A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets
Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Brägelmann, Johannes [verfasserIn] Lorenzo Bermejo, Justo - 1972- [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Anmerkung: |
Published: 06 August 2018 Gesehen am 21.04.2020 |
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| Umfang: |
11 |
| Übergeordnetes Werk: |
Enthalten in: Briefings in bioinformatics - Oxford [u.a.] : Oxford University Press, 2000, 20(2019), 6, Seite 2055-2065 |
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| Übergeordnetes Werk: |
volume:20 ; year:2019 ; number:6 ; pages:2055-2065 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1093/bib/bby068 |
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| Katalog-ID: |
1695277171 |
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| 245 | 1 | 2 | |a A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets |c Johannes Brägelmann and Justo Lorenzo Bermejo |
| 264 | 1 | |c 2019 | |
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| 500 | |a Gesehen am 21.04.2020 | ||
| 520 | |a Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. | ||
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10.1093/bib/bby068 doi (DE-627)1695277171 (DE-599)KXP1695277171 (OCoLC)1341315952 DE-627 ger DE-627 rda eng Brägelmann, Johannes verfasserin (DE-588)1053647247 (DE-627)790416050 (DE-576)409599506 aut A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets Johannes Brägelmann and Justo Lorenzo Bermejo 2019 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Published: 06 August 2018 Gesehen am 21.04.2020 Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. 2018 Lorenzo Bermejo, Justo 1972- verfasserin (DE-588)124754619 (DE-627)706705572 (DE-576)294483632 aut Enthalten in Briefings in bioinformatics Oxford [u.a.] : Oxford University Press, 2000 20(2019), 6, Seite 2055-2065 Online-Ressource (DE-627)325359237 (DE-600)2036055-1 (DE-576)099210959 1477-4054 nnns volume:20 year:2019 number:6 pages:2055-2065 extent:11 https://doi.org/10.1093/bib/bby068 Verlag Resolving-System Volltext https://academic.oup.com/bib/article/20/6/2055/5066710 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 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_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 20 2019 6 2055-2065 11 2013 01 DE-16-250 3627741242 00 --%%-- --%%-- --%%-- --%%-- l01 29-07-19 2013 01 DE-16-250 00 s hd2019 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_2 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1462354653 Lorenzo Bermejo, Justo 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 |
| spelling |
10.1093/bib/bby068 doi (DE-627)1695277171 (DE-599)KXP1695277171 (OCoLC)1341315952 DE-627 ger DE-627 rda eng Brägelmann, Johannes verfasserin (DE-588)1053647247 (DE-627)790416050 (DE-576)409599506 aut A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets Johannes Brägelmann and Justo Lorenzo Bermejo 2019 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Published: 06 August 2018 Gesehen am 21.04.2020 Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. 2018 Lorenzo Bermejo, Justo 1972- verfasserin (DE-588)124754619 (DE-627)706705572 (DE-576)294483632 aut Enthalten in Briefings in bioinformatics Oxford [u.a.] : Oxford University Press, 2000 20(2019), 6, Seite 2055-2065 Online-Ressource (DE-627)325359237 (DE-600)2036055-1 (DE-576)099210959 1477-4054 nnns volume:20 year:2019 number:6 pages:2055-2065 extent:11 https://doi.org/10.1093/bib/bby068 Verlag Resolving-System Volltext https://academic.oup.com/bib/article/20/6/2055/5066710 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 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_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 20 2019 6 2055-2065 11 2013 01 DE-16-250 3627741242 00 --%%-- --%%-- --%%-- --%%-- l01 29-07-19 2013 01 DE-16-250 00 s hd2019 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_2 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1462354653 Lorenzo Bermejo, Justo 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 |
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10.1093/bib/bby068 doi (DE-627)1695277171 (DE-599)KXP1695277171 (OCoLC)1341315952 DE-627 ger DE-627 rda eng Brägelmann, Johannes verfasserin (DE-588)1053647247 (DE-627)790416050 (DE-576)409599506 aut A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets Johannes Brägelmann and Justo Lorenzo Bermejo 2019 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Published: 06 August 2018 Gesehen am 21.04.2020 Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. 2018 Lorenzo Bermejo, Justo 1972- verfasserin (DE-588)124754619 (DE-627)706705572 (DE-576)294483632 aut Enthalten in Briefings in bioinformatics Oxford [u.a.] : Oxford University Press, 2000 20(2019), 6, Seite 2055-2065 Online-Ressource (DE-627)325359237 (DE-600)2036055-1 (DE-576)099210959 1477-4054 nnns volume:20 year:2019 number:6 pages:2055-2065 extent:11 https://doi.org/10.1093/bib/bby068 Verlag Resolving-System Volltext https://academic.oup.com/bib/article/20/6/2055/5066710 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 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_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 20 2019 6 2055-2065 11 2013 01 DE-16-250 3627741242 00 --%%-- --%%-- --%%-- --%%-- l01 29-07-19 2013 01 DE-16-250 00 s hd2019 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_2 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1462354653 Lorenzo Bermejo, Justo 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 |
