scConcept enables concept-level exploration of single-cell transcriptomic data (opens in new tab)

Interpreting high-dimensional single-cell transcriptomic data remains challenging, as existing methods rely on latent representations or prior knowledge that require extensive post hoc analysis to derive biologically meaningful insights. Topic models provide interpretable gene-level signals but often produce redundant and coarse-grained programs that are difficult to translate into coherent biological concepts. While recent foundation models and large language models (LLMs) show promise, they...