastCCC, a novel permutation-free framework for scalable, robust, and reference-based CCC analysis.
• To enable the identification of critical CCCs and uncover deeper biological insights from single-cell data.

Main Methods:

• FastCCC utilizes fast Fourier transformation-based convolution for analytical computation of p-values, eliminating the need for permutations.
• A modular algebraic operation framework is employed to capture diverse CCC patterns.
• The framework supports leveraging atlas-scale single-cell references to enhance CCC analysis on user-generated datasets.

Main Results:

• FastCCC demonstrates superior analytical capabilities compared to existing methods across multiple datasets.
• The framework reliably identifies biologically meaningful CCCs in complex tissue environments.
• Specific examples include differential interactions in COVID-19 severity, thymic T-cell development, and reference-based CCC analysis.

Conclusions:

• FastCCC provides a scalable, robust, and efficient solution for reference-based CCC analysis in single-cell transcriptomics.
• The framework facilitates the discovery of critical cell-cell interactions and enhances biological understanding.
• The development of the first human CCC reference panel supports routine and advanced CCC analyses.