Abstract 1063: CellMap: a comprehensive human single cell gene expression reference for automated cell annotation and cancer cell-of-origin analysis
Authors: Yuping Zhang, Gabriel Cruz, Hanbyul Cho, Chandan Kumar-Sinha, Rahul Mannan, Jin Chen, Xuhong Cao, Saravana M. Dhanasekaran, Arul M. Chinnaiyan
Journal: Cancer Research (2025)

Abstract In single-cell transcriptomic analysis, accurate cell type annotation forms the essential basis for all downstream analysis and data interpretation. While time consuming manual annotation requires extensive prior knowledge, available automated cell annotation tools lack a unified, curated human cell reference to ensure successful identification of all cell types present in any given dataset. To fill in the gap we create “CellMap” a comprehensive single cell gene expression reference database of known cell types across most human tissues by compiling, curating and integrating single cell datasets from multiple sources including Human Protein Atlas, Tabula Sapiens, ArrayExpress and GEO. CellMap contains 151 different celltypes/states including 94 epithelial, 31 brain, 4 from soft tissues, 8 stromal and 15 major immune celltypes from 42 human tissues. Projecting pseudo-bulk profiles of the 151 celltypes into 2-dimensional UMAP revealed major clusters of cells from the nervous system, respiratory system, gastrointestinal tract, proximal digestive system, female tissues and endocrine tissues. Immune cells and stromal cells are generally clustered together regardless of their tissue origin. We also curated about 200 immune cell phenotypes from the ImmGen database for fine-tuning annotation of immune cell subpopulations. Importantly by combining CellMap and the SingleR algorithm, we successfully identified the cell lineage of the tumor and minor cell types originated from the biopsy site of a breast cancer metastatic sample; the latter were left unannotated with reference derived from breast tissue or misclassified using Human Primary Cell Atlas. In addition, CellMap allowed us to identify highly specific tissue markers and cell type markers. Based on 1241 identified marker genes, we trained a random forest model to predict cancer types using 80% of the TCGA tumor samples of each cancer type (n = 7160) and tested on a 20% hold-out testing set (n = 1774), achieving a median of 95.4% accuracy across cancer types. Cancer types with low accuracy include esophageal carcinoma (mostly mis-classified as head and neck squamous cell carcinoma) and rectum adenocarcinoma (mostly mis-classified as colon adenocarcinoma). The misclassification could be explained by the high similarity between these cancer types in terms of cell-of-origin. Finally a webportal for exploring celltypes in CellMap and an open-source R-package for automated cell annotation of single cell data and cell lineage prediction for bulk RNA-seq data were developed. This work provides valuable resources for conducting single-cell analysis and cancer cell-of-origin analysis. Additionally, it helps identify closely related cell types across tissue types, which is particularly useful since diseases originating from related cell types may share similar therapeutic targets. Citation Format: Yuping Zhang, Gabriel Cruz, Hanbyul Cho, Chandan Kumar-Sinha, Rahul Mannan, Jin Chen, Xuhong Cao, Saravana M. Dhanasekaran, Arul M. Chinnaiyan. CellMap: a comprehensive human single cell gene expression reference for automated cell annotation and cancer cell-of-origin analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1063.