quantro: a data-driven approach to guide the choice of an appropriate normalization method
Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hicks, Stephanie C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Anmerkung: |
© Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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| Übergeordnetes Werk: |
Enthalten in: Genome biology - London : BioMed Central, 2000, 16(2015), 1 vom: 04. Juni |
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| Übergeordnetes Werk: |
volume:16 ; year:2015 ; number:1 ; day:04 ; month:06 |
| Links: |
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| DOI / URN: |
10.1186/s13059-015-0679-0 |
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| 520 | |a Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. | ||
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10.1186/s13059-015-0679-0 doi (DE-627)SPR03002319X (SPR)s13059-015-0679-0-e DE-627 ger DE-627 rakwb eng Hicks, Stephanie C. verfasserin aut quantro: a data-driven approach to guide the choice of an appropriate normalization method 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. Mean Square Error (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Global Difference (dpeaa)DE-He213 Relative Mean Square Error (dpeaa)DE-He213 Quantile Distribution (dpeaa)DE-He213 Irizarry, Rafael A. aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 04. Juni (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:04 month:06 https://dx.doi.org/10.1186/s13059-015-0679-0 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 04 06 |
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10.1186/s13059-015-0679-0 doi (DE-627)SPR03002319X (SPR)s13059-015-0679-0-e DE-627 ger DE-627 rakwb eng Hicks, Stephanie C. verfasserin aut quantro: a data-driven approach to guide the choice of an appropriate normalization method 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. Mean Square Error (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Global Difference (dpeaa)DE-He213 Relative Mean Square Error (dpeaa)DE-He213 Quantile Distribution (dpeaa)DE-He213 Irizarry, Rafael A. aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 04. Juni (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:04 month:06 https://dx.doi.org/10.1186/s13059-015-0679-0 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 04 06 |
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10.1186/s13059-015-0679-0 doi (DE-627)SPR03002319X (SPR)s13059-015-0679-0-e DE-627 ger DE-627 rakwb eng Hicks, Stephanie C. verfasserin aut quantro: a data-driven approach to guide the choice of an appropriate normalization method 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. Mean Square Error (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Global Difference (dpeaa)DE-He213 Relative Mean Square Error (dpeaa)DE-He213 Quantile Distribution (dpeaa)DE-He213 Irizarry, Rafael A. aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 04. Juni (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:04 month:06 https://dx.doi.org/10.1186/s13059-015-0679-0 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 04 06 |
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10.1186/s13059-015-0679-0 doi (DE-627)SPR03002319X (SPR)s13059-015-0679-0-e DE-627 ger DE-627 rakwb eng Hicks, Stephanie C. verfasserin aut quantro: a data-driven approach to guide the choice of an appropriate normalization method 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. Mean Square Error (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Global Difference (dpeaa)DE-He213 Relative Mean Square Error (dpeaa)DE-He213 Quantile Distribution (dpeaa)DE-He213 Irizarry, Rafael A. aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 04. Juni (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:04 month:06 https://dx.doi.org/10.1186/s13059-015-0679-0 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 04 06 |
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10.1186/s13059-015-0679-0 doi (DE-627)SPR03002319X (SPR)s13059-015-0679-0-e DE-627 ger DE-627 rakwb eng Hicks, Stephanie C. verfasserin aut quantro: a data-driven approach to guide the choice of an appropriate normalization method 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. Mean Square Error (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Global Difference (dpeaa)DE-He213 Relative Mean Square Error (dpeaa)DE-He213 Quantile Distribution (dpeaa)DE-He213 Irizarry, Rafael A. aut Enthalten in Genome biology London : BioMed Central, 2000 16(2015), 1 vom: 04. Juni (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:16 year:2015 number:1 day:04 month:06 https://dx.doi.org/10.1186/s13059-015-0679-0 lizenzpflichtig 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_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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 04 06 |
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Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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Abstract Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html. © Hicks and Irizarry. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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