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bioRxiv · 10.1101/2024.03.19.585758

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

Lodi, M.·Osei, K.·Ranganathan, V.·Hwang, P.·Ghosh, P.
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Abstract

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state of the art clustering methods: CHAI-AvgSim and CHAI-SNF. Both methods demonstrate improved performance on a diverse selection of benchmarking datasets, besides also outperforming a previous consensus clustering method. We demonstrate CHAIs practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI is intuitive and easily customizable; it provides a way for users to add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai

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Lodi, M., Osei, K., Ranganathan, V., Hwang, P., Ghosh, P.. 2024-03-22. CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification. https://doi.org/10.1101/2024.03.19.585758

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