We developed mixture model inference with discrete-coupled autoencoders (MMIDAS), an unsupervised variational framework that jointly learns discrete clusters and continuous cluster-specific variability. When applied to unimodal or multimodal single-cell omic data, MMIDAS learned single-cell representations with robust cell type definitions and interpretable, continuous within-cell type variability.
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This is a summary of: Marghi, Y. et al. Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00683-8 (2024).
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Joint inference of discrete and continuous factors captures variability across and within cell types. Nat Comput Sci 4, 733–734 (2024). https://doi.org/10.1038/s43588-024-00696-3
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DOI: https://doi.org/10.1038/s43588-024-00696-3
