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Abstract Details
Activity Number:
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359
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #302339 |
Title:
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Statistical Analysis of Taxonomic Trees in Microbiome Research
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Author(s):
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William Shannon*+ and Patricio La Rosa and Elena Deych and Yanjiao Zhou and George Weinstock and Erica Sodergren and Berkley Shands
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Companies:
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Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine
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Address:
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660 S Euclid Ave, Box 8005, St Louis, MO, 63110,
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Keywords:
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Object data analysis ;
taxonomic trees ;
microbiome ;
genetic sequencing ;
applied biostatistics
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Abstract:
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Human microbiome research uses next generation sequencing to characterize the microbial content from human samples to begin to learn how interactions between bacteria and their human host might impact health. As an emerging medical research area there are few formal methods for designing and analyzing these experiments, with most approaches being ad hoc and applicable to the particular problem being faced. Since microbiome samples can be represented as taxonomic trees, it is natural to consider statistical methods which operate on graphical structures such as tree objects. A unimodal probability model for graph-valued random objects has been derived and applied to several types of graphs (cluster trees, digraphs, and classification trees). In this work we apply this model to HMP taxonomic trees which allows for a fully statistical data analysis. This model allows us to calculate core microbiomes using statistical maximum likelihood estimation, test hypotheses and calculate P values of whether the core microbiomes are the same or different across patient subgroups using likelihood ratio tests. As an example, we apply our methodology to the HMP data on 24 subjects.
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