Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale

Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for...
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Autor*in:	Olive D. Buhule [verfasserIn] Ryan L. Minster [verfasserIn] Nicola L. Hawley [verfasserIn] Mario eMedvedovic [verfasserIn] Guangyun eSun [verfasserIn] Satupaitea eViali [verfasserIn] Ranjan eDeka [verfasserIn] Stephen T. McGarvey [verfasserIn] Daniel E. Weeks [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	DNA Methylation Obesity epigenetics Study Design Array data Batch effects

Übergeordnetes Werk:	In: Frontiers in Genetics - Frontiers Media S.A., 2011, 5(2014)
Übergeordnetes Werk:	volume:5 ; year:2014

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fgene.2014.00354

Katalog-ID:	DOAJ022250484

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245	1	0	\|a Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale
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520			\|a Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects.
650		4	\|a DNA Methylation
650		4	\|a Obesity
650		4	\|a epigenetics
650		4	\|a Study Design
650		4	\|a Array data
650		4	\|a Batch effects
653		0	\|a Genetics
700	0		\|a Ryan L. Minster \|e verfasserin \|4 aut
700	0		\|a Nicola L. Hawley \|e verfasserin \|4 aut
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700	0		\|a Guangyun eSun \|e verfasserin \|4 aut
700	0		\|a Satupaitea eViali \|e verfasserin \|4 aut
700	0		\|a Ranjan eDeka \|e verfasserin \|4 aut
700	0		\|a Stephen T. McGarvey \|e verfasserin \|4 aut
700	0		\|a Daniel E. Weeks \|e verfasserin \|4 aut
700	0		\|a Daniel E. Weeks \|e verfasserin \|4 aut
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allfields	10.3389/fgene.2014.00354 doi (DE-627)DOAJ022250484 (DE-599)DOAJ6a679014a6c34f9496bdc1201f12da14 DE-627 ger DE-627 rakwb eng QH426-470 Olive D. Buhule verfasserin aut Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects. DNA Methylation Obesity epigenetics Study Design Array data Batch effects Genetics Ryan L. Minster verfasserin aut Nicola L. Hawley verfasserin aut Mario eMedvedovic verfasserin aut Guangyun eSun verfasserin aut Satupaitea eViali verfasserin aut Ranjan eDeka verfasserin aut Stephen T. McGarvey verfasserin aut Daniel E. Weeks verfasserin aut Daniel E. Weeks verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 5(2014) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:5 year:2014 https://doi.org/10.3389/fgene.2014.00354 kostenfrei https://doaj.org/article/6a679014a6c34f9496bdc1201f12da14 kostenfrei http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00354/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 5 2014
spelling	10.3389/fgene.2014.00354 doi (DE-627)DOAJ022250484 (DE-599)DOAJ6a679014a6c34f9496bdc1201f12da14 DE-627 ger DE-627 rakwb eng QH426-470 Olive D. Buhule verfasserin aut Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects. DNA Methylation Obesity epigenetics Study Design Array data Batch effects Genetics Ryan L. Minster verfasserin aut Nicola L. Hawley verfasserin aut Mario eMedvedovic verfasserin aut Guangyun eSun verfasserin aut Satupaitea eViali verfasserin aut Ranjan eDeka verfasserin aut Stephen T. McGarvey verfasserin aut Daniel E. Weeks verfasserin aut Daniel E. Weeks verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 5(2014) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:5 year:2014 https://doi.org/10.3389/fgene.2014.00354 kostenfrei https://doaj.org/article/6a679014a6c34f9496bdc1201f12da14 kostenfrei http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00354/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 5 2014
allfields_unstemmed	10.3389/fgene.2014.00354 doi (DE-627)DOAJ022250484 (DE-599)DOAJ6a679014a6c34f9496bdc1201f12da14 DE-627 ger DE-627 rakwb eng QH426-470 Olive D. Buhule verfasserin aut Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects. DNA Methylation Obesity epigenetics Study Design Array data Batch effects Genetics Ryan L. Minster verfasserin aut Nicola L. Hawley verfasserin aut Mario eMedvedovic verfasserin aut Guangyun eSun verfasserin aut Satupaitea eViali verfasserin aut Ranjan eDeka verfasserin aut Stephen T. McGarvey verfasserin aut Daniel E. Weeks verfasserin aut Daniel E. Weeks verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 5(2014) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:5 year:2014 https://doi.org/10.3389/fgene.2014.00354 kostenfrei https://doaj.org/article/6a679014a6c34f9496bdc1201f12da14 kostenfrei http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00354/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 5 2014
