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Bioinformatics Advance Access published online on January 10, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn012
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

OutlierD: An R package for outlier detection using quantile regression on mass spectrometry data

HyungJun Cho 1,2, Yang-jin Kim 3, Hee Jung Jung 4, Sang-Won Lee 4 and Jae Won Lee 1,*

1Department of Statistics, 2Department of Biostatistics, 3Institute of Statistics, 4Department of Chemistry, Korea University, Seoul, Korea

*To whom correspondence should be addressed. Jae Won Lee, E-mail: jael{at}korea.ac.kr


  Abstract

Summary: It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, nonlinear, and nonparametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment.

Availability: An R package, OutlierD, is available at the Bioconductor project (http://www.bioconductor.org).

Contact: jael{at}korea.ac.kr

Associate Editor: Dr. Limsoon Wong

Received on August 10, 2007; revised on January 4, 2008; accepted on January 4, 2008

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