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

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 imput...

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Main Authors:	Karpievitch, Yuliya V, Dabney, Alan R, Smith, Richard D
Format:	Online
Language:	English
Published:	BioMed Central 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3489534/

id	pubmed-3489534
recordtype	oai_dc
spelling	pubmed-34895342012-11-08 Normalization and missing value imputation for label-free LC-MS analysis Karpievitch, Yuliya V Dabney, Alan R Smith, Richard D Review 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. BioMed Central 2012-11-05 /pmc/articles/PMC3489534/ /pubmed/23176322 http://dx.doi.org/10.1186/1471-2105-13-S16-S5 Text en Copyright ©2012 Karpievitch et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Karpievitch, Yuliya V Dabney, Alan R Smith, Richard D
spellingShingle	Karpievitch, Yuliya V Dabney, Alan R Smith, Richard D Normalization and missing value imputation for label-free LC-MS analysis
author_facet	Karpievitch, Yuliya V Dabney, Alan R Smith, Richard D
author_sort	Karpievitch, Yuliya V
title	Normalization and missing value imputation for label-free LC-MS analysis
title_short	Normalization and missing value imputation for label-free LC-MS analysis
title_full	Normalization and missing value imputation for label-free LC-MS analysis
title_fullStr	Normalization and missing value imputation for label-free LC-MS analysis
title_full_unstemmed	Normalization and missing value imputation for label-free LC-MS analysis
title_sort	normalization and missing value imputation for label-free lc-ms analysis
description	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.
publisher	BioMed Central
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3489534/
_version_	1611921476124934144

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

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