IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v175y2022ics016794732200127x.html
   My bibliography  Save this article

sJIVE: Supervised joint and individual variation explained

Author

Listed:
  • Palzer, Elise F.
  • Wendt, Christine H.
  • Bowler, Russell P.
  • Hersh, Craig P.
  • Safo, Sandra E.
  • Lock, Eric F.

Abstract

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. The proposed method, supervised joint and individual variation explained (sJIVE), can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures. These two components are weighted to compromise between explaining variation in the multi-source data and in the outcome. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data. An application to data from the COPDGene study explores gene expression and proteomic patterns associated with lung function.

Suggested Citation

  • Palzer, Elise F. & Wendt, Christine H. & Bowler, Russell P. & Hersh, Craig P. & Safo, Sandra E. & Lock, Eric F., 2022. "sJIVE: Supervised joint and individual variation explained," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:csdana:v:175:y:2022:i:c:s016794732200127x
    DOI: 10.1016/j.csda.2022.107547
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794732200127X
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2022.107547?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. Gen Li & Sungkyu Jung, 2017. "Incorporating covariates into integrated factor analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 73(4), pages 1433-1442, December.
    2. Irina Gaynanova & Gen Li, 2019. "Structural learning and integrative decomposition of multi‐view data," Biometrics, The International Biometric Society, vol. 75(4), pages 1121-1132, December.
    3. Witten Daniela M & Tibshirani Robert J., 2009. "Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-27, June.
    4. Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
    5. 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)

    Citations

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


    Cited by:

    1. Kim, Jonathan & Sandri, Brian J. & Rao, Raghavendra B. & Lock, Eric F., 2023. "Bayesian predictive modeling of multi-source multi-way data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).

    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. Xing Gao & Sungwon Lee & Gen Li & Sungkyu Jung, 2021. "Covariate‐driven factorization by thresholding for multiblock data," Biometrics, The International Biometric Society, vol. 77(3), pages 1011-1023, September.
    2. Sangyoon Yi & Raymond Ka Wai Wong & Irina Gaynanova, 2023. "Hierarchical nuclear norm penalization for multi‐view data integration," Biometrics, The International Biometric Society, vol. 79(4), pages 2933-2946, December.
    3. Zhao, Yuxuan & Matteson, David S. & Mostofsky, Stewart H. & Nebel, Mary Beth & Risk, Benjamin B., 2022. "Group linear non-Gaussian component analysis with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    4. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    5. 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).
    6. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
    7. Kui Shen & Nan Song & Youngchul Kim & Chunqiao Tian & Shara D Rice & Michael J Gabrin & W Fraser Symmans & Lajos Pusztai & Jae K Lee, 2012. "A Systematic Evaluation of Multi-Gene Predictors for the Pathological Response of Breast Cancer Patients to Chemotherapy," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
    8. Xiuli Du & Xiaohu Jiang & Jinguan Lin, 2023. "Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 975-1001, September.
    9. Wang, Wenjia & Zhou, Yi-Hui, 2021. "Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    10. Wang Zhu & Wang C.Y., 2010. "Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
    11. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    12. Tommaso Proietti, 2016. "On the Selection of Common Factors for Macroeconomic Forecasting," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 593-628, Emerald Group Publishing Limited.
    13. Federico Pavone & Juho Piironen & Paul-Christian Bürkner & Aki Vehtari, 2023. "Using reference models in variable selection," Computational Statistics, Springer, vol. 38(1), pages 349-371, March.
    14. Min Cai & Hui Dai & Yongyong Qiu & Yang Zhao & Ruyang Zhang & Minjie Chu & Juncheng Dai & Zhibin Hu & Hongbing Shen & Feng Chen, 2013. "SNP Set Association Analysis for Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
    15. Jose A Seoane & Colin Campbell & Ian N M Day & Juan P Casas & Tom R Gaunt, 2014. "Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-13, October.
    16. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
    17. Jianqing Fan & Yang Feng & Jiancheng Jiang & Xin Tong, 2016. "Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 275-287, March.
    18. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    19. Zambom, Adriano Zanin & Akritas, Michael G., 2015. "Nonparametric significance testing and group variable selection," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 51-60.
    20. Hatem Jemmali & Mohamed Salah Matoussi, 2012. "A Multidimensional Analysis of Water Poverty at A Local Scale- Application of Improved Water Poverty Index for Tunisia," Working Papers 730, Economic Research Forum, revised 2012.

    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:175:y:2022:i:c:s016794732200127x. 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.