IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data (opens in new tab)

Extracting biologically interpretable insights from single-cell RNA sequencing (scRNA-seq) data remains a major challenge. While deep learning approaches have demonstrated strong predictive performance for dimensionality reduction and representation learning, their latent representations often lack clear biological meaning, and prior biological knowledge is frequently incorporated only weakly or inconsistently. As a result, learned features may not faithfully reflect known gene-function relat...