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Pre-processing Agilent microarray data.

Pre-processing Agilent microarray data. Research Abstract Details 

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  • Pre-processing Agilent microarray data. Abstract Text:

    marianna zahurakMarianna Zahurak,giovanni parmigianiGiovanni Parmigiani,wayne yuWayne Yu,robert b scharpfRobert B Scharpf,david bermanDavid Berman,edward schaefferEdward Schaeffer,shabana shabbeerShabana Shabbeer,leslie copeLeslie Cope,

    BACKGROUND: Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions. RESULTS: Simple loess normalization without background subtraction produced the lowest variability. However, without background subtraction, fold changes were biased towards zero, particularly at low intensities. ROC analysis of a spike-in experiment showed that differentially expressed genes are most reliably detected when background is not subtracted. Loess normalization and no background subtraction yielded an AUC of 99.7% compared with 88.8% for Agilent processed fold changes. All methods performed well when error was taken into account by t- or z-statistics, AUCs > or = 99.8%. A substantial proportion of genes showed dye effects, 43% (99% CI: 39%, 47%). However, these effects were generally small regardless of the pre-processing method. CONCLUSION: Simple loess normalization without background subtraction resulted in low variance fold changes that more reliably ranked gene expression than the other methods. While t-statistics and other measures that take variation into account, including Agilent's z-statistic, can also be used to reliably select differentially expressed genes, fold changes are a standard measure of differential expression for exploratory work, cross platform comparison, and biological interpretation and can not be entirely replaced. Although dye effects are small for most genes, many array features are affected. Therefore, an experimental design that incorporates dye swaps or a common reference could be valuable.

    Pre-processing Agilent microarray data. Publishing Authors By Initials

    m zahurakM Zahurak,g parmigianiG Parmigiani,w yuW Yu,rb scharpfRB Scharpf,d bermanD Berman,e schaefferE Schaeffer,s shabbeerS Shabbeer,l copeL Cope,

    For similar investigative techniques: chemistry, analytical: microchip analytical procedures: microarray analysis: oligonucleotide array sequence analysis research abstracts see: investigative techniques: chemistry, analytical: microchip analytical procedures: microarray analysis: oligonucleotide array sequence analysis research

    PUBMED ID PMID:

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    Pre-processing Agilent microarray data. Journal Published:

    PUBLICATION TYPE: Research Support, U.S. Gov't,

    Journal: BMC bioinformatics

    VOLUME: 8

    Page Numbers: 142

    Journal Abbreviation: BMC Bioinformatics

    ISSN: 1471-2105

    DAY: 1

    MONTH: 05

    YEAR: 2007

    Pre-processing Agilent microarray data. Information

    Number of References:

    LANGUAGE: eng

    NlmUniqueID: 100965194

    Pre-processing Agilent microarray data. Keywords Mesh Terms:

    KEYWORDS: Oligonucleotide Array Sequence Analysis

    MESH TERMS: statistics & numerical data

    Chemical & Substance for Abstract: Pre-processing Agilent microarray data. Information

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    Grant and Affiliation Information for Pre-processing Agilent microarray data.

    AFFILIATION: Johns Hopkins University School of Medicine, Oncology Biostatistics, Baltimore, MD 21205, USA. zahurma@jhmi.edu

    Country: England

    England Research PublicationEngland Research Publication

    AGENCY: United States NCI

    GRANT: P30 CA06973-44

    ACRONYM: CA

    MEDLINETA: BMC Bioinformatics

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