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 va...
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Autor*in:	Karpievitch, Yuliya V [verfasserIn] Dabney, Alan R Smith, Richard D

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Mass Spectrometry Data Batch Effect Peptide Abundance Instrument Detection Limit Surrogate Variable Analysis

Anmerkung:	© Karpievitch et al.; licensee BioMed Central Ltd. 2012

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 13(2012), Suppl 16 vom: 05. Nov.
Übergeordnetes Werk:	volume:13 ; year:2012 ; number:Suppl 16 ; day:05 ; month:11

Links:	Volltext

DOI / URN:	10.1186/1471-2105-13-S16-S5

Katalog-ID:	SPR026880490

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allfields	10.1186/1471-2105-13-S16-S5 doi (DE-627)SPR026880490 (SPR)1471-2105-13-S16-S5-e DE-627 ger DE-627 rakwb eng Karpievitch, Yuliya V verfasserin aut Normalization and missing value imputation for label-free LC-MS analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Karpievitch et al.; licensee BioMed Central Ltd. 2012 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. Mass Spectrometry Data (dpeaa)DE-He213 Batch Effect (dpeaa)DE-He213 Peptide Abundance (dpeaa)DE-He213 Instrument Detection Limit (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Dabney, Alan R aut Smith, Richard D aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 16 vom: 05. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 16 day:05 month:11 https://dx.doi.org/10.1186/1471-2105-13-S16-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 16 05 11
spelling	10.1186/1471-2105-13-S16-S5 doi (DE-627)SPR026880490 (SPR)1471-2105-13-S16-S5-e DE-627 ger DE-627 rakwb eng Karpievitch, Yuliya V verfasserin aut Normalization and missing value imputation for label-free LC-MS analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Karpievitch et al.; licensee BioMed Central Ltd. 2012 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. Mass Spectrometry Data (dpeaa)DE-He213 Batch Effect (dpeaa)DE-He213 Peptide Abundance (dpeaa)DE-He213 Instrument Detection Limit (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Dabney, Alan R aut Smith, Richard D aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 16 vom: 05. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 16 day:05 month:11 https://dx.doi.org/10.1186/1471-2105-13-S16-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 16 05 11
allfields_unstemmed	10.1186/1471-2105-13-S16-S5 doi (DE-627)SPR026880490 (SPR)1471-2105-13-S16-S5-e DE-627 ger DE-627 rakwb eng Karpievitch, Yuliya V verfasserin aut Normalization and missing value imputation for label-free LC-MS analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Karpievitch et al.; licensee BioMed Central Ltd. 2012 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. Mass Spectrometry Data (dpeaa)DE-He213 Batch Effect (dpeaa)DE-He213 Peptide Abundance (dpeaa)DE-He213 Instrument Detection Limit (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Dabney, Alan R aut Smith, Richard D aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 16 vom: 05. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 16 day:05 month:11 https://dx.doi.org/10.1186/1471-2105-13-S16-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 16 05 11
allfieldsGer	10.1186/1471-2105-13-S16-S5 doi (DE-627)SPR026880490 (SPR)1471-2105-13-S16-S5-e DE-627 ger DE-627 rakwb eng Karpievitch, Yuliya V verfasserin aut Normalization and missing value imputation for label-free LC-MS analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Karpievitch et al.; licensee BioMed Central Ltd. 2012 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. Mass Spectrometry Data (dpeaa)DE-He213 Batch Effect (dpeaa)DE-He213 Peptide Abundance (dpeaa)DE-He213 Instrument Detection Limit (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Dabney, Alan R aut Smith, Richard D aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 16 vom: 05. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 16 day:05 month:11 https://dx.doi.org/10.1186/1471-2105-13-S16-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 16 05 11
allfieldsSound	10.1186/1471-2105-13-S16-S5 doi (DE-627)SPR026880490 (SPR)1471-2105-13-S16-S5-e DE-627 ger DE-627 rakwb eng Karpievitch, Yuliya V verfasserin aut Normalization and missing value imputation for label-free LC-MS analysis 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Karpievitch et al.; licensee BioMed Central Ltd. 2012 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. Mass Spectrometry Data (dpeaa)DE-He213 Batch Effect (dpeaa)DE-He213 Peptide Abundance (dpeaa)DE-He213 Instrument Detection Limit (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Dabney, Alan R aut Smith, Richard D aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), Suppl 16 vom: 05. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:Suppl 16 day:05 month:11 https://dx.doi.org/10.1186/1471-2105-13-S16-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 Suppl 16 05 11
language	English
source	Enthalten in BMC bioinformatics 13(2012), Suppl 16 vom: 05. Nov. volume:13 year:2012 number:Suppl 16 day:05 month:11
sourceStr	Enthalten in BMC bioinformatics 13(2012), Suppl 16 vom: 05. Nov. volume:13 year:2012 number:Suppl 16 day:05 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mass Spectrometry Data Batch Effect Peptide Abundance Instrument Detection Limit Surrogate Variable Analysis
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container_title	BMC bioinformatics
authorswithroles_txt_mv	Karpievitch, Yuliya V @@aut@@ Dabney, Alan R @@aut@@ Smith, Richard D @@aut@@
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author	Karpievitch, Yuliya V
spellingShingle	Karpievitch, Yuliya V misc Mass Spectrometry Data misc Batch Effect misc Peptide Abundance misc Instrument Detection Limit misc Surrogate Variable Analysis Normalization and missing value imputation for label-free LC-MS analysis
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topic_title	Normalization and missing value imputation for label-free LC-MS analysis Mass Spectrometry Data (dpeaa)DE-He213 Batch Effect (dpeaa)DE-He213 Peptide Abundance (dpeaa)DE-He213 Instrument Detection Limit (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213
topic	misc Mass Spectrometry Data misc Batch Effect misc Peptide Abundance misc Instrument Detection Limit misc Surrogate Variable Analysis
topic_unstemmed	misc Mass Spectrometry Data misc Batch Effect misc Peptide Abundance misc Instrument Detection Limit misc Surrogate Variable Analysis
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title	Normalization and missing value imputation for label-free LC-MS analysis
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title_full	Normalization and missing value imputation for label-free LC-MS analysis
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doi_str_mv	10.1186/1471-2105-13-S16-S5
title_sort	normalization and missing value imputation for label-free lc-ms analysis
title_auth	Normalization and missing value imputation for label-free LC-MS analysis
abstract	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. © Karpievitch et al.; licensee BioMed Central Ltd. 2012
abstractGer	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. © Karpievitch et al.; licensee BioMed Central Ltd. 2012
abstract_unstemmed	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. © Karpievitch et al.; licensee BioMed Central Ltd. 2012
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title_short	Normalization and missing value imputation for label-free LC-MS analysis
url	https://dx.doi.org/10.1186/1471-2105-13-S16-S5
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Normalization and missing value imputation for label-free LC-MS analysis

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