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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, United States.

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|August 29, 2024
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Summary

CHAI, a novel consensus clustering method, aggregates results from multiple single-cell RNA sequencing clustering algorithms. This wisdom of crowds approach improves cell-type identification accuracy and offers a flexible R package for researchers.

Keywords:
cell-type identificationclusteringsingle-cell biologywisdom-of-crowds

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNAseq) analysis requires robust cell-type identification.
  • Selecting optimal computational methods for scRNAseq clustering remains a challenge for researchers.

Purpose of the Study:

  • To develop a novel consensus clustering framework, CHAI (consensus Clustering tHrough similArIty matrix integratIon), for improved scRNAseq cell-type identification.
  • To provide a flexible and extensible R package for scRNAseq data analysis.

Main Methods:

  • CHAI aggregates clustering results from seven state-of-the-art methods using two approaches: CHAI-AvgSim and CHAI-SNF.
  • Performance was evaluated on multiple benchmarking datasets and compared against existing consensus clustering methods.
  • The utility of CHAI was demonstrated through identifying a tumor cell cluster enriched with CDH3 and integrating spatial transcriptomics data.

Main Results:

  • CHAI-AvgSim and CHAI-SNF demonstrated superior performance across benchmarking datasets.
  • Both CHAI methods outperformed the SAME-clustering consensus method.
  • CHAI-SNF showed improved performance when incorporating spatial transcriptomics data, highlighting its multiomic integration capabilities.

Conclusions:

  • CHAI offers a robust and accurate solution for scRNAseq cell-type identification by leveraging a wisdom of crowds approach.
  • The CHAI R package is a customizable and extensible platform, ensuring its continued utility as new clustering algorithms emerge.
  • CHAI facilitates advanced analyses, including multiomic integration and identification of specific cell populations like CDH3-enriched tumor cells.