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

Hyungjun Cho, Yang Jin Kim, Hee Jung Jung, Sang Won Lee, Jae Won Lee

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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, non-linear and non-parametric 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.

Original languageEnglish
Pages (from-to)882-884
Number of pages3
JournalBioinformatics
Volume24
Issue number6
DOIs
Publication statusPublished - 2008 Mar

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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