  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.
