odm-package: Functions for detecting outlying parameters (peptides) or...
In OutlierDM: Outlier Detection for Multi-replicated High-throughput Data
Description
Details
Author(s)
References
See Also
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
Author(s)
Soo-Heang Eo <eo.sooheang@gmail.com> and HyungJun Cho <hj4cho@korea.ac.kr>
Maintainer: Soo-Heang Eo <eo.sooheang@gmail.com>
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
OutlierDM documentation built on May 1, 2019, 7:58 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References See Also
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 |
Author(s)
Soo-Heang Eo <eo.sooheang@gmail.com> and HyungJun Cho <hj4cho@korea.ac.kr>
Maintainer: Soo-Heang Eo <eo.sooheang@gmail.com>
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.