Dataset Information

Multiscale adaptive differential abundance analysis in microbial compositional data.

ABSTRACT:

Motivation

Differential abundance analysis is an essential and commonly used tool to characterize the difference between microbial communities. However, identifying differentially abundant microbes remains a challenging problem because the observed microbiome data are inherently compositional, excessive sparse, and distorted by experimental bias. Besides these major challenges, the results of differential abundance analysis also depend largely on the choice of analysis unit, adding another practical complexity to this already complicated problem.

Results

In this work, we introduce a new differential abundance test called the MsRDB test, which embeds the sequences into a metric space and integrates a multiscale adaptive strategy for utilizing spatial structure to identify differentially abundant microbes. Compared with existing methods, the MsRDB test can detect differentially abundant microbes at the finest resolution offered by data and provide adequate detection power while being robust to zero counts, compositional effect, and experimental bias in the microbial compositional dataset. Applications to both simulated and real microbial compositional datasets demonstrate the usefulness of the MsRDB test.

Availability and implementation

All analyses can be found under https://github.com/lakerwsl/MsRDB-Manuscript-Code.

SUBMITTER: Wang S

PROVIDER: S-EPMC10112958 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Multiscale adaptive differential abundance analysis in microbial compositional data.

Wang Shulei S

Bioinformatics (Oxford, England) 20230401 4

<h4>Motivation</h4>Differential abundance analysis is an essential and commonly used tool to characterize the difference between microbial communities. However, identifying differentially abundant microbes remains a challenging problem because the observed microbiome data are inherently compositional, excessive sparse, and distorted by experimental bias. Besides these major challenges, the results of differential abundance analysis also depend largely on the choice of analysis unit, adding anoth ...[more]

PMID: 37018137

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Project description:MotivationMicrobial communities have been shown to be associated with many complex diseases, such as cancers and cardiovascular diseases. The identification of differentially abundant taxa is clinically important. It can help understand the pathology of complex diseases, and potentially provide preventive and therapeutic strategies. Appropriate differential analyses for microbiome data are challenging due to its unique data characteristics including compositional constraint, excessive zeros and high dimensionality. Most existing approaches either ignore these data characteristics or only account for the compositional constraint by using log-ratio transformations with zero observations replaced by a pseudocount. However, there is no consensus on how to choose a pseudocount. More importantly, ignoring the characteristic of excessive zeros may result in poorly powered analyses and therefore yield misleading findings.ResultsWe develop a novel microbiome-based direction-assisted test for the detection of overall difference in microbial relative abundances between two health conditions, which simultaneously incorporates the characteristics of relative abundance data. The proposed test (i) divides the taxa into two clusters by the directions of mean differences of relative abundances and then combines them at cluster level, in light of the compositional characteristic; and (ii) contains a burden type test, which collapses multiple taxa into a single one to account for excessive zeros. Moreover, the proposed test is an adaptive procedure, which can accommodate high-dimensional settings and yield high power against various alternative hypotheses. We perform extensive simulation studies across a wide range of scenarios to evaluate the proposed test and show its substantial power gain over some existing tests. The superiority of the proposed approach is further demonstrated with real datasets from two microbiome studies.Availability and implementationAn R package for MiDAT is available at https://github.com/zhangwei0125/MiDAT.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Multiscale adaptive differential abundance analysis in microbial compositional data.

Motivation

Results

Availability and implementation

Publications

Multiscale adaptive differential abundance analysis in microbial compositional data.

Similar Datasets

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