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Activity Number:
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144
- Biases, Batch Effects, and Novel Statistical Methodologies: Handling Them in Large-Scale Microbiome Sequencing Studies
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Type:
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Invited
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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ENAR
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Abstract #317024
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Title:
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Bias-Robust Analysis of Microbiome Data
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Author(s):
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Glen Satten*
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Companies:
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Emory University
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Keywords:
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Abstract:
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It is known that count data in a feature table for microbiome data are subject to a wide variety of biases. Further, there is no currently-available method of standardization or normalization of microbime data that can remove or account for these biases. A recent advance is the proposal that microbiome bias is multiplicative, acting separately on each taxon or feature. This model is useful because it allows us to identify a variety of models which are invariant to bias; use of one of these models can thus identify 'true' parameters that are not affected by bias. In this talk we discuss several models in this class, and show their strengths and weaknesses using real and simulated data.
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Authors who are presenting talks have a * after their name.
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