title: OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data.
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.
  
  
  BACKGROUND
  An R package, OutlierD, is available at the Bioconductor project at http://www.bioconductor.org
authors.affiliation: Department of Statistics, Department of Biostatistics, Institute of Statistics and Department of Chemistry, Korea University, Seoul, Korea.
authors.forename: Hyungjun
authors.initials: H
authors.lastname: Cho
authors.forename: Yang-Jin
authors.initials: YJ
authors.lastname: Kim
authors.forename: Hee Jung
authors.initials: HJ
authors.lastname: Jung
authors.forename: Sang-Won
authors.initials: SW
authors.lastname: Lee
authors.forename: Jae Won
authors.initials: JW
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D013058:Mass Spectrometry; D010449:Peptide Mapping / Q000379:methods*; D011381:Programming Languages; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software