IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28845-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28845-0
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-28845-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    2. Guillermo Serrano & Nerea Berastegui & Aintzane Díaz-Mazkiaran & Paula García-Olloqui & Carmen Rodriguez-Res & Sofia Huerga-Dominguez & Marina Ainciburu & Amaia Vilas-Zornoza & Patxi San Martin-Uriz &, 2024. "Single-cell transcriptional profile of CD34+ hematopoietic progenitor cells from del(5q) myelodysplastic syndromes and impact of lenalidomide," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    4. Xiaohong Li & Guy N Brock & Eric C Rouchka & Nigel G F Cooper & Dongfeng Wu & Timothy E O’Toole & Ryan S Gill & Abdallah M Eteleeb & Liz O’Brien & Shesh N Rai, 2017. "A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    5. Kerr Kathleen F., 2012. "Optimality Criteria for the Design of 2-Color Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-9, January.
    6. Ambroise Jérôme & Bearzatto Bertrand & Robert Annie & Macq Benoit & Gala Jean-Luc, 2012. "Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-20, February.
    7. J. McClatchy & R. Strogantsev & E. Wolfe & H. Y. Lin & M. Mohammadhosseini & B. A. Davis & C. Eden & D. Goldman & W. H. Fleming & P. Conley & G. Wu & L. Cimmino & H. Mohammed & A. Agarwal, 2023. "Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    8. Alexandra Gyurdieva & Stefan Zajic & Ya-Fang Chang & E. Andres Houseman & Shan Zhong & Jaegil Kim & Michael Nathenson & Thomas Faitg & Mary Woessner & David C. Turner & Aisha N. Hasan & John Glod & Ro, 2022. "Biomarker correlates with response to NY-ESO-1 TCR T cells in patients with synovial sarcoma," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    9. Sora Yoon & Seon-Young Kim & Dougu Nam, 2016. "Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    10. Yu Lianbo & Gulati Parul & Fernandez Soledad & Pennell Michael & Kirschner Lawrence & Jarjoura David, 2011. "Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, September.
    11. Sinan Xiong & Jianbiao Zhou & Tze King Tan & Tae-Hoon Chung & Tuan Zea Tan & Sabrina Hui-Min Toh & Nicole Xin Ning Tang & Yunlu Jia & Yi Xiang See & Melissa Jane Fullwood & Takaomi Sanda & Wee-Joo Chn, 2024. "Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    12. Jonathan C. M. Wan & Dennis Stephens & Lingqi Luo & James R. White & Caitlin M. Stewart & Benoît Rousseau & Dana W. Y. Tsui & Luis A. Diaz, 2022. "Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    13. Chaofeng Yuan & Wensheng Zhu & Xuming He & Jianhua Guo, 2019. "A mixture factor model with applications to microarray data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 60-76, March.
    14. Nan Li & Matthew N. McCall & Zhijin Wu, 2017. "Establishing Informative Prior for Gene Expression Variance from Public Databases," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 160-177, June.
    15. Brian Caffo & Liu Dongmei & Giovanni Parmigiani, 2004. "Power Conjugate Multilevel Models with Applications to Genomics," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1062, Berkeley Electronic Press.
    16. Nott, David J. & Yu, Zeming & Chan, Eva & Cotsapas, Chris & Cowley, Mark J. & Pulvers, Jeremy & Williams, Rohan & Little, Peter, 2007. "Hierarchical Bayes variable selection and microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 852-872, April.
    17. Santu Ghosh & Alan M. Polansky, 2022. "Large-Scale Simultaneous Testing Using Kernel Density Estimation," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 808-843, August.
    18. Qianxing Mo & Faming Liang, 2010. "Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model," Biometrics, The International Biometric Society, vol. 66(4), pages 1284-1294, December.
    19. Ahmed Hossain & Hafiz T.A. Khan, 2016. "Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2538-2549, October.
    20. Alexander Kaever & Manuel Landesfeind & Kirstin Feussner & Burkhard Morgenstern & Ivo Feussner & Peter Meinicke, 2014. "Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28845-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.