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Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #302697 |
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Title:
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Mining Illumina Chip Time Course Microarray Data: Assessment of Normalization Methods and Pattern-Based Clustering of Genes Using Contrast Analysis
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Author(s):
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Suman Duvvuru*+
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Companies:
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The University of Tennessee, Knoxville
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Address:
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3700 sutherland avenue apt k2, Knoxville, TN, 37919,
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Keywords:
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normalization ; clustering ; contrast
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Abstract:
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Statistical genomics is subject to challenges of microarray data analysis including appropriateness of normalization and assessment of statistical significance in a context of massive correlation among statistical tests. Here we present a toolchain for the analysis of time course microarray data which involves two steps: i) Preprocessing of the data: Selection of best normalization method and ii) clustering based on polynomial and Helmert contrast analysis. Several normalization methods such as background correction, average normalization, rank invariant, quantile and cubic spline normalization have been compared and assessed using intraclass correlation. Intraclass correlation is the amount of variance among the different time points relative to total variance. Once the approppriate normalization method has been chosen,contrast analysis is performed to explore the patterns across time.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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