IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v8y2009i1n13.html

Detecting Outlier Samples in Microarray Data

Author

Listed:
  • Shieh Albert D

    (Harvard University)

  • Hung Yeung Sam

    (University of Hong Kong)

Abstract

In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data. We propose an outlier detection method based on principal component analysis (PCA) and robust estimation of Mahalanobis distances that is fully automatic. We demonstrate that our outlier detection method identifies biologically significant outliers with high accuracy and that outlier removal improves the prediction accuracy of classifiers. Our outlier detection method is closely related to existing robust PCA methods, so we compare our outlier detection method to a prominent robust PCA method.

Suggested Citation

  • Shieh Albert D & Hung Yeung Sam, 2009. "Detecting Outlier Samples in Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, February.
    • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:13
    DOI: 10.2202/1544-6115.1426
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1426
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1426?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Mu & Ghodsi, Ali, 2006. "Automatic dimensionality selection from the scree plot via the use of profile likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 918-930, November.
    2. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January.
    3. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    4. Lee, Jae Won & Lee, Jung Bok & Park, Mira & Song, Seuck Heun, 2005. "An extensive comparison of recent classification tools applied to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 869-885, April.
    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. Junlong Zhao & Chao Liu & Lu Niu & Chenlei Leng, 2019. "Multiple influential point detection in high dimensional regression spaces," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 385-408, April.
    2. Laura Selicato & Flavia Esposito & Grazia Gargano & Maria Carmela Vegliante & Giuseppina Opinto & Gian Maria Zaccaria & Sabino Ciavarella & Attilio Guarini & Nicoletta Del Buono, 2021. "A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices," Mathematics, MDPI, vol. 9(8), pages 1-26, April.
    3. Asuman Turkmen & Nedret Billor, 2013. "Partial least squares classification for high dimensional data using the PCOUT algorithm," Computational Statistics, Springer, vol. 28(2), pages 771-788, April.

    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. Anne-Laure Boulesteix & Robert Hable & Sabine Lauer & Manuel J. A. Eugster, 2015. "A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 201-212, August.
    2. Alan R Dabney & John D Storey, 2007. "Optimality Driven Nearest Centroid Classification from Genomic Data," PLOS ONE, Public Library of Science, vol. 2(10), pages 1-7, October.
    3. Yang, Tae Young, 2009. "Simple Bayesian binary framework for discovering significant genes and classifying cancer diagnosis," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1743-1754, March.
    4. Dong, Kai & Pang, Herbert & Tong, Tiejun & Genton, Marc G., 2016. "Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 127-142.
    5. Valkenborg Dirk & Van Sanden Suzy & Lin Dan & Kasim Adetayo & Zhu Qi & Haldermans Philippe & Jansen Ivy & Shkedy Ziv & Burzykowski Tomasz, 2008. "A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-22, March.
    6. Lambert-Lacroix, Sophie & Peyre, Julie, 2006. "Local likelihood regression in generalized linear single-index models with applications to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 2091-2113, December.
    7. Asuman Turkmen & Nedret Billor, 2013. "Partial least squares classification for high dimensional data using the PCOUT algorithm," Computational Statistics, Springer, vol. 28(2), pages 771-788, April.
    8. Scrucca, Luca, 2007. "Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 438-451, September.
    9. Conde David & Salvador Bonifacio & Rueda Cristina & Fernández Miguel A., 2013. "Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(5), pages 583-602, October.
    10. 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.
    11. Frénay, Benoît & Doquire, Gauthier & Verleysen, Michel, 2014. "Estimating mutual information for feature selection in the presence of label noise," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 832-848.
    12. Angelo Mele & Lingxin Hao & Joshua Cape & Carey E. Priebe, 2019. "Spectral inference for large Stochastic Blockmodels with nodal covariates," Papers 1908.06438, arXiv.org, revised Mar 2021.
    13. Wang, Tao & Zhu, Lixing, 2013. "Sparse sufficient dimension reduction using optimal scoring," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 223-232.
    14. G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
    15. Kubokawa, Tatsuya & Srivastava, Muni S., 2008. "Estimation of the precision matrix of a singular Wishart distribution and its application in high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 1906-1928, October.
    16. Jansen, Nora & Hinz, Oliver & Deusser, Clemens & Strufe, Thorsten, 2021. "Is the Buzz on? – A Buzz Detection System for Viral Posts in Social Media," Journal of Interactive Marketing, Elsevier, vol. 56(C), pages 1-17.
    17. Jingxuan Luo & Xuejiao Li & Chongxiu Yu & Gaorong Li, 2023. "Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure Among Predictors," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 614-637, November.
    18. Makoto Aoshima & Kazuyoshi Yata, 2014. "A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 983-1010, October.
    19. Sancar Adali & Carey E. Priebe, 2016. "Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 485-506, October.
    20. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.

    More about this item

    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:bpj:sagmbi:v:8:y:2009:i:1:n:13. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.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.