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Latent variable models are powerful statistical tools that can uncoverrelevant variation between patients or cells, by inferring unobserved hiddenstates from observable high-dimensional data. A major shortcoming of currentmethods, however, is their inability to learn sparse and interpretable hiddenstates. Additionally, in settings where partial knowledge on the latentstructure of the data is readily available, a statistically sound integrationof prior information into current methods is challenging. To address theseissues, we propose spex-LVM, a factorial latent variable model with sparsepriors to encourage the inference of explainable factors driven bydomain-relevant information. spex-LVM utilizes existing knowledge of curatedbiomedical pathways to automatically assign annotated attributes to latentfactors, yielding interpretable results tailored to the corresponding domain ofinterest. Evaluations on simulated and real single-cell RNA-seq datasetsdemonstrate that our model robustly identifies relevant structure in aninherently explainable manner, distinguishes technical noise from sources ofbiomedical variation, and provides dataset-specific adaptations of existingpathway annotations. Implementation is available athttps://github.com/MLO-lab/spexlvm.

Encoding Domain Information with Sparse Priors for Inferring Explainable Latent Variables