An imputation approach for oligonucleotide microarrays.

Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various s...

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Autores principales:	Ming Li, Yalu Wen, Qing Lu, Wenjiang J Fu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b2a869e724854f8684361449782d4175
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id	oai:doaj.org-article:b2a869e724854f8684361449782d4175
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spelling	oai:doaj.org-article:b2a869e724854f8684361449782d41752021-11-18T07:54:19ZAn imputation approach for oligonucleotide microarrays.1932-620310.1371/journal.pone.0058677https://doaj.org/article/b2a869e724854f8684361449782d41752013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23505547/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various sources, observed as "bright spots", "dark clouds", and "shadowy circles", etc. It is crucial that these image defects are correctly identified and properly processed. Existing approaches mainly focus on detecting defect areas and removing affected intensities. In this article, we propose to use a mixed effect model for imputing the affected intensities. The proposed imputation procedure is a single-array-based approach which does not require any biological replicate or between-array normalization. We further examine its performance by using Affymetrix high-density SNP arrays. The results show that this imputation procedure significantly reduces genotyping error rates. We also discuss the necessary adjustments for its potential extension to other oligonucleotide microarrays, such as gene expression profiling. The R source code for the implementation of approach is freely available upon request.Ming LiYalu WenQing LuWenjiang J FuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e58677 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ming Li Yalu Wen Qing Lu Wenjiang J Fu An imputation approach for oligonucleotide microarrays.
description	Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various sources, observed as "bright spots", "dark clouds", and "shadowy circles", etc. It is crucial that these image defects are correctly identified and properly processed. Existing approaches mainly focus on detecting defect areas and removing affected intensities. In this article, we propose to use a mixed effect model for imputing the affected intensities. The proposed imputation procedure is a single-array-based approach which does not require any biological replicate or between-array normalization. We further examine its performance by using Affymetrix high-density SNP arrays. The results show that this imputation procedure significantly reduces genotyping error rates. We also discuss the necessary adjustments for its potential extension to other oligonucleotide microarrays, such as gene expression profiling. The R source code for the implementation of approach is freely available upon request.
format	article
author	Ming Li Yalu Wen Qing Lu Wenjiang J Fu
author_facet	Ming Li Yalu Wen Qing Lu Wenjiang J Fu
author_sort	Ming Li
title	An imputation approach for oligonucleotide microarrays.
title_short	An imputation approach for oligonucleotide microarrays.
title_full	An imputation approach for oligonucleotide microarrays.
title_fullStr	An imputation approach for oligonucleotide microarrays.
title_full_unstemmed	An imputation approach for oligonucleotide microarrays.
title_sort	imputation approach for oligonucleotide microarrays.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/b2a869e724854f8684361449782d4175
work_keys_str_mv	AT mingli animputationapproachforoligonucleotidemicroarrays AT yaluwen animputationapproachforoligonucleotidemicroarrays AT qinglu animputationapproachforoligonucleotidemicroarrays AT wenjiangjfu animputationapproachforoligonucleotidemicroarrays AT mingli imputationapproachforoligonucleotidemicroarrays AT yaluwen imputationapproachforoligonucleotidemicroarrays AT qinglu imputationapproachforoligonucleotidemicroarrays AT wenjiangjfu imputationapproachforoligonucleotidemicroarrays
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An imputation approach for oligonucleotide microarrays.

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