Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elem...
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Autor*in:	van Wieringen, Wessel N [verfasserIn] Unger, Kristian Leday, Gwenaël GR Krijgsman, Oscar de Menezes, Renée X Ylstra, Bauke van de Wiel, Mark A

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Matching Procedure Gene Expression Array Copy Number Data Prostate Cancer Sample Gene Expression Feature

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

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 13(2012), 1 vom: 04. Mai
Übergeordnetes Werk:	volume:13 ; year:2012 ; number:1 ; day:04 ; month:05

Links:	Volltext

DOI / URN:	10.1186/1471-2105-13-80

Katalog-ID:	SPR026877279

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matchkey_str	article:14712105:2012----::acigfrachngnepesomcorafauefrhproef
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allfields	10.1186/1471-2105-13-80 doi (DE-627)SPR026877279 (SPR)1471-2105-13-80-e DE-627 ger DE-627 rakwb eng van Wieringen, Wessel N verfasserin aut Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Wieringen et al.; licensee BioMed Central Ltd. 2012 Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. Matching Procedure (dpeaa)DE-He213 Gene Expression Array (dpeaa)DE-He213 Copy Number Data (dpeaa)DE-He213 Prostate Cancer Sample (dpeaa)DE-He213 Gene Expression Feature (dpeaa)DE-He213 Unger, Kristian aut Leday, Gwenaël GR aut Krijgsman, Oscar aut de Menezes, Renée X aut Ylstra, Bauke aut van de Wiel, Mark A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), 1 vom: 04. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:1 day:04 month:05 https://dx.doi.org/10.1186/1471-2105-13-80 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 1 04 05
spelling	10.1186/1471-2105-13-80 doi (DE-627)SPR026877279 (SPR)1471-2105-13-80-e DE-627 ger DE-627 rakwb eng van Wieringen, Wessel N verfasserin aut Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Wieringen et al.; licensee BioMed Central Ltd. 2012 Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. Matching Procedure (dpeaa)DE-He213 Gene Expression Array (dpeaa)DE-He213 Copy Number Data (dpeaa)DE-He213 Prostate Cancer Sample (dpeaa)DE-He213 Gene Expression Feature (dpeaa)DE-He213 Unger, Kristian aut Leday, Gwenaël GR aut Krijgsman, Oscar aut de Menezes, Renée X aut Ylstra, Bauke aut van de Wiel, Mark A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), 1 vom: 04. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:1 day:04 month:05 https://dx.doi.org/10.1186/1471-2105-13-80 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 1 04 05
allfields_unstemmed	10.1186/1471-2105-13-80 doi (DE-627)SPR026877279 (SPR)1471-2105-13-80-e DE-627 ger DE-627 rakwb eng van Wieringen, Wessel N verfasserin aut Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Wieringen et al.; licensee BioMed Central Ltd. 2012 Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. Matching Procedure (dpeaa)DE-He213 Gene Expression Array (dpeaa)DE-He213 Copy Number Data (dpeaa)DE-He213 Prostate Cancer Sample (dpeaa)DE-He213 Gene Expression Feature (dpeaa)DE-He213 Unger, Kristian aut Leday, Gwenaël GR aut Krijgsman, Oscar aut de Menezes, Renée X aut Ylstra, Bauke aut van de Wiel, Mark A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), 1 vom: 04. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:1 day:04 month:05 https://dx.doi.org/10.1186/1471-2105-13-80 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 1 04 05
allfieldsGer	10.1186/1471-2105-13-80 doi (DE-627)SPR026877279 (SPR)1471-2105-13-80-e DE-627 ger DE-627 rakwb eng van Wieringen, Wessel N verfasserin aut Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Wieringen et al.; licensee BioMed Central Ltd. 2012 Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. Matching Procedure (dpeaa)DE-He213 Gene Expression Array (dpeaa)DE-He213 Copy Number Data (dpeaa)DE-He213 Prostate Cancer Sample (dpeaa)DE-He213 Gene Expression Feature (dpeaa)DE-He213 Unger, Kristian aut Leday, Gwenaël GR aut Krijgsman, Oscar aut de Menezes, Renée X aut Ylstra, Bauke aut van de Wiel, Mark A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), 1 vom: 04. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:1 day:04 month:05 https://dx.doi.org/10.1186/1471-2105-13-80 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 1 04 05
allfieldsSound	10.1186/1471-2105-13-80 doi (DE-627)SPR026877279 (SPR)1471-2105-13-80-e DE-627 ger DE-627 rakwb eng van Wieringen, Wessel N verfasserin aut Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Wieringen et al.; licensee BioMed Central Ltd. 2012 Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. Matching Procedure (dpeaa)DE-He213 Gene Expression Array (dpeaa)DE-He213 Copy Number Data (dpeaa)DE-He213 Prostate Cancer Sample (dpeaa)DE-He213 Gene Expression Feature (dpeaa)DE-He213 Unger, Kristian aut Leday, Gwenaël GR aut Krijgsman, Oscar aut de Menezes, Renée X aut Ylstra, Bauke aut van de Wiel, Mark A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 13(2012), 1 vom: 04. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:13 year:2012 number:1 day:04 month:05 https://dx.doi.org/10.1186/1471-2105-13-80 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 1 04 05
language	English
source	Enthalten in BMC bioinformatics 13(2012), 1 vom: 04. Mai volume:13 year:2012 number:1 day:04 month:05
sourceStr	Enthalten in BMC bioinformatics 13(2012), 1 vom: 04. Mai volume:13 year:2012 number:1 day:04 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Matching Procedure Gene Expression Array Copy Number Data Prostate Cancer Sample Gene Expression Feature
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	van Wieringen, Wessel N @@aut@@ Unger, Kristian @@aut@@ Leday, Gwenaël GR @@aut@@ Krijgsman, Oscar @@aut@@ de Menezes, Renée X @@aut@@ Ylstra, Bauke @@aut@@ van de Wiel, Mark A @@aut@@
publishDateDaySort_date	2012-05-04T00:00:00Z
hierarchy_top_id	326644814
id	SPR026877279
language_de	englisch
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author	van Wieringen, Wessel N
spellingShingle	van Wieringen, Wessel N misc Matching Procedure misc Gene Expression Array misc Copy Number Data misc Prostate Cancer Sample misc Gene Expression Feature Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses
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topic_title	Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses Matching Procedure (dpeaa)DE-He213 Gene Expression Array (dpeaa)DE-He213 Copy Number Data (dpeaa)DE-He213 Prostate Cancer Sample (dpeaa)DE-He213 Gene Expression Feature (dpeaa)DE-He213
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title	Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses
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title_full	Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses
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author_browse	van Wieringen, Wessel N Unger, Kristian Leday, Gwenaël GR Krijgsman, Oscar de Menezes, Renée X Ylstra, Bauke van de Wiel, Mark A
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doi_str_mv	10.1186/1471-2105-13-80
title_sort	matching of array cgh and gene expression microarray features for the purpose of integrative genomic analyses
title_auth	Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses
abstract	Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. © van Wieringen et al.; licensee BioMed Central Ltd. 2012
abstractGer	Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. © van Wieringen et al.; licensee BioMed Central Ltd. 2012
abstract_unstemmed	Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. © van Wieringen et al.; licensee BioMed Central Ltd. 2012
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title_short	Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses
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author2	Unger, Kristian Leday, Gwenaël GR Krijgsman, Oscar de Menezes, Renée X Ylstra, Bauke van de Wiel, Mark A
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Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

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