singIST: an R/bioconductor library and quarto dashboard for automated single-cell comparative transcriptomics...

Preclinical disease models frequently fail to recapitulate human pathophysiology at single-cell resolution, complicating model selection and limiting translational relevance. While single-cell RNA sequencing enables detailed characterization of disease mechanisms, systematic and interpretable frameworks to compare disease models against human references across cell types and pathways remain limited. There is a need for standardized, reproducible tools that support quantitative cross-species comparison while remaining accessible to applied researchers.

We present singIST, an R/Bioconductor package for quantitative and interpretable comparison of disease model single-cell transcriptomic data against human reference datasets. singIST integrates pathway-level modeling with cell-type resolution and one-to-one orthology mapping to quantify the direction and magnitude of recapitulation across pathways, cell types, and genes. To facilitate interpretation and reporting, we additionally provide singIST Visualizer, a companion Quarto-based Shiny application enabling interactive exploration of results and automated generation of publication-ready figures and tables without custom code. We demonstrate the workflow using a mouse oxazolone model compared against a human atopic dermatitis reference, assessing biological alignment at multiple levels of resolution.

singIST provides a standardized and reproducible framework for comparative single-cell transcriptomic analysis between disease models and humans, with an emphasis on interpretability and practical usability. By combining quantitative recapitulation metrics with an interactive visualization interface, singIST supports informed model evaluation, hypothesis generation, and translational decision-making in preclinical research. The software is freely available via Bioconductor under an open-source license. The singIST visualizer is freely available in Shiny with 8 GB memory https://amoruno.shinyapps.io/singIST_visualizer/.