<ul data-eligibleForWebStory="false"><li>Advances in sequencing technologies have allowed exploration of cellular heterogeneity at single-cell resolution.</li><li>Interpretability has become important alongside the increase in complexity of deep learning models.</li><li>A new model called scE2TM combines topic modeling for interpretable single-cell embedding learning.</li><li>scE2TM provides high-quality cell embeddings, improves clustering performance, and offers strong interpretation with external biological knowledge.</li></ul>

scE$^2$TM: Toward Interpretable Single-Cell Embedding via Topic Modeling

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