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MQuad enables clonal substructure discovery using single cell mitochondrial variants

Author

Listed:
  • Aaron Wing Cheung Kwok

    (The University of Hong Kong)

  • Chen Qiao

    (The University of Hong Kong)

  • Rongting Huang

    (The University of Hong Kong)

  • Mai-Har Sham

    (The Chinese University of Hong Kong)

  • Joshua W. K. Ho

    (The University of Hong Kong
    Hong Kong Science Park)

  • Yuanhua Huang

    (The University of Hong Kong
    The University of Hong Kong)

Abstract

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, identifying informative mtDNA variants in noisy and sparse single-cell sequencing data is still challenging with few computation methods available. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA, DNA or ATAC sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrate its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution.

Suggested Citation

  • Aaron Wing Cheung Kwok & Chen Qiao & Rongting Huang & Mai-Har Sham & Joshua W. K. Ho & Yuanhua Huang, 2022. "MQuad enables clonal substructure discovery using single cell mitochondrial variants," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28845-0
    DOI: 10.1038/s41467-022-28845-0
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    References listed on IDEAS

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    1. Olivier Poirion & Xun Zhu & Travers Ching & Lana X. Garmire, 2018. "Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    2. Seongyeol Park & Nanda Maya Mali & Ryul Kim & Jeong-Woo Choi & Junehawk Lee & Joonoh Lim & Jung Min Park & Jung Woo Park & Donghyun Kim & Taewoo Kim & Kijong Yi & June Hyug Choi & Seong Gyu Kwon & Joo, 2021. "Clonal dynamics in early human embryogenesis inferred from somatic mutation," Nature, Nature, vol. 597(7876), pages 393-397, September.
    3. Akdes Serin Harmanci & Arif O. Harmanci & Xiaobo Zhou, 2020. "CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    4. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    5. Luiza Moore & Alex Cagan & Tim H. H. Coorens & Matthew D. C. Neville & Rashesh Sanghvi & Mathijs A. Sanders & Thomas R. W. Oliver & Daniel Leongamornlert & Peter Ellis & Ayesha Noorani & Thomas J. Mit, 2021. "The mutational landscape of human somatic and germline cells," Nature, Nature, vol. 597(7876), pages 381-386, September.
    6. Wei Wei & Daniel J. Gaffney & Patrick F. Chinnery, 2021. "Cell reprogramming shapes the mitochondrial DNA landscape," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
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