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Title: Normalization and missing value imputation for label-free LC-MS analysis

Abstract

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1074318
Report Number(s):
PNNL-SA-94014
34708; KP1601010
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
BMC Bioinformatics, 13(Suppl 16):Article No. S5
Additional Journal Information:
Journal Name: BMC Bioinformatics, 13(Suppl 16):Article No. S5
Country of Publication:
United States
Language:
English
Subject:
Environmental Molecular Sciences Laboratory

Citation Formats

Karpievitch, Yuliya, Dabney, Alan R., and Smith, Richard D. Normalization and missing value imputation for label-free LC-MS analysis. United States: N. p., 2012. Web. doi:10.1186/1471-2105-13-S16-S5.
Karpievitch, Yuliya, Dabney, Alan R., & Smith, Richard D. Normalization and missing value imputation for label-free LC-MS analysis. United States. doi:10.1186/1471-2105-13-S16-S5.
Karpievitch, Yuliya, Dabney, Alan R., and Smith, Richard D. Mon . "Normalization and missing value imputation for label-free LC-MS analysis". United States. doi:10.1186/1471-2105-13-S16-S5.
@article{osti_1074318,
title = {Normalization and missing value imputation for label-free LC-MS analysis},
author = {Karpievitch, Yuliya and Dabney, Alan R. and Smith, Richard D.},
abstractNote = {Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.},
doi = {10.1186/1471-2105-13-S16-S5},
journal = {BMC Bioinformatics, 13(Suppl 16):Article No. S5},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {11}
}