OutlierDM (version 1.1.1)

OutlierDM-package: Functions for detecting outlying parameters (peptides) or observations (samples) in multi-replicated high-throughtput data such as mass spectrometry experiments

Description

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.

Details

Package:

OutlierDM

Type:

Package

Version:

1.1.1

Date:

2014-12-23

License:

GPL version 3

LazyLoad:

no

References

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.

See Also

odm, odm.control, quantreg