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Activity Number:
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573
- Biometrics Student Paper Awards 2
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #322658
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Title:
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A Fast Small-Sample Kernel Independence Test for Microbiome Community-Level Association Analysis
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Author(s):
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Xiang Zhan* and Anna Plantinga and Ni Zhao and Michael C. Wu
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Companies:
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Fred Hutchinson Cancer Research Center and University of Washington and Johns Hopkins University and Fred Hutchinson Cancer Research Center
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Keywords:
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KRV coefficient ;
Microbiome composition ;
Multivariate association ;
Statistical independence
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Abstract:
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To fully understand the role of microbiome in human health and diseases, researchers are increasingly interested in assessing the relationship between microbiome composition and host genomic data. The dimensionality of the data as well as complex relationships between microbiota and host genomic data pose considerable challenges for analysis. In this paper, we propose a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition. The KRV statistic can capture non-linear correlations and complex relationships among the individual data types through measuring general dependency. Testing proceeds via a fast permutation technique which allows for rapid p-value calculation. Simulation studies show that KRV can powerfully identify existing associations between microbiome composition and host genomic data while protecting type I type I error. We apply the KRV to a microbiome study examining the relationship between host transcriptome and mucosal microbiome composition within the context of inflammatory bowel disease and are able to derive new biological insights and provide formal inference on prior qualitative observation.
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Authors who are presenting talks have a * after their name.
