scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder
This work addresses batch effect issues in single-cell transcriptomics for researchers, offering an interpretable tool that complements existing methods, though it is incremental in its approach.
The paper tackles the challenge of disentangling biological signal from batch effects in single-cell RNA sequencing data by proposing scMEDAL, a framework that separately models batch-invariant and batch-specific effects using a deep mixed effects autoencoder. It shows that scMEDAL-RE produces interpretable embeddings that yield more accurate predictions of disease status, donor group, and tissue across diverse conditions.
Single-cell RNA sequencing enables high-resolution analysis of cellular heterogeneity, yet disentangling biological signal from batch effects remains a major challenge. Existing batch-correction algorithms suppress or discard batch-related variation rather than modeling it. We propose scMEDAL, single-cell Mixed Effects Deep Autoencoder Learning, a framework that separately models batch-invariant and batch-specific effects using two complementary subnetworks. The principal innovation, scMEDAL-RE, is a random-effects Bayesian autoencoder that learns batch-specific representations while preserving biologically meaningful information confounded with batch effects signal often lost under standard correction. Complementing it, the fixed-effects subnetwork, scMEDAL-FE, trained via adversarial learning provides a default batch-correction component. Evaluations across diverse conditions (autism, leukemia, cardiovascular), cell types, and technical and biological effects show that scMEDAL-RE produces interpretable, batch-specific embeddings that complement both scMEDAL-FE and established correction methods (scVI, Scanorama, Harmony, SAUCIE), yielding more accurate prediction of disease status, donor group, and tissue. scMEDAL also provides generative visualizations, including counterfactual reconstructions of a cell's expression as if acquired in another batch. The framework allows substitution of the fixed-effects component with other correction methods, while retaining scMEDAL-RE's enhanced predictive power and visualization. Overall, scMEDAL is a versatile, interpretable framework that complements existing correction, providing enhanced insight into cellular heterogeneity and data acquisition.