Data Sheet 1_singIST: an R/bioconductor library and quarto dashboard for automated single-cell comparative transcriptomics analysis of disease models and humans.pdf

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.</p>Results<p>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.</p>Conclusion<p>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.