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arXiv · 2207.07734

COEM: Cross-Modal Embedding for MetaCell Identification

Haiyi Mao·Minxue Jia·Jason Xiaotian Dou·Haotian Zhang·Panayiotis V. Benos
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Abstract

Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks.

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Haiyi Mao, Minxue Jia, Jason Xiaotian Dou, Haotian Zhang, Panayiotis V. Benos. 2022-07-25. COEM: Cross-Modal Embedding for MetaCell Identification. https://arxiv.org/abs/2207.07734

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