Abstract
Clustering is widely used to identify cell types in cellular-resolution transcriptomic data, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST). Mixed-membership clustering assigns fractional memberships across clusters and captures continuous variation beyond hard clustering, but integrating and interpreting results from either approach is complicated by the "clustering alignment problem," which arises from label switching, multi-modality, and differences in model settings (including differing numbers of clusters). We introduce ACE-OF-Clust, enabling a four-step workflow for single-cell clustering: multiple clustering, clustering alignment, model comparison, and identification of informative features. ACE-OF-Clust introduces direct comparison of clustering solutions, assesses consistency against annotations, and leverages feature-level clustering profiles to prioritize genes discriminating among cell types. We demonstrate its utility on PBMC scRNA-seq and breast cancer ST data, and on multi-omic single-cell data. ACE-OF-Clust quantifies cross-omic clustering variability and suggests putative cross-omic regulatory links. Overall, ACE-OF-Clust increases the interpretability, flexibility, and robustness of single-cell clustering, providing a scalable tool for studying cellular heterogeneity and gene expression dynamics.