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

Hyungjun Cho; Yang Jin Kim; Hee Jung Jung; Sang Won Lee; Jae Won Lee

doi:10.1093/bioinformatics/btn012

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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, non-linear and non-parametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment.

Original language	English
Pages (from-to)	882-884
Number of pages	3
Journal	Bioinformatics
Volume	24
Issue number	6
DOIs	https://doi.org/10.1093/bioinformatics/btn012
Publication status	Published - 2008 Mar

Bibliographical note

Funding Information:
This work was supported by the Korea Research Foundation Grant (R14-2003-002-01002-0) for J.W. Lee. It was also supported by the Korea Research Foundation Grant (KRF-2007-331-C00065) for H. Cho and by 21C Frontier Project for Functional Proteomics (FPR05A1-400) for S.W. Lee.

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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Cite this

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title = "OutlierD: An R package for outlier detection using quantile regression on mass spectrometry data",

abstract = "It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, non-linear and non-parametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment.",

author = "Hyungjun Cho and Kim, \{Yang Jin\} and Jung, \{Hee Jung\} and Lee, \{Sang Won\} and Lee, \{Jae Won\}",

note = "Funding Information: This work was supported by the Korea Research Foundation Grant (R14-2003-002-01002-0) for J.W. Lee. It was also supported by the Korea Research Foundation Grant (KRF-2007-331-C00065) for H. Cho and by 21C Frontier Project for Functional Proteomics (FPR05A1-400) for S.W. Lee.",

year = "2008",

month = mar,

doi = "10.1093/bioinformatics/btn012",

language = "English",

volume = "24",

pages = "882--884",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

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AU - Cho, Hyungjun

AU - Kim, Yang Jin

AU - Jung, Hee Jung

AU - Lee, Sang Won

AU - Lee, Jae Won

N1 - Funding Information: This work was supported by the Korea Research Foundation Grant (R14-2003-002-01002-0) for J.W. Lee. It was also supported by the Korea Research Foundation Grant (KRF-2007-331-C00065) for H. Cho and by 21C Frontier Project for Functional Proteomics (FPR05A1-400) for S.W. Lee.

PY - 2008/3

Y1 - 2008/3

N2 - It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, non-linear and non-parametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment.

AB - It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, non-linear and non-parametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment.

UR - https://www.scopus.com/pages/publications/40749141227

U2 - 10.1093/bioinformatics/btn012

DO - 10.1093/bioinformatics/btn012

M3 - Article

C2 - 18187441

AN - SCOPUS:40749141227

SN - 1367-4803

VL - 24

SP - 882

EP - 884

JO - Bioinformatics

JF - Bioinformatics

IS - 6

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

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