PreDigs: a Database of Context-specific Cell-type Markers and Precise cell subtypes for Digestive Cell Annotation
Meng, J.; Han, M.; Huang, Y.; Li, L.; Ju, Y.; Lv, D.; Chen, X.; Yuan, L.; Zhang, G.
Show abstract
Research on cell type markers aids investigators in exploring the diverse cellular compositions within gastrointestinal tumors, enhancing our understanding of tumor heterogeneity and its implications for disease progression and treatment response. However, issues such as the integration of large-scale datasets and the lack of standardized cell type identification hinder comprehensive characterization. Here, we developed a user-friendly web interface called PreDigs (Predicted Signatures in Digestive System), which offers 124 tailored scRNA-seq datasets available for download, encompassing over 3.4 million cells. After unsupervised clustering, we unified the identification and naming of subtype labels, ultimately constructing a cell ontology tree that includes 142 cell types, with up to eight hierarchical levels. Meanwhile, we calculated three different context-specific cell-type markers--Cell Markers, Subtype Markers, and TPN Markers--based on various application requirements within or across tissues. Through the integrated analysis of PreDigs gastrointestinal data, we identified distinct cell subpopulations exclusive to tumors, one of which corresponds to tumor-specific endothelial cells (TEC). Furthermore, PreDigs offers online cell annotation tools that empower users to perform single-cell classification with greater flexibility, accessible at https://www.biosino.org/predigs/.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment 96%
- scCancerExplorer: a comprehensive database for interactively exploring single-cell multi-omics data of human pan-cancer 96%
- MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets 94%
Similar papers in this journal
- Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing 94%
- SSMD: A semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data 94%
- Enhancing single-cell cellular state inference by incorporating molecular network features 94%