Exploration, normalization, and summaries of high density oligonucleotide array probe level data

Exploration, normalization, and summaries of high density oligonucleotide array probe level data In this paper we report exploratory analyses of high‐density oligonucleotide array data from the Affymetrix GeneChip ® system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip ® arrays, part of the data from an extensive spike‐in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG‐U95A human GeneChip ® arrays; and part of a dilution study conducted by Gene Logic involving 75 HG‐U95A GeneChip ® arrays. We display some familiar features of the perfect match and mismatch probe ( PM and MM ) values of these data, and examine the variance–mean relationship with probe‐level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike‐in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model‐based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi‐array average (RMA) of background‐adjusted, normalized, and log‐transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike‐in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe‐specific affinities. Copyright Oxford University Press 2003 « Previous | Next Article » Table of Contents This Article Biostat (2003) 4 (2): 249-264. doi: 10.1093/biostatistics/4.2.249 » Abstract Free Full Text (PDF) Free Classifications Article Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Similar articles in PubMed Add to my archive Download citation Request Permissions Disclaimer Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Irizarry, R. A. Articles by Speed, T. P. Search for related content PubMed PubMed citation Articles by Irizarry, R. A. Articles by Hobbs, B. Articles by Collin, F. Articles by Beazer‐Barclay, Y. D. Articles by Antonellis, K. J. Articles by Scherf, U. Articles by Speed, T. P. Related Content Load related web page information Share Email this article CiteULike Delicious Facebook Google+ Mendeley Twitter What's this? Search this journal: Advanced » Current Issue October 2015 16 (4) Alert me to new issues The Journal About this journal Rights & Permissions Dispatch date of the next issue We are mobile – find out more Journals Career Network Impact factor: 2.649 5-Yr impact factor: 2.853 Co-Editors Geert Molenberghs Anastasios Tsiatis View full editorial board For Authors Instructions to authors Submit online now! Self archiving policy Open access options for authors - visit Oxford Open This journal enables compliance with the NIH Public Access Policy Alerting Services Email table of contents Email Advance Access CiteTrack XML RSS feed Corporate Services Advertising sales Reprints Supplements Widget Get a widget var taxonomies = ("SCI01780"); Most Most Read Adjusting batch effects in microarray expression data using empirical Bayes methods Reproducible research and Biostatistics Exploration, normalization, and summaries of high density oligonucleotide array probe level data Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses Sparse inverse covariance estimation with the graphical lasso » View all Most Read articles Most Cited Exploration, normalization, and summaries of high density oligonucleotide array probe level data Circular binary segmentation for the analysis of array-based DNA copy number data Adjusting batch effects in microarray expression data using empirical Bayes methods Sparse inverse covariance estimation with the graphical lasso Small-sample estimation of negative binomial dispersion, with applications to SAGE data » View all Most Cited articles Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department. Online ISSN 1468-4357 - Print ISSN 1465-4644 Copyright © 2015 Oxford University Press Oxford Journals Oxford University Press Site Map Privacy Policy Cookie Policy Legal Notices Frequently Asked Questions Other Oxford University Press sites: Oxford University Press Oxford Journals China Oxford Journals Japan Academic & Professional books Children's & Schools Books Dictionaries & Reference Dictionary of National Biography Digital Reference English Language Teaching Higher Education Textbooks International Education Unit Law Medicine Music Online Products & Publishing Oxford Bibliographies Online Oxford Dictionaries Online Oxford English Dictionary Oxford Language Dictionaries Online Oxford Scholarship Online Reference Rights and Permissions Resources for Retailers & Wholesalers Resources for the Healthcare Industry Very Short Introductions World's Classics function fnc_onDomLoaded() { var query_context = getQueryContext(); PF_initOIUnderbar(query_context,":QS:default","","JRN"); PF_insertOIUnderbar(0); }; if (window.addEventListener) { window.addEventListener('load', fnc_onDomLoaded, false); } else if (window.attachEvent) { window.attachEvent('onload', fnc_onDomLoaded); } var gaJsHost = (("https:" == document.location.protocol) ? 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Exploration, normalization, and summaries of high density oligonucleotide array probe level data

