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

Area of Science:

• Computational Biology
• Single-cell Transcriptomics
• Systems Biology

Background:

• Understanding cell-cell communications (CCCs) is crucial for deciphering multicellular organism function.
• Existing methods for CCC detection in single-cell transcriptomics can be computationally intensive and lack scalability.

Purpose of the Study:

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

Main Methods:

• FastCCC utilizes fast Fourier transformation-based convolution for analytical p-value computation, eliminating the need for permutations.
• Employs a modular algebraic operation framework to capture diverse CCC patterns.
• Integrates atlas-scale single-cell references for enhanced analysis of user-collected datasets.

Main Results:

• FastCCC demonstrates superior analytical capabilities compared to existing methods across multiple datasets.
• Successfully identifies biologically meaningful CCCs in complex tissue environments, including COVID-19 severity-linked interactions.
• Reveals dynamic communications during T-cell development and distinct interactions in reference-based analyses.

Conclusions:

• FastCCC provides a scalable, robust, and efficient solution for reference-based CCC analysis in single-cell transcriptomics.
• The framework and the human CCC reference panel facilitate deeper biological insights into complex cellular interactions.
• Enables reliable detection of critical cell-cell communications in diverse biological contexts.