This package provides several outlier detection algorithms for multi-replicated high-throughput data ranged from classical approaches to boxplot approaches based on a MA plot.
|License:||GPL version 3|
Maintainer: Soo-Heang Eo <[email protected]>
Eo, S-H and Cho, H (2015) OutlierDM: More robust outlier detection algorithms for multi-replicated high-throughput data.
Cho, H and Eo, S-H. (2015) Outlier detection for mass-spectrometry data.
Eo, S-H, Pak D, Choi J, Cho H (2012) Outlier detection using projection quantile regression for mass spectrometry data with low replication. BMC Res Notes.
Cho H, Lee JW, Kim Y-J, et al. (2008) OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data. Bioinformatics 24:882–884.
Min H-K, Hyung S-W, Shin J-W, 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.
Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21.
Dixon WJ (1951) Ratios involving extreme values. Ann Math Statistics 22:68–78.
Dixon WJ (1950) Analysis of extreme values. Ann Math Statistics 21:488–506.
Grubbs FE (1950) Sample criteria for testing outlying observations. Ann Math Statistics 21:27–58.
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