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10.1093/bib/bby068 doi (DE-627)1695277171 (DE-599)KXP1695277171 (OCoLC)1341315952 DE-627 ger DE-627 rda eng Brägelmann, Johannes verfasserin (DE-588)1053647247 (DE-627)790416050 (DE-576)409599506 aut A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets Johannes Brägelmann and Justo Lorenzo Bermejo 2019 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Published: 06 August 2018 Gesehen am 21.04.2020 Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. 2018 Lorenzo Bermejo, Justo 1972- verfasserin (DE-588)124754619 (DE-627)706705572 (DE-576)294483632 aut Enthalten in Briefings in bioinformatics Oxford [u.a.] : Oxford University Press, 2000 20(2019), 6, Seite 2055-2065 Online-Ressource (DE-627)325359237 (DE-600)2036055-1 (DE-576)099210959 1477-4054 nnns volume:20 year:2019 number:6 pages:2055-2065 extent:11 https://doi.org/10.1093/bib/bby068 Verlag Resolving-System Volltext https://academic.oup.com/bib/article/20/6/2055/5066710 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 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_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 20 2019 6 2055-2065 11 2013 01 DE-16-250 3627741242 00 --%%-- --%%-- --%%-- --%%-- l01 29-07-19 2013 01 DE-16-250 00 s hd2019 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_2 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1462354653 Lorenzo Bermejo, Justo 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 |
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10.1093/bib/bby068 doi (DE-627)1695277171 (DE-599)KXP1695277171 (OCoLC)1341315952 DE-627 ger DE-627 rda eng Brägelmann, Johannes verfasserin (DE-588)1053647247 (DE-627)790416050 (DE-576)409599506 aut A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets Johannes Brägelmann and Justo Lorenzo Bermejo 2019 11 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Published: 06 August 2018 Gesehen am 21.04.2020 Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. 2018 Lorenzo Bermejo, Justo 1972- verfasserin (DE-588)124754619 (DE-627)706705572 (DE-576)294483632 aut Enthalten in Briefings in bioinformatics Oxford [u.a.] : Oxford University Press, 2000 20(2019), 6, Seite 2055-2065 Online-Ressource (DE-627)325359237 (DE-600)2036055-1 (DE-576)099210959 1477-4054 nnns volume:20 year:2019 number:6 pages:2055-2065 extent:11 https://doi.org/10.1093/bib/bby068 Verlag Resolving-System Volltext https://academic.oup.com/bib/article/20/6/2055/5066710 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 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_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 20 2019 6 2055-2065 11 2013 01 DE-16-250 3627741242 00 --%%-- --%%-- --%%-- --%%-- l01 29-07-19 2013 01 DE-16-250 00 s hd2019 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_2 2013 01 DE-16-250 03 s s_11 2013 01 DE-16-250 04 p (DE-627)1462354653 Lorenzo Bermejo, Justo 2013 01 DE-16-250 04 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_2 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1695277171</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230426133536.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200421r20192018xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1093/bib/bby068</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1695277171</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1695277171</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1341315952</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Brägelmann, Johannes</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1053647247</subfield><subfield code="0">(DE-627)790416050</subfield><subfield code="0">(DE-576)409599506</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets</subfield><subfield code="c">Johannes Brägelmann and Justo Lorenzo Bermejo</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Published: 06 August 2018</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Gesehen am 21.04.2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime.</subfield></datafield><datafield tag="534" ind1=" " ind2=" "><subfield code="c">2018</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lorenzo Bermejo, Justo</subfield><subfield code="d">1972-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)124754619</subfield><subfield code="0">(DE-627)706705572</subfield><subfield code="0">(DE-576)294483632</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Briefings in bioinformatics</subfield><subfield code="d">Oxford [u.a.] : Oxford University Press, 2000</subfield><subfield code="g">20(2019), 6, Seite 2055-2065</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)325359237</subfield><subfield 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A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets |
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A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets Johannes Brägelmann and Justo Lorenzo Bermejo |
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Brägelmann, Johannes |
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Briefings in bioinformatics |
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Brägelmann, Johannes |
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comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets |
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A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets |
| abstract |
Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. Published: 06 August 2018 Gesehen am 21.04.2020 |
| abstractGer |
Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. Published: 06 August 2018 Gesehen am 21.04.2020 |
| abstract_unstemmed |
Abstract: Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible rlationship between human disease and epigenetic variability. DNA samples fromperipheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylationdifferences related to a particular phenotype. Since information on the cell-type composition of the sample is generally notavailable and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-typeheterogeneity in EWAS.In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linearmixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variableanalysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied amultilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimatedmethylation differences according to major study characteristics.While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASherresulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-typeheterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results basedon real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher andSmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimatedmethylation differences and runtime. Published: 06 August 2018 Gesehen am 21.04.2020 |
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A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets |
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