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Activity Number:
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223
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302004 |
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Title:
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Are Gene Locations Clustered on Chromosomes?
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Author(s):
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Naomi S. Altman*+ and Eli Walters and Laura Elnitski
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Companies:
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Pennsylvania State University and Pennsylvania State University and Pennsylvania State University
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Address:
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Dept. of Statistics, University Park, PA, 16802,
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Keywords:
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gene clustering ; goodness-of-fit ; chi-squared test ; Kolmogorov test ; Cramer-von Mises test ; bioinformatics
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Abstract:
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Genes that are more closely spaced on the chromosome than expected by chance are said to be spatially clustered. The arrangement of genes along the chromosome is known to be associated with gene expression, so understanding spatial clustering is important. Standard tests of clustering vs. uniformity do not take into account two important features of genes: the high variability of gene length and the low probability that gene locations overlap (exclusion). We show by simulation that the standard null distributions which ignore length and exclusion do not appropriately approximate the true null distributions of standard tests such as the chi-squared test. We therefore recommend bootstrap sampling to estimate the null distributions. Simulations demonstrate that the chi-squared goodness-of-fit test is a more powerful test of clustering than two other commonly used tests--Kolmogorov and Cramer-von Mises--when the distribution of gene lengths and locations is modeled by a mixture of exponentials and there is a single cluster. The bootstrap method to test clustering is illustrated using data from human chromosome 22.
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