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Publisher
Oxford University Press
Copyright
Copyright © 2015 Oxford University Press
ISSN
1465-4644
eISSN
1468-4357
D.O.I.
10.1093/biostatistics/4.2.249
Publisher site
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Abstract

In this paper we report exploratory analyses of high‐density oligonucleotide array data from the Affymetrix GeneChip ® system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip ® arrays, part of the data from an extensive spike‐in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG‐U95A human GeneChip ® arrays; and part of a dilution study conducted by Gene Logic involving 75 HG‐U95A GeneChip ® arrays. We display some familiar features of the perfect match and mismatch probe ( PM and MM ) values of these data, and examine the variance–mean relationship with probe‐level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike‐in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model‐based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi‐array average (RMA) of background‐adjusted, normalized, and log‐transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike‐in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe‐specific affinities. Copyright Oxford University Press 2003 « Previous | Next Article » Table of Contents This Article Biostat (2003) 4 (2): 249-264. doi: 10.1093/biostatistics/4.2.249 » Abstract Free Full Text (PDF) Free Classifications Article Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Similar articles in PubMed Add to my archive Download citation Request Permissions Disclaimer Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Irizarry, R. A. Articles by Speed, T. P. Search for related content PubMed PubMed citation Articles by Irizarry, R. A. Articles by Hobbs, B. Articles by Collin, F. Articles by Beazer‐Barclay, Y. D. Articles by Antonellis, K. J. Articles by Scherf, U. Articles by Speed, T. P. Related Content Load related web page information Share Email this article CiteULike Delicious Facebook Google+ Mendeley Twitter What's this? Search this journal: Advanced » Current Issue October 2015 16 (4) Alert me to new issues The Journal About this journal Rights & Permissions Dispatch date of the next issue We are mobile – find out more Journals Career Network Impact factor: 2.649 5-Yr impact factor: 2.853 Co-Editors Geert Molenberghs Anastasios Tsiatis View full editorial board For Authors Instructions to authors Submit online now! Self archiving policy Open access options for authors - visit Oxford Open This journal enables compliance with the NIH Public Access Policy Alerting Services Email table of contents Email Advance Access CiteTrack XML RSS feed Corporate Services Advertising sales Reprints Supplements Widget Get a widget var taxonomies = ("SCI01780"); Most Most Read Adjusting batch effects in microarray expression data using empirical Bayes methods Reproducible research and Biostatistics Exploration, normalization, and summaries of high density oligonucleotide array probe level data Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses Sparse inverse covariance estimation with the graphical lasso » View all Most Read articles Most Cited Exploration, normalization, and summaries of high density oligonucleotide array probe level data Circular binary segmentation for the analysis of array-based DNA copy number data Adjusting batch effects in microarray expression data using empirical Bayes methods Sparse inverse covariance estimation with the graphical lasso Small-sample estimation of negative binomial dispersion, with applications to SAGE data » View all Most Cited articles Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department. Online ISSN 1468-4357 - Print ISSN 1465-4644 Copyright © 2015 Oxford University Press Oxford Journals Oxford University Press Site Map Privacy Policy Cookie Policy Legal Notices Frequently Asked Questions Other Oxford University Press sites: Oxford University Press Oxford Journals China Oxford Journals Japan Academic & Professional books Children's & Schools Books Dictionaries & Reference Dictionary of National Biography Digital Reference English Language Teaching Higher Education Textbooks International Education Unit Law Medicine Music Online Products & Publishing Oxford Bibliographies Online Oxford Dictionaries Online Oxford English Dictionary Oxford Language Dictionaries Online Oxford Scholarship Online Reference Rights and Permissions Resources for Retailers & Wholesalers Resources for the Healthcare Industry Very Short Introductions World's Classics function fnc_onDomLoaded() { var query_context = getQueryContext(); PF_initOIUnderbar(query_context,":QS:default","","JRN"); PF_insertOIUnderbar(0); }; if (window.addEventListener) { window.addEventListener('load', fnc_onDomLoaded, false); } else if (window.attachEvent) { window.attachEvent('onload', fnc_onDomLoaded); } var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-189672-16"); pageTracker._setDomainName(".oxfordjournals.org"); pageTracker._trackPageview(); } catch(err) {}

Journal

BiostatisticsOxford University Press

Published: Apr 1, 2003

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