COEM: Cross-Modal Embedding for MetaCell Identification
This addresses the challenge of integrating multiple modalities for more precise cell state identification in single-cell genomics, representing an incremental improvement over existing methods.
The paper tackles the problem of identifying metacells from single-cell multi-omics data by developing COEM, a method that uses cross-modal embedding of scATAC-seq and scRNA-seq to outperform SEACells in accuracy and separation across datasets.
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.