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Updated: May 12, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

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Published on: January 10, 2019

scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas.

Chang-Xiao Li1, Can Huang1, Dong-Sheng Chen2

  • 1State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu Province, China.

Chinese Medical Sciences Journal = Chung-Kuo I Hsueh K'O Hsueh Tsa Chih
|March 31, 2025
PubMed
Summary

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We developed scPANDA, a tool using a 10-million-cell atlas for accurate blood cell type identification from single-cell RNA sequencing data. This enhances the analysis of cellular heterogeneity in complex immune systems.

Area of Science:

  • Hematology
  • Immunology
  • Bioinformatics
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) advances cellular heterogeneity studies, especially in the hematological system.
  • Accurate immune cell type annotation is crucial but challenging due to cell complexity.
  • Existing methods require improvement for precise blood cell identification.

Purpose of the Study:

  • To develop the PAN-blood single-cell Data Annotator (scPANDA) for precise blood cell type annotation.
  • To leverage a comprehensive 10-million-cell atlas as a reference for scRNA-seq data.
  • To overcome challenges in annotating complex immune cell populations.

Main Methods:

  • Constructed a 10-million-cell atlas from 16 studies with rigorous quality control and integration.
Keywords:
blood cellscell type annotationimmunologysingle-cell RNA sequencingsingle-cell atlas

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  • Employed scPANDA with a three-layer inference approach for progressive cell type refinement.
  • Utilized iterative clustering and harmonization for cell type purity and evaluated performance on external datasets.

    • Main Results:

    • The atlas features a hierarchical structure: 16 compartments, 54 classes, 4,460 low-level clusters, and 611 high-level clusters.
    • scPANDA demonstrated robust performance in annotating diverse immune scRNA-seq datasets.
    • The tool successfully analyzed immune-tumor coexisting clusters and identified conserved cell clusters across species.

    Conclusions:

    • scPANDA represents an effective reference mapping strategy using a large-scale atlas.
    • The tool significantly enhances the accuracy and reliability of blood cell type identification.
    • This approach advances the study of cellular heterogeneity in hematological and immunological research.