Missing value imputation strategies for metabolomics data
The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Armitage, Emily Grace [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Electrophoresis - Weinheim : Wiley-VCH, 1980, 36(2015), 24, Seite 3050-3060 |
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| Übergeordnetes Werk: |
volume:36 ; year:2015 ; number:24 ; pages:3050-3060 |
| Links: |
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| DOI / URN: |
10.1002/elps.201500352 |
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| 520 | |a The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. | ||
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10.1002/elps.201500352 doi PQ20160617 (DE-627)OLC1958967114 (DE-599)GBVOLC1958967114 (PRQ)p927-b5b5a994d8613c28550d7e445dce004084555a05a4eb5508265a49738119ef863 (KEY)0204026320150000036002403050missingvalueimputationstrategiesformetabolomicsdat DE-627 ger DE-627 rakwb eng 540 570 DNB 570 AVZ BIODIV fid 35.29 bkl Armitage, Emily Grace verfasserin aut Missing value imputation strategies for metabolomics data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. Nutzungsrecht: © 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. CE‐MS Data False‐discovery rate k‐nearest neighbour Imputation Missing values Metabolomics Godzien, Joanna oth Alonso‐Herranz, Vanesa oth López‐Gonzálvez, Ángeles oth Barbas, Coral oth Enthalten in Electrophoresis Weinheim : Wiley-VCH, 1980 36(2015), 24, Seite 3050-3060 (DE-627)130409952 (DE-600)619001-7 (DE-576)015913732 0173-0835 nnns volume:36 year:2015 number:24 pages:3050-3060 http://dx.doi.org/10.1002/elps.201500352 Volltext http://onlinelibrary.wiley.com/doi/10.1002/elps.201500352/abstract http://www.ncbi.nlm.nih.gov/pubmed/26376450 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2219 GBV_ILN_4012 35.29 AVZ AR 36 2015 24 3050-3060 |
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10.1002/elps.201500352 doi PQ20160617 (DE-627)OLC1958967114 (DE-599)GBVOLC1958967114 (PRQ)p927-b5b5a994d8613c28550d7e445dce004084555a05a4eb5508265a49738119ef863 (KEY)0204026320150000036002403050missingvalueimputationstrategiesformetabolomicsdat DE-627 ger DE-627 rakwb eng 540 570 DNB 570 AVZ BIODIV fid 35.29 bkl Armitage, Emily Grace verfasserin aut Missing value imputation strategies for metabolomics data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. Nutzungsrecht: © 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. CE‐MS Data False‐discovery rate k‐nearest neighbour Imputation Missing values Metabolomics Godzien, Joanna oth Alonso‐Herranz, Vanesa oth López‐Gonzálvez, Ángeles oth Barbas, Coral oth Enthalten in Electrophoresis Weinheim : Wiley-VCH, 1980 36(2015), 24, Seite 3050-3060 (DE-627)130409952 (DE-600)619001-7 (DE-576)015913732 0173-0835 nnns volume:36 year:2015 number:24 pages:3050-3060 http://dx.doi.org/10.1002/elps.201500352 Volltext http://onlinelibrary.wiley.com/doi/10.1002/elps.201500352/abstract http://www.ncbi.nlm.nih.gov/pubmed/26376450 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2219 GBV_ILN_4012 35.29 AVZ AR 36 2015 24 3050-3060 |
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10.1002/elps.201500352 doi PQ20160617 (DE-627)OLC1958967114 (DE-599)GBVOLC1958967114 (PRQ)p927-b5b5a994d8613c28550d7e445dce004084555a05a4eb5508265a49738119ef863 (KEY)0204026320150000036002403050missingvalueimputationstrategiesformetabolomicsdat DE-627 ger DE-627 rakwb eng 540 570 DNB 570 AVZ BIODIV fid 35.29 bkl Armitage, Emily Grace verfasserin aut Missing value imputation strategies for metabolomics data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. Nutzungsrecht: © 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. CE‐MS Data False‐discovery rate k‐nearest neighbour Imputation Missing values Metabolomics Godzien, Joanna oth Alonso‐Herranz, Vanesa oth López‐Gonzálvez, Ángeles oth Barbas, Coral oth Enthalten in Electrophoresis Weinheim : Wiley-VCH, 1980 36(2015), 24, Seite 3050-3060 (DE-627)130409952 (DE-600)619001-7 (DE-576)015913732 0173-0835 nnns volume:36 year:2015 number:24 pages:3050-3060 http://dx.doi.org/10.1002/elps.201500352 Volltext http://onlinelibrary.wiley.com/doi/10.1002/elps.201500352/abstract http://www.ncbi.nlm.nih.gov/pubmed/26376450 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_2219 GBV_ILN_4012 35.29 AVZ AR 36 2015 24 3050-3060 |
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Missing value imputation strategies for metabolomics data |
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The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. |
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The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. |
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The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros. |
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Missing value imputation strategies for metabolomics data |
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