Analysis of compositions of microbiomes with bias correction View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-07-14

AUTHORS

Huang Lin, Shyamal Das Peddada

ABSTRACT

Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of “sampling fraction” and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement. More... »

PAGES

3514

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-020-17041-7

DOI

http://dx.doi.org/10.1038/s41467-020-17041-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1129323965

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32665548


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