IDEAS home Printed from https://ideas.repec.org/a/adp/jbboaj/v10y2020i2p30-40.html
   My bibliography  Save this article

A Systematic Comparison of Methods Designed for Association Analysis with Multi-Omics Data

Author

Listed:
  • Xiaqiong Wang
  • Yalu Wen

    (Department of Statistics, University of Auckland, New Zealand)

Abstract

With high-throughput biotechnologies, emerging multi-omics data have provided unprecedented opportunities for detecting new disease-associated biomarkers. A commonly used strategy for such an analysis is to form various sub-hypotheses based on each omics data and their combinations, and then integrate them. Existing methods designed for combining correlated results can be adapted for these association tests. However, there lack systematic comparisons of their performance when applied to multi-omics data. In this study, we conducted extensive simulation studies to evaluate the impacts of 1) inter-correlation among multi-omics data; 2) interaction effects within and between different layers of omics data; and 3) the underlying disease model on the performance of five selected methods, including the Kost and McDermott method, the Omnibus-Fisher method, the truncated product method (TPM), the harmonic mean P-value (HMP) method and the minimum P-value method.

Suggested Citation

  • Xiaqiong Wang & Yalu Wen, 2020. "A Systematic Comparison of Methods Designed for Association Analysis with Multi-Omics Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 10(2), pages 30-40, August.
    • Handle: RePEc:adp:jbboaj:v:10:y:2020:i:2:p:30-40
    DOI: 10.19080/BBOAJ.2020.10.555783
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/bboaj/pdf/BBOAJ.MS.ID.555783.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/bboaj/BBOAJ.MS.ID.555783.php
    Download Restriction: no
    File URL: https://libkey.io/10.19080/BBOAJ.2020.10.555783?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. Sheng, Xuguang & Yang, Jingyun, 2013. "An adaptive truncated product method for combining dependent p-values," Economics Letters, Elsevier, vol. 119(2), pages 180-182.
    2. Gelio Alves & Yi-Kuo Yu, 2014. "Accuracy Evaluation of the Unified P-Value from Combining Correlated P-Values," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    3. Kost, James T. & McDermott, Michael P., 2002. "Combining dependent P-values," Statistics & Probability Letters, Elsevier, vol. 60(2), pages 183-190, November.
    4. Hou, Chia-Ding, 2005. "A simple approximation for the distribution of the weighted combination of non-independent or independent probabilities," Statistics & Probability Letters, Elsevier, vol. 73(2), pages 179-187, June.
    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. Chien Li-Chu, 2020. "Combining dependent p-values by gamma distributions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(4-6), pages 1-12, December.
    2. Hong Zhang & Zheyang Wu, 2023. "The generalized Fisher's combination and accurate p‐value calculation under dependence," Biometrics, The International Biometric Society, vol. 79(2), pages 1159-1172, June.
    3. Holm, Hakan J. & Samahita, Margaret, 2018. "Curating social image: Experimental evidence on the value of actions and selfies," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 83-104.
    4. Olga A Vsevolozhskaya & Min Shi & Fengjiao Hu & Dmitri V Zaykin, 2020. "DOT: Gene-set analysis by combining decorrelated association statistics," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-25, April.
    5. Arie Shaus & Yana Gerber & Shira Faigenbaum-Golovin & Barak Sober & Eli Piasetzky & Israel Finkelstein, 2020. "Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-15, September.
    6. Oliver M. Crook & Colin T. R. Davies & Lisa M. Breckels & Josie A. Christopher & Laurent Gatto & Paul D. W. Kirk & Kathryn S. Lilley, 2022. "Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    7. Wimmer, Thomas & Geyer-Klingeberg, Jerome & Hütter, Marie & Schmid, Florian & Rathgeber, Andreas, 2021. "The impact of speculation on commodity prices: A Meta-Granger analysis," Journal of Commodity Markets, Elsevier, vol. 22(C).
    8. Christiaan A de Leeuw & Joris M Mooij & Tom Heskes & Danielle Posthuma, 2015. "MAGMA: Generalized Gene-Set Analysis of GWAS Data," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-19, April.
    9. Clara Bicalho & Adam Bouyamourn & Thad Dunning, 2022. "Conditional Balance Tests: Increasing Sensitivity and Specificity With Prognostic Covariates," Papers 2205.10478, arXiv.org.
    10. Follmann, Dean & Proschan, Michael, 2012. "A test of location for exchangeable multivariate normal data with unknown correlation," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 115-125, February.
    11. Sang Cheol Kim & Seul Ji Lee & Won Jun Lee & Young Na Yum & Joo Hwan Kim & Soojung Sohn & Jeong Hill Park & Jeongmi Lee & Johan Lim & Sung Won Kwon, 2013. "Stouffer’s Test in a Large Scale Simultaneous Hypothesis Testing," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    12. Peter M. Aronow, 2012. "A General Method for Detecting Interference Between Units in Randomized Experiments," Sociological Methods & Research, , vol. 41(1), pages 3-16, February.

    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:adp:jbboaj:v:10:y:2020:i:2:p:30-40. 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: Robert Thomas (email available below). General contact details of provider: .

    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.