IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i18p3376-d917305.html
   My bibliography  Save this article

DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes

Author

Listed:
  • Juan Antonio Villatoro-García

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain
    Bioinformatics Unit, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, 18016 Granada, Spain)

  • Jordi Martorell-Marugán

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain
    Bioinformatics Unit, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, 18016 Granada, Spain
    Data Science for Heath Research Unit, Fondazione Bruno Kessler, 38123 Trento, Italy)

  • Daniel Toro-Domínguez

    (Medical Genomics, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, 18016 Granada, Spain)

  • Yolanda Román-Montoya

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain)

  • Pedro Femia

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain)

  • Pedro Carmona-Sáez

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain
    Bioinformatics Unit, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, 18016 Granada, Spain)

Abstract

Meta-analysis techniques allow researchers to jointly analyse different studies to determine common effects. In the field of transcriptomics, these methods have gained popularity in recent years due to the increasing number of datasets that are available in public repositories. Despite this, there is a limited number of statistical software packages that implement proper meta-analysis functionalities for this type of data. This article describes DExMA, an R package that provides a set of functions for performing gene expression meta-analyses, from data downloading to results visualization. Additionally, we implemented functions to control the number of missing genes, which can be a major issue when comparing studies generated with different analytical platforms. DExMA is freely available in the Bioconductor repository.

Suggested Citation

  • Juan Antonio Villatoro-García & Jordi Martorell-Marugán & Daniel Toro-Domínguez & Yolanda Román-Montoya & Pedro Femia & Pedro Carmona-Sáez, 2022. "DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3376-:d:917305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/18/3376/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/18/3376/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yaowu Liu & Jun Xie, 2020. "Cauchy Combination Test: A Powerful Test With Analytic p-Value Calculation Under Arbitrary Dependency Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 393-402, January.
    2. N A Heard & P Rubin-Delanchy, 2018. "Choosing between methods of combining $p$-values," Biometrika, Biometrika Trust, vol. 105(1), pages 239-246.
    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. Xiong, Peihan & Hu, Taizhong, 2022. "On Samuel’s p-value model and the Simes test under dependence," Statistics & Probability Letters, Elsevier, vol. 187(C).
    2. Song, Zhi & Mukherjee, Amitava & Zhang, Jiujun, 2021. "Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment," European Journal of Operational Research, Elsevier, vol. 289(1), pages 177-196.
    3. Zichen Zhang & Ye Eun Bae & Jonathan R. Bradley & Lang Wu & Chong Wu, 2022. "SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Yuzhuo Ma & Yanlong Zhao & Ji-Feng Zhang & Wenjian Bi, 2025. "Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    5. Vicente Gallego & Ramon Oller, 2024. "A Kernel approach for extending nonparametric multivariate analysis of variance in high-dimensional settings," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1315-1335, November.
    6. Xiduo Chen & Xingdong Feng & Antonio F. Galvao & Yeheng Ge, 2025. "Treatment Effects Inference with High-Dimensional Instruments and Control Variables," Papers 2503.20149, arXiv.org.
    7. Lulu Shang & Peijun Wu & Xiang Zhou, 2025. "Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    8. Yu, Xiufan & Yao, Jiawei & Xue, Lingzhou, 2024. "Power enhancement for testing multi-factor asset pricing models via Fisher’s method," Journal of Econometrics, Elsevier, vol. 239(2).
    9. Zimmermann, Paul, 2021. "The role of the leverage effect in the price discovery process of credit markets," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    10. Paulo C. Rodrigues & Vanda M. Lourenço, 2020. "Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço," Computational Statistics, Springer, vol. 35(1), pages 57-58, March.
    11. 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.
    12. Haque Md Rejuan & Kubatko Laura, 2024. "A global test of hybrid ancestry from genome-scale data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 23(1), pages 1-18, January.
    13. Xin Yuan & Yanran Ma & Ruitian Gao & Shuya Cui & Yifan Wang & Botao Fa & Shiyang Ma & Ting Wei & Shuangge Ma & Zhangsheng Yu, 2024. "HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    14. He Xu & Yuzhuo Ma & Lin-lin Xu & Yin Li & Yufei Liu & Ying Li & Xu-jie Zhou & Wei Zhou & Seunggeun Lee & Peipei Zhang & Weihua Yue & Wenjian Bi, 2025. "SPAGRM: effectively controlling for sample relatedness in large-scale genome-wide association studies of longitudinal traits," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    15. Patrick B. Langthaler & Riccardo Ceccato & Luigi Salmaso & Rosa Arboretti & Arne C. Bathke, 2023. "Permutation testing for thick data when the number of variables is much greater than the sample size: recent developments and some recommendations," Computational Statistics, Springer, vol. 38(1), pages 101-132, March.
    16. Meida Wang & Shuanglin Zhang & Qiuying Sha, 2022. "A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-13, April.
    17. Wang, Guanpeng & Wu, Jiujing & Cui, Hengjian, 2024. "Cross projection test for mean vectors via multiple random splits in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
    18. Seonghun Cho & Minsup Shin & Young Hyun Cho & Johan Lim, 2025. "Change point detection in high dimensional covariance matrix using Pillai’s statistics," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(1), pages 53-84, March.
    19. Andrea Cerasa, 2022. "Testing for Benford’s Law in very small samples: Simulation study and a new test proposal," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-17, July.
    20. Congran Yu & Hengjian Cui, 2025. "Feature screening via false discovery rate control for linear model with multivariate responses," Statistical Papers, Springer, vol. 66(2), pages 1-29, 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:gam:jmathe:v:10:y:2022:i:18:p:3376-:d:917305. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.