Description Details Author(s) References See Also Examples
This package provides outlier detection algorithms for multiple replicated high-throughput data, especially in the field of mass spectrometry.
Package: | OutlierDM |
Type: | Package |
Version: | 1.1-0 |
Date: | 2014-12-31 |
License: | GPL version 3 |
LazyLoad: | no |
Soo-Heang Eo <eo.sooheang@gmail.com> and HyungJun Cho <hj4cho@korea.ac.kr>
Maintainer: Soo-Heang Eo <eo.sooheang@gmail.com>
Cho H, and Eo, S-H. (2014). Outlier Detection for Mass-Spectrometry Data. Submitted.
Eo, S-H, Pak, D, Choi, J, and Cho, H. (2012). Outlier Detection for Multiplicative High-throughput Data. BMC Research Notes, 5, 1–6.
Cho et al. (2008). OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data. Bioinformatics, 24(6), 882–884.
Min 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## Not run:
data(lcms3)
## Fit projection approaches
fit.proj.const <- odm(lcms3, method="constant")
fit.proj.linear <- odm(lcms3, method="linear")
fit.proj.nonlin <- odm(lcms3, method="nonlin")
fit.proj.nonpara <- odm(lcms3, method="nonpar", lbda = 1)
par(mfrow = c(2,2))
plot(fit.proj.const, main = "Constant")
plot(fit.proj.linear, main = "Linear")
plot(fit.proj.nonlin, main = "NonLinear")
plot(fit.proj.nonpara, main = "Nonparametric")
## Fit pairwise OutlierD algorithm
fit0 <- odm(lcms3, type = "pair")
plot(fit0)
## End(Not run)
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