CoState: Interpretable Donor Representation Learning from Single-Cell Transcriptomes

Single-cell RNA sequencing offers new opportunities for individual-level disease classification. Disease-associated differences between donors can reflect changes in cell-type composition, within-type expression states, or both. However, existing approaches that average cell features to represent donors do not distinguish between these two sources of variation. We introduce CoState (Composition and State), which jointly learns cell and donor representations by combining observed cell-type proportions with learned within-type state distributions into a joint probability distribution. Using Hellinger geometry, we map this distribution to a donor vector, enabling interpretable comparisons that quantify each cell type's contribution to differences between donors. On a benchmark comprising 1,007 donors from 23 Inflammation Atlas studies spanning 19 diseases and healthy controls, CoState outperforms the evaluated baselines in cross-study disease retrieval, achieving an approximately 16% relative improvement in retrieval macro-F1 over the strongest baseline in independent-study evaluation. Compared with baseline methods in the training-cell scaling experiment, CoState benefits more from increasing the number of training cells, achieving greater performance gains under a fixed training schedule. Code is available at https://anonymous.4open.science/r/costate-0D40.