Analysis of compositions of microbiomes with bias correction
By Huang Lin, Shyamal Das Peddada. Nature Communications, 2020
1,770 citations recorded by the source corpus.
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. Differential abundance analysis of microbiome data continues to be challenging due to data complexity. The authors propose a method which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
Subfields: Food Science, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ca0b687b7bab17edfab161f07439a2eadd6721b2
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In Food Science
- Benjamin Schubert
- Jun Chen
- Xianyang Zhang
- Huijuan Zhou
- Christian L. Müller
- Fabian J Theis
- Maren Büttner
- Kejun He
In Molecular Biology
- Fabian J Theis
- Maren Büttner
- Benjamin Schubert
- Jun Chen
- Xianyang Zhang
- Huijuan Zhou
- Christian L. Müller
- Kejun He
Citations in the corpus
- scCODA is a Bayesian model for compositional single-cell data analysis (2021, methodology): “Other compositional non-Bayesian models such as ANCOM-BC 13 , ANCOM 14 , ALDEx2 15 , and additive log-ratio (ALR) transformed proportions combined with a t test (Methods —“ Model comparison ” ) showed similar behavior, albeit with lower MCC. Non-compositional models, such as the Beta-Binomial model…”
- LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, methodology): “Numerous differential abundance analysis methods have been proposed focusing on addressing the compositional effects [13, 15–17, 19, 20, 22– 24].”