Predicting treatment of rheumatoid arthritis with LIVI

Abstract

Large datasets can today be created with single-cell RNA sequencing (scRNA-seq), allowing researchers to measure RNA expression per cell. In 2026, a new model, Latent Interaction Variational Inference (LIVI), was proposed to analyze these data. LIVI is novel in capturing both cell- and donor-specific variation in the latent space with a Variational Autoencoder (VAE). The research by Vagiaki et al. was primarily focused on discovering expression quantitative trait loci (eQTLs). It is interesting to see how well the cell and donor latent space captures other characteristics, such as treatment success/failure. This research investigates whether the latent spaces of the LIVI model capture major cell types, sub-cell types, and treatment response. A simple classifier (MLP/SVM/Random Forest) was added on top of the latent spaces to evaluate this. Major cell types are clearly distinguishable from the cell latent space C, sub-cell types with a relatively larger class size can be well distinguished in the cell latent space C, and treatment response is partially captured in the DxC space, but not fully separable by a simple classifier. SVM and MLP outperform Random Forest in classifying treatment response. These findings indicate that biologically and clinically relevant information is preserved within the LIVI latent representations.