PreDigs: A Database of Context-specific Cell Type Markers and Precise Cell Subtypes for Digestive Cell Annotation

Research on cell type markers helps investigators explore the diverse cellular composition of gastrointestinal tumors, thereby enhancing our understanding of tumor heterogeneity and its impact on disease progression and treatment response. However, the integration of large-scale datasets and the standardization of cell type identification remain challenging. Here, we developed PreDigs, a user-friendly database of predicted signatures for the digestive system, which offers 124 curated single-cell RNA sequencing datasets, covering over 3.4 million cells, all available for download. After unsupervised clustering, we unified the identification and nomenclature of cell subtype labels, constructing a cell ontology tree with 142 cell types across 8 hierarchical levels. Meanwhile, we calculated three different context-specific cell type markers, including "Cell Markers", "Subtype Markers", and "TPN Markers", based on various application requirements within or across tissues. Through the integrated analysis of PreDigs data, we identified distinct cell subpopulations exclusive to tumors, one of which corresponds to tumor-specific endothelial cells. Additionally, PreDigs offers online cell annotation tools, allowing users to classify single cells with greater flexibility. PreDigs is accessible at https://www.biosino.org/predigs/.

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Publication Details

Journal
Genomics Proteomics & Bioinformatics
Published
2025-08-01
DOI
https://doi.org/10.1093/gpbjnl/qzaf066
Citations
1
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
1.05

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article

PreDigs: A Database of Context-specific Cell Type Markers and Precise Cell Subtypes for Digestive Cell Annotation

Jiayue Meng, Liyun Yuan, X. T. Chen, Yuanhu Ju et al.
1 citations
Genomics Proteomics & Bioinformatics
Cancer Cells and Metastasis
1.05
article

PreDigs: A Database of Context-specific Cell Type Markers and Precise Cell Subtypes for Digestive Cell Annotation

Jiayue Meng, Liyun Yuan, X. T. Chen, Yuanhu Ju, Guoqing Zhang, Liang Li, Mengyao Han, Daqing Lv, Yuwei Huang
article en
1 citations

Abstract

Research on cell type markers helps investigators explore the diverse cellular composition of gastrointestinal tumors, thereby enhancing our understanding of tumor heterogeneity and its impact on disease progression and treatment response. However, the integration of large-scale datasets and the standardization of cell type identification remain challenging. Here, we developed PreDigs, a user-friendly database of predicted signatures for the digestive system, which offers 124 curated single-cell RNA sequencing datasets, covering over 3.4 million cells, all available for download. After unsupervised clustering, we unified the identification and nomenclature of cell subtype labels, constructing a cell ontology tree with 142 cell types across 8 hierarchical levels. Meanwhile, we calculated three different context-specific cell type markers, including "Cell Markers", "Subtype Markers", and "TPN Markers", based on various application requirements within or across tissues. Through the integrated analysis of PreDigs data, we identified distinct cell subpopulations exclusive to tumors, one of which corresponds to tumor-specific endothelial cells. Additionally, PreDigs offers online cell annotation tools, allowing users to classify single cells with greater flexibility. PreDigs is accessible at https://www.biosino.org/predigs/.

Genomics Proteomics & BioinformaticsVol. 23(4)
Ningbo University (CN), Tongji University (CN), Shanghai Genon Biological Products (China) (CN), Shanghai Institute of Nutrition and Health (CN), Central Hospital of Putuo District (CN), Shanghai Sixth People's Hospital (CN), Shanghai Industrial Technology Institute (CN), Hongzhiwei Technology (China) (CN), University of Chinese Academy of Sciences (CN), Ningbo Institute of Industrial Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences
Openalex Percentile: Top 19%
Cancer Cells and Metastasis
1.05
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