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An entropy-based metric for assessing the purity of single cell populations

Author

Listed:
  • Baolin Liu

    (Peking University)

  • Chenwei Li

    (Peking University
    Analytical Biosciences Limited)

  • Ziyi Li

    (Peking University)

  • Dongfang Wang

    (Peking University)

  • Xianwen Ren

    (Peking University)

  • Zemin Zhang

    (Peking University
    Peking University
    Analytical Biosciences Limited)

Abstract

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.

Suggested Citation

  • Baolin Liu & Chenwei Li & Ziyi Li & Dongfang Wang & Xianwen Ren & Zemin Zhang, 2020. "An entropy-based metric for assessing the purity of single cell populations," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16904-3
    DOI: 10.1038/s41467-020-16904-3
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    Cited by:

    1. Yanchuan Li & Huamei Li & Cheng Peng & Ge Meng & Yijun Lu & Honglin Liu & Li Cui & Huan Zhou & Zhu Xu & Lingyun Sun & Lihong Liu & Qing Xiong & Beicheng Sun & Shiping Jiao, 2024. "Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling," Nature Communications, Nature, vol. 15(1), pages 1-25, December.
    2. Clara Alsinet & Maria Nascimento Primo & Valentina Lorenzi & Erica Bello & Iva Kelava & Carla P. Jones & Roser Vilarrasa-Blasi & Carmen Sancho-Serra & Andrew J. Knights & Jong-Eun Park & Beata S. Wysp, 2022. "Robust temporal map of human in vitro myelopoiesis using single-cell genomics," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. F. Nadalin & M. J. Marzi & M. Pirra Piscazzi & P. Fuentes-Bravo & S. Procaccia & M. Climent & P. Bonetti & C. Rubolino & B. Giuliani & I. Papatheodorou & J. C. Marioni & F. Nicassio, 2024. "Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    4. Lucy Xia & Christy Lee & Jingyi Jessica Li, 2024. "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    5. Jiexiang Zhao & Ping Lu & Cong Wan & Yaping Huang & Manman Cui & Xinyan Yang & Yuqiong Hu & Yi Zheng & Ji Dong & Mei Wang & Shu Zhang & Zhaoting Liu & Shuhui Bian & Xiaoman Wang & Rui Wang & Shaofang , 2021. "Cell-fate transition and determination analysis of mouse male germ cells throughout development," Nature Communications, Nature, vol. 12(1), pages 1-20, December.
    6. David R. Ghasemi & Konstantin Okonechnikov & Anne Rademacher & Stephan Tirier & Kendra K. Maass & Hanna Schumacher & Piyush Joshi & Maxwell P. Gold & Julia Sundheimer & Britta Statz & Ahmet S. Rifaiog, 2024. "Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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