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

Bioinformatics

Volumn 24, Issue 6, 2008, Pages 882-884

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

^a Korea University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL PARAMETERS; ARTICLE; COMPUTER ANALYSIS; COMPUTER INTERFACE; COMPUTER PROGRAM; DATA MINING; HIGH THROUGHPUT SCREENING; INFORMATION PROCESSING; INTERNET; LINEAR SYSTEM; MASS SPECTROMETRY; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROTEOMICS; REGRESSION ANALYSIS;

EID: 40749141227     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn012     Document Type: Article

References (5)

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2. Koenker,R. (2005) Quantile Regression. Cambridge University Press, New York.
3. Min,H.-K. et al. (2007) Ultrahigh-pressure dual online solid phase extraction/capillary reverse-phase liquid chromatography/tandem mass spectrometry (DO-SPE/cRPLC/MS/MS): A versatile separation platform for high-throughput and highly sensitive proteomic analyses. Electrophoresis, 28, 1012-1021.
4. Peng,J. et al. (2003) Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: The yeast proteome. J. Proteome Res. 2, 43-50.
5. Zhang,H. et al. (2003) Identification and quantification of N linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol., 21, 660-666.