alexa Robust Detection of Outlier Samples and Genes in Expression Datasets
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Robust Detection of Outlier Samples and Genes in Expression Datasets

Ahmad Barghash1,2, Taner Arslan1 and Volkhard Helms1*

1Center for Bioinformatics, Saarland University, Saarbruecken, Germany

2Saarbruecken Graduate School of Computer Science, Saarbruecken, Germany

*Corresponding Author:
Volkhard Helms
Center for Bioinformatics
Campus E2 1, R. 315
P.O. Box 15 11 50
D-66041 Saarbrücken, Germany
Tel: +49 681 302 70701
Fax: +49 681 302 70702
E-mail: [email protected]

Received Date: December 29, 2015; Accepted Date: February 12, 2016; Published Date: February 16, 2016

Citation: Barghash A, Arslan T, Helms V (2016) Robust Detection of Outlier Samples and Genes in Expression Datasets. J Proteomics Bioinform 9:038-048. doi:10.4172/jpb.1000387

Copyright: © 2016 Barghash A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Expression and methylation datasets are standard genomic techniques and an increasing number of computational methods are implemented to aid in analyzing the huge and complex amount of generated data. Such generated datasets often contain a sizeable fraction of outliers that cause misleading results in downstream analysis. Here, we present a comprehensive approach to detect sample and gene outliers in expression or methylation datasets. The core algorithms detected most outliers that were artificially introduced by us. Sample outliers detected by hierarchical clustering are validated by the Silhouette coefficient. At the gene level, the GESD, Boxplot, and MAD algorithms detected with f-measure of at least 83% the simulated outlier genes in non-intersected distributions. This combined approach detected many outliers in publicly available datasets from the TCGA and GEO portals. Frequently, some functionally similar genes marked as outliers turned out to have outlier observations in common samples. As such cases may be of special interest, they are labeled for further investigations. Expression and DNA methylation datasets should clearly be checked for outlier points before proceeding with any further analysis. We suggest that already 2 outlier observations are enough to label an outlier gene as they are enough to ruin a perfect co-expression. Besides, outliers might also carry useful information and thus functionally similar outliers should be labeled for further investigation. The presented software is freely available via github

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