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Statistical Applications in Genetics and Molecular Biology

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Volume 8, Issue 1 (Feb 2009)


Detecting Outlier Samples in Microarray Data

Albert D Shieh
  • Harvard University
/ Yeung Sam Hung
  • University of Hong Kong
Published Online: 2009-02-11 | DOI: https://doi.org/10.2202/1544-6115.1426

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.

About the article

Published Online: 2009-02-11

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1426. Export Citation

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