The graph representation of the Crosswalk of CellTypist annotated cells to Cell Ontology dataset.

This crosswalk maps the cell type annotations from CellTypist (Xu et al. 2023; Domínguez Conde et al. 2022) to the Cell Ontology Version IRI: http://purl.obolibrary.org/obo/cl/releases/2025-04-10/cl.owl. More information on CellTypist is available at https://www.celltypist.org/.

The Human Reference Atlas regularly adds new cell types to Cell Ontology; an overview and current numbers are available under "Ontologies Extended" at https://apps.humanatlas.io/dashboard/data.

Bibliography:

• Domínguez Conde, C., C. Xu, L. B. Jarvis, D. B. Rainbow, S. B. Wells, T. Gomes, S. K. Howlett, et al. 2022. “Cross-Tissue Immune Cell Analysis Reveals Tissue-Specific Features in Humans.” Science 376 (6594): eabl5197. https://doi.org/10.1126/science.abl5197.
• Xu, Chuan, Martin Prete, Simone Webb, Laura Jardine, Benjamin J. Stewart, Regina Hoo, Peng He, Kerstin B. Meyer, and Sarah A. Teichmann. 2023. “Automatic Cell-Type Harmonization and Integration across Human Cell Atlas Datasets.” Cell 186 (26): 5876-5891.e20. https://doi.org/10.1016/j.cell.2023.11.026.

The graph representation of the Ccell type annotations for CellTypist dataset.

This crosswalk maps the cell type annotations from CellTypist https://doi.org/10.1126/science.abl5197 to the latest revision of Cell Ontology Version IRI: http://purl.obolibrary.org/obo/cl/releases/2024-05-15/cl.owl in which many new cell types have been added since the original cell type annotations were mapped.

We provide a .Rds file of an annotated Seurat Object of scRNA-seq data of two bronchioid models derived from distinct donors after 21days of culture using 10x genomics 3' v3 chemistry. Raw data was processed using CellRanger v7.1.0. Cells were filtered based on detected UMIs (>2000) and fraction of mitochondrial counts ( patient information for every cell (patient1 or patient2)- nCount_RNA -> UMI counts per cell- nFeature_RNA -> genes detected per cell- percent.mt -> mitochondrial count fraction per cell- seurat_clusters -> unsupervised clustering results using Louvain algorithm with resolution = 0.5- Manual.Annotation -> Cell types annotated based on marker gene expression- Celltypist.prediction -> Cell types predicted with CellTypist Python package- Celltypist.prediction.ari -> Cell types predicted with CellTypist Python package, with harmonized names for comparison with manual annotation

This project contains the datasets used to evaluate and reproduce the results of ENACT (End-to-End Analysis and Cell Type
Annotation for Visium HD Slides). The dataset consists of:

• a sample Visium HD sample of human colorectal cancer, courtesy of 10X Genomics (Visium HD Spatial Gene Expression Library, Human Colorectal Cancer (FFPE) - 10x Genomics). All credit goes to 10X Genomics.
• configuration files to be used to reproduce the results provided in the ENACT publication,
• evaluation plots used in the ENACT publication, and
• results obtained after running ENACT on the human colorectal cancer sample using the four bin-to-cell assignment methods (naive, weighted_by_area, weighted_by_transcript, and weighted_by_cluster) and the three cell annotation methods (Sargent, CellAssign, CellTypist)

Additionally, results from running ENACT on the following three public VisiumHD samples are provided to showcase ENACT’s tissue-agnostic nature:

MHC class 2 of AT2 is crucial for alveoli regeneration We will update this repository according to the results of the paper submission rds files and celltypist model (pkl) related to our project Mouse alveolar epithelium reference is built by merging GSE145031, GSE204787, GSE261793 and GSE262927 data