Single-cell RNA-seq datasets for inter-sample consistency (ISC) and label-transfer benchmarking with scTypeEval

This Zenodo record contains the single-cell RNA-seq datasets used in the benchmarking analyses associated with the inter-sample consistency (ISC) framework. ISC is a reference-agnostic approach designed to evaluate whether annotated cell types form reproducible transcriptional entities across biological samples. The datasets are provided as R .rds objects compatible with the Seurat/SeuratObject framework and include standardized metadata fields required by the reproducibility workflow. These objects serve as the starting inputs for dataset preprocessing, perturbation tasks, and benchmarking procedures implemented in the companion repository ISC_benchmark_reproducibility. The datasets are used to evaluate ISC metrics and dissimilarity functions across multiple perturbation scenarios and to support downstream benchmarking of supervised label-transfer tasks. All files are formatted to integrate directly with the benchmarking utilities provided for the scTypeEval R package. Dataset sources: Sikkema_2023_37291214_b351804c-293e-4aeb-9c4c-043db67f4540.rds: Sikkema, L. et al. An integrated cell atlas of the lung in health and disease. Nature Medicine 2023 29:6 29, 1563–1577 (2023). Yerly_2022_35986012.rds: Yerly, L. et al. Wounding triggers invasive progression in human basal cell carcinoma. bioRxiv 2024.05.31.596823 (2024) doi:10.1101/2024.05.31.596823; Yerly, L. et al. Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma. Nat Commun 13, 4897 (2022). Ganier_2024_38165934.rds: Yerly, L. et al. Wounding triggers invasive progression in human basal cell carcinoma. bioRxiv 2024.05.31.596823 (2024) doi:10.1101/2024.05.31.596823; Ganier, C. et al. Multiscale spatial mapping of cell populations across anatomical sites in healthy human skin and basal cell carcinoma. Proceedings of the National Academy of Sciences of the United States of America 121, (2024). Joanito_2022_35773407.rds: Joanito, I. et al. Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer. Nature genetics 54, 963–975 (2022). Gondal_2025_36095221_8d918bdd-ab11-4c83-9de0-93640aeb8e20.rds: Gondal, M. N., Cieslik, M. & Chinnaiyan, A. M. Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients. Scientific data 12, (2025). Stephenson_2021_33879890.rds: Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nature medicine 27, 904–916 (2021).