IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-44014-3.html
   My bibliography  Save this article

Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA

Author

Listed:
  • Mohammadamin Edrisi

    (Rice University)

  • Xiru Huang

    (Rice University)

  • Huw A. Ogilvie

    (Rice University)

  • Luay Nakhleh

    (Rice University)

Abstract

Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett’s esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.

Suggested Citation

  • Mohammadamin Edrisi & Xiru Huang & Huw A. Ogilvie & Luay Nakhleh, 2023. "Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44014-3
    DOI: 10.1038/s41467-023-44014-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44014-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-44014-3?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. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Isabella Zwiener & Barbara Frisch & Harald Binder, 2014. "Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-13, January.
    3. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    4. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    5. Christina Curtis & Sohrab P. Shah & Suet-Feung Chin & Gulisa Turashvili & Oscar M. Rueda & Mark J. Dunning & Doug Speed & Andy G. Lynch & Shamith Samarajiwa & Yinyin Yuan & Stefan Gräf & Gavin Ha & Gh, 2012. "The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups," Nature, Nature, vol. 486(7403), pages 346-352, June.
    6. Prabhakar Chalise & Brooke L Fridley, 2017. "Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
    7. Pierre Martinez & Margriet R. Timmer & Chiu T. Lau & Silvia Calpe & Maria del Carmen Sancho-Serra & Danielle Straub & Ann-Marie Baker & Sybren L. Meijer & Fiebo J. W. ten Kate & Rosalie C. Mallant-Hen, 2016. "Dynamic clonal equilibrium and predetermined cancer risk in Barrett’s oesophagus," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
    8. Brian D. Dynlacht, 1997. "Regulation of transcription by proteins that control the cell cycle," Nature, Nature, vol. 389(6647), pages 149-152, September.
    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. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
    2. Nicolas Jouvin & Pierre Latouche & Charles Bouveyron & Guillaume Bataillon & Alain Livartowski, 2021. "Greedy clustering of count data through a mixture of multinomial PCA," Computational Statistics, Springer, vol. 36(1), pages 1-33, March.
    3. Flavia Esposito, 2021. "A Review on Initialization Methods for Nonnegative Matrix Factorization: Towards Omics Data Experiments," Mathematics, MDPI, vol. 9(9), pages 1-17, April.
    4. Geert-Jan Huizing & Ina Maria Deutschmann & Gabriel Peyré & Laura Cantini, 2023. "Paired single-cell multi-omics data integration with Mowgli," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Morgan A. Schmitz & Matthieu Heitz & Nicolas Bonneel & Fred Ngolè & David Coeurjolly, 2017. "Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning," Working Papers 2017-84, Center for Research in Economics and Statistics.
    6. Hiroyasu Abe & Hiroshi Yadohisa, 2019. "Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 825-853, December.
    7. Md Tauhidul Islam & Lei Xing, 2023. "Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    8. You, Na & Dai, Hongsheng & Wang, Xueqin & Yu, Qingyun, 2024. "Sequential estimation for mixture of regression models for heterogeneous population," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    9. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
    10. Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    11. Miriam Aparicio, 2021. "Resiliency and Cooperation or Regarding Social and Collective Competencies for University Achievement. An Analysis from a Systemic Perspective," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 8, ejser_v8_.
    12. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    13. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    14. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    15. Aleix Prat & Fara Brasó-Maristany & Olga Martínez-Sáez & Esther Sanfeliu & Youli Xia & Meritxell Bellet & Patricia Galván & Débora Martínez & Tomás Pascual & Mercedes Marín-Aguilera & Anna Rodríguez &, 2023. "Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    16. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    17. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    18. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.
    19. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    20. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.

    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:14:y:2023:i:1:d:10.1038_s41467-023-44014-3. 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.