allfieldsGer	10.3389/fgene.2014.00354 doi (DE-627)DOAJ022250484 (DE-599)DOAJ6a679014a6c34f9496bdc1201f12da14 DE-627 ger DE-627 rakwb eng QH426-470 Olive D. Buhule verfasserin aut Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects. DNA Methylation Obesity epigenetics Study Design Array data Batch effects Genetics Ryan L. Minster verfasserin aut Nicola L. Hawley verfasserin aut Mario eMedvedovic verfasserin aut Guangyun eSun verfasserin aut Satupaitea eViali verfasserin aut Ranjan eDeka verfasserin aut Stephen T. McGarvey verfasserin aut Daniel E. Weeks verfasserin aut Daniel E. Weeks verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 5(2014) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:5 year:2014 https://doi.org/10.3389/fgene.2014.00354 kostenfrei https://doaj.org/article/6a679014a6c34f9496bdc1201f12da14 kostenfrei http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00354/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 5 2014
allfieldsSound	10.3389/fgene.2014.00354 doi (DE-627)DOAJ022250484 (DE-599)DOAJ6a679014a6c34f9496bdc1201f12da14 DE-627 ger DE-627 rakwb eng QH426-470 Olive D. Buhule verfasserin aut Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects. DNA Methylation Obesity epigenetics Study Design Array data Batch effects Genetics Ryan L. Minster verfasserin aut Nicola L. Hawley verfasserin aut Mario eMedvedovic verfasserin aut Guangyun eSun verfasserin aut Satupaitea eViali verfasserin aut Ranjan eDeka verfasserin aut Stephen T. McGarvey verfasserin aut Daniel E. Weeks verfasserin aut Daniel E. Weeks verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 5(2014) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:5 year:2014 https://doi.org/10.3389/fgene.2014.00354 kostenfrei https://doaj.org/article/6a679014a6c34f9496bdc1201f12da14 kostenfrei http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00354/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 5 2014
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Buhule</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</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="520" ind1=" " ind2=" "><subfield code="a">Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. 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callnumber-first	Q - Science
author	Olive D. Buhule
spellingShingle	Olive D. Buhule misc QH426-470 misc DNA Methylation misc Obesity misc epigenetics misc Study Design misc Array data misc Batch effects misc Genetics Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale
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topic_title	QH426-470 Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale DNA Methylation Obesity epigenetics Study Design Array data Batch effects
topic	misc QH426-470 misc DNA Methylation misc Obesity misc epigenetics misc Study Design misc Array data misc Batch effects misc Genetics
topic_unstemmed	misc QH426-470 misc DNA Methylation misc Obesity misc epigenetics misc Study Design misc Array data misc Batch effects misc Genetics
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title	Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale
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title_full	Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale
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author_browse	Olive D. Buhule Ryan L. Minster Nicola L. Hawley Mario eMedvedovic Guangyun eSun Satupaitea eViali Ranjan eDeka Stephen T. McGarvey Daniel E. Weeks
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title_sort	stratified randomization controls better for batch effects in 450k methylation analysis: a cautionary tale
callnumber	QH426-470
title_auth	Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale
abstract	Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects.
abstractGer	Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects.
abstract_unstemmed	Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects.
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title_short	Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale
url	https://doi.org/10.3389/fgene.2014.00354 https://doaj.org/article/6a679014a6c34f9496bdc1201f12da14 http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00354/full https://doaj.org/toc/1664-8021
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author2	Ryan L. Minster Nicola L. Hawley Mario eMedvedovic Guangyun eSun Satupaitea eViali Ranjan eDeka Stephen T. McGarvey Daniel E. Weeks
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up_date	2024-07-04T00:48:10.014Z
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To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. 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