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Activity Number:
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285
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #308917 |
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Title:
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Statistical Differential Analysis of 2-D Gel Images
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Author(s):
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Feng Li*+ and Francoise Seillier-Moiseiwitsch
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Companies:
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University of Maryland, Baltimore County and Georgetown University Medical Center
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Address:
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4702 Gateway Terrace, Baltimore, MD, 21227,
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Keywords:
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2-D Gel ; image analysis ; multiple comparison ; FDR ; empirical Bayes
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Abstract:
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s2-D Gel is the most powerful protein separation technique. Statistical differential analysis of 2-D Gel images is important and necessary but it is in its infancy. To do statistical differential analysis of 2-D Gel images, the following procedure is used. The 2-D Gel images must be well aligned first. Watershed algorithm was applied to selected 2-D gel images to build a common watershed region for all images. Each watershed region was classified into background and object parts. The difference between the means of the object part and background part was used as a summary statistics for each watershed region. ANOVA method was applied to each watershed region to get a p-value. To account for multiple hypothesis testing problem, FDR procedure and empirical Bayes method were used to detect meaningful results. The application of this procedure to a set of gel images showed the effectiveness.
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