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CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.

Musaddiq K Lodi¹, Muzammil Lodi², Kezie Osei³

¹Integrative Life Sciences, Virginia Commonwealth University, Richmond, VA 23284.

Biorxiv : the Preprint Server for Biology

|April 2, 2024

Summary

CHAI (consensus Clustering tHrough similArIty matrix integratIon) is a new tool that combines multiple clustering methods to improve cell type identification in single-cell RNA sequencing (scRNAseq) data.

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Accurate cell type identification is crucial for single-cell RNA sequencing (scRNAseq) data analysis.
Selecting the optimal computational method for scRNAseq clustering remains a challenge for researchers.

Approach:

CHAI (consensus Clustering tHrough similArIty matrix integratIon) employs a wisdom of crowds approach, aggregating results from seven state-of-the-art clustering algorithms.
Two distinct aggregation methods, CHAI-AvgSim and CHAI-SNF, are presented.
The open-source R package allows for user customization, including the addition or selection of specific clustering methods.

Key Points:

CHAI demonstrates superior performance on diverse benchmarking datasets compared to existing methods.
The tool successfully identified a CDH3-enriched tumor cell cluster in a practical application.
CHAI-SNF shows enhanced performance when integrating spatial transcriptomics data, facilitating multiomic analysis.

Conclusions:

CHAI offers a robust and flexible platform for improved scRNAseq data clustering and cell type identification.
The consensus clustering approach enhances analytical accuracy and facilitates multiomic integration.
CHAI provides a valuable, customizable, and open-source solution for the research community.