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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 #317025
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Title:
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Differential Abundance Analysis of Microbiomes with Bias Correction
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Author(s):
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Shyamal Peddada*
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Companies:
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The Eunice Kennedy Shriver National Institute of Child Health and Human Development
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Keywords:
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Abstract:
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Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this talk we describe a recent methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) [Lin, Peddada, Nature Comm. 2020], which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The abundance data are modeled using a linear regression framework. This formulation (a) provides a statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR) under appropriate conditions while maintaining high power, and (d) is computationally simple to implement.
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Authors who are presenting talks have a * after their name.
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