IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1703.00703.html
   My bibliography  Save this paper

*K-means and Cluster Models for Cancer Signatures

Author

Listed:
  • Zura Kakushadze
  • Willie Yu

Abstract

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1,389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.

Suggested Citation

  • Zura Kakushadze & Willie Yu, 2017. "*K-means and Cluster Models for Cancer Signatures," Papers 1703.00703, arXiv.org, revised Jul 2017.
    • Handle: RePEc:arx:papers:1703.00703
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1703.00703
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zura Kakushadze & Willie Yu, 2016. "Statistical Industry Classification," Journal of Risk & Control, Risk Market Journals, vol. 3(1), pages 17-65.
    2. Vincent Caval & Rodolphe Suspène & Milana Shapira & Jean-Pierre Vartanian & Simon Wain-Hobson, 2014. "A prevalent cancer susceptibility APOBEC3A hybrid allele bearing APOBEC3B 3′UTR enhances chromosomal DNA damage," Nature Communications, Nature, vol. 5(1), pages 1-7, December.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    5. Connor, Gregory & Korajczyk, Robert A, 1993. "A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    6. 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.
    7. Gunes Gundem & Peter Van Loo & Barbara Kremeyer & Ludmil B. Alexandrov & Jose M. C. Tubio & Elli Papaemmanuil & Daniel S. Brewer & Heini M. L. Kallio & Gunilla Högnäs & Matti Annala & Kati Kivinummi &, 2015. "The evolutionary history of lethal metastatic prostate cancer," Nature, Nature, vol. 520(7547), pages 353-357, April.
    8. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    9. Kakushadze, Zura & Yu, Willie, 2016. "Factor models for cancer signatures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 527-559.
    10. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 1-65.
    2. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Papers 2003.05095, arXiv.org.
    3. Mantas Lukauskas & Tomas Ruzgas, 2023. "Reduced Clustering Method Based on the Inversion Formula Density Estimation," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

    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. Zura Kakushadze & Willie Yu, 2016. "Statistical Industry Classification," Papers 1607.04883, arXiv.org, revised Dec 2018.
    2. Zura Kakushadze & Willie Yu, 2017. "Mutation Clusters from Cancer Exome," Papers 1707.08504, arXiv.org.
    3. Zura Kakushadze & Willie Yu, 2016. "Factor Models for Cancer Signatures," Papers 1604.08743, arXiv.org, revised Jan 2017.
    4. Zura Kakushadze & Willie Yu, 2018. "Betas, Benchmarks and Beating the Market," Papers 1807.09919, arXiv.org.
    5. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 1-65.
    6. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.
    7. Kakushadze, Zura & Yu, Willie, 2016. "Factor models for cancer signatures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 527-559.
    8. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Papers 2003.05095, arXiv.org.
    9. José M. Maisog & Andrew T. DeMarco & Karthik Devarajan & Stanley Young & Paul Fogel & George Luta, 2021. "Assessing Methods for Evaluating the Number of Components in Non-Negative Matrix Factorization," Mathematics, MDPI, vol. 9(22), pages 1-13, November.
    10. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    11. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    12. Li, Hongjun & Li, Qi & Shi, Yutang, 2017. "Determining the number of factors when the number of factors can increase with sample size," Journal of Econometrics, Elsevier, vol. 197(1), pages 76-86.
    13. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    14. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    15. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    16. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
    17. Norman R. Swanson & Nii Ayi Armah, 2011. "Diffusion Index Models and Index Proxies: Recent Results and New Directions," Departmental Working Papers 201114, Rutgers University, Department of Economics.
    18. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    19. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    20. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1703.00703. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.