IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v180y2023ics0167947322002298.html
   My bibliography  Save this article

Jackstraw inference for AJIVE data integration

Author

Listed:
  • Yang, Xi
  • Hoadley, Katherine A.
  • Hannig, Jan
  • Marron, J.S.

Abstract

In the age of big data, data integration is a critical step especially in the understanding of how diverse data types work together and work separately. Among data integration methods, the Angle-Based Joint and Individual Variation Explained (AJIVE) approach is particularly attractive because it not only studies joint behavior but also individual behavior. Typically AJIVE scores indicate important relationships between data objects, such as clusters. An important challenge is understanding which features, i.e. variables, are associated with those relationships. This challenge is addressed by the proposal of a hypothesis test for assessing statistical significance of features. The new test is inspired by the related jackstraw method developed for Principal Component Analysis. We use a high-dimensional multi-genomic cancer data set as our strong motivation and deep illustration of the methodology.

Suggested Citation

  • Yang, Xi & Hoadley, Katherine A. & Hannig, Jan & Marron, J.S., 2023. "Jackstraw inference for AJIVE data integration," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:csdana:v:180:y:2023:i:c:s0167947322002298
    DOI: 10.1016/j.csda.2022.107649
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947322002298
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2022.107649?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Charles M. Perou & Therese Sørlie & Michael B. Eisen & Matt van de Rijn & Stefanie S. Jeffrey & Christian A. Rees & Jonathan R. Pollack & Douglas T. Ross & Hilde Johnsen & Lars A. Akslen & Øystein Flu, 2000. "Molecular portraits of human breast tumours," Nature, Nature, vol. 406(6797), pages 747-752, August.
    2. Feng, Qing & Jiang, Meilei & Hannig, Jan & Marron, J.S., 2018. "Angle-based joint and individual variation explained," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 241-265.
    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. Manish G & Anil Kumar Badana & Rama Rao Malla, 2017. "Emerging Diagnostic and Prognostic Biomarkers of Triple Negative Breast Cancer," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 1(3), pages 561-565, August.
    2. Jacob Elnaggar & Fern Tsien & Lucio Miele & Chindo Hicks & Clayton Yates & Melisa Davis, 2019. "An Integrative Genomics Approach for Associating Genetic Susceptibility with the Tumor Immune Microenvironment in Triple Negative Breast Cancer," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 15(1), pages 1-12, February.
    3. Egashira, Kento & Yata, Kazuyoshi & Aoshima, Makoto, 2024. "Asymptotic properties of hierarchical clustering in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    4. María Elena Martínez & Jonathan T Unkart & Li Tao & Candyce H Kroenke & Richard Schwab & Ian Komenaka & Scarlett Lin Gomez, 2017. "Prognostic significance of marital status in breast cancer survival: A population-based study," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    5. Yishai Shimoni, 2018. "Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-15, February.
    6. Marcin Pilarczyk & Mehdi Fazel-Najafabadi & Michal Kouril & Behrouz Shamsaei & Juozas Vasiliauskas & Wen Niu & Naim Mahi & Lixia Zhang & Nicholas A. Clark & Yan Ren & Shana White & Rashid Karim & Huan, 2022. "Connecting omics signatures and revealing biological mechanisms with iLINCS," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Junhee Seok & Ronald W Davis & Wenzhong Xiao, 2015. "A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-15, May.
    8. Qing Qu & Yan Mao & Xiao-chun Fei & Kun-wei Shen, 2013. "The Impact of Androgen Receptor Expression on Breast Cancer Survival: A Retrospective Study and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    9. Bourret, Pascale & Keating, Peter & Cambrosio, Alberto, 2011. "Regulating diagnosis in post-genomic medicine: Re-aligning clinical judgment?," Social Science & Medicine, Elsevier, vol. 73(6), pages 816-824, September.
    10. G. Gambardella & G. Viscido & B. Tumaini & A. Isacchi & R. Bosotti & D. di Bernardo, 2022. "A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    11. Yoo-Ah Kim & Stefan Wuchty & Teresa M Przytycka, 2011. "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
    12. Pauliina M. Munne & Lahja Martikainen & Iiris Räty & Kia Bertula & Nonappa & Janika Ruuska & Hanna Ala-Hongisto & Aino Peura & Babette Hollmann & Lilya Euro & Kerim Yavuz & Linda Patrikainen & Maria S, 2021. "Compressive stress-mediated p38 activation required for ERα + phenotype in breast cancer," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    13. Radhakrishnan Nagarajan & Marco Scutari, 2013. "Impact of Noise on Molecular Network Inference," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    14. R Joseph Bender & Feilim Mac Gabhann, 2013. "Expression of VEGF and Semaphorin Genes Define Subgroups of Triple Negative Breast Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-15, May.
    15. Marron, J.S., 2017. "Big Data in context and robustness against heterogeneity," Econometrics and Statistics, Elsevier, vol. 2(C), pages 73-80.
    16. Deepak Poduval & Zuzana Sichmanova & Anne Hege Straume & Per Eystein Lønning & Stian Knappskog, 2020. "The novel microRNAs hsa-miR-nov7 and hsa-miR-nov3 are over-expressed in locally advanced breast cancer," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-23, April.
    17. Mariana Segovia-Mendoza & Margarita Isabel Palacios-Arreola & Luz María Monroy-Escamilla & Alexandra Estela Soto-Piña & Karen Elizabeth Nava-Castro & Yizel Becerril-Alarcón & Roberto Camacho-Beiza & D, 2022. "Association of Serum Levels of Plasticizers Compounds, Phthalates and Bisphenols, in Patients and Survivors of Breast Cancer: A Real Connection?," IJERPH, MDPI, vol. 19(13), pages 1-22, June.
    18. Chi-Cheng Huang & Shih-Hsin Tu & Heng-Hui Lien & Jaan-Yeh Jeng & Ching-Shui Huang & Chi-Jung Huang & Liang-Chuan Lai & Eric Y Chuang, 2013. "Concurrent Gene Signatures for Han Chinese Breast Cancers," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    19. Fadia Gujam & Katie Dickson & Pamela McCall & Donald McMillan & Joanne Edwards, 2018. "The Relationship Between Androgen Receptor, Components of Tumour Microenvironment and Survival in Breast Cancer Molecular Subtypes," Cancer Therapy & Oncology International Journal, Juniper Publishers Inc., vol. 11(3), pages 77-85, July.
    20. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.

    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:eee:csdana:v:180:y:2023:i:c:s0167947322002298. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.