BASILISC: Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Concordance

Single-cell assays are the established method to study the genome one cell at a time. Unimodal assays measure one molecular layer, and the newer multimodal assays measure several. Most data are single-modality, so integrating them relies on models trained on the multimodal assays, where the cell correspondence across modalities is known. These models return a matching, but rarely a confidence measure. We present BASILISC (Bayesian Alignment of Siamese-Integrated Latents for Inferring Single-cell Concordance), an uncertainty-aware siamese network for matching unimodal single-cell embeddings. It places the uncertainty on the correspondence itself: for a cross-modal pair of cells, it predicts a distribution over whether the two share a cell type, and at the cell level over which cell is the partner. To our knowledge, this is the first uncertainty score on a learned cross-modal correspondence evaluated against a known ground-truth pairing. BASILISC learns a biologically meaningful similarity across modalities that reflects cell lineage: an unseen cell type is matched to its lineage relative, matching still works on such held-out cell types (balanced accuracy 0.85 on unseen vs. 0.93 on seen types), and the predictive uncertainty rises on these out-of-distribution cells. The uncertainty score also selects high-quality matches for downstream multimodal analysis: discarding the least certain fifth of the pairs raises accuracy from 0.93 to 0.98. At the cell level, a deep ensemble of the architecture reaches 0.062 matching accuracy on the benchmark test set, above the competition winner, and the posterior read on that assignment flags the wrong matches that cross a cell-type boundary better than a deterministic read of the same scores (AUROC 0.68 against 0.64).