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Testing for differential abundance in compositional counts data, with application to microbiome studies

https://doi.org/10.1214/22-aoas1607
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Tests for Differentiation in Gene Expression Using a Data‐Driven Order or Weights for Hypotheses
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PLS‐DA for compositional data with application to metabolomics
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Design of Observational Studies
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The Statistical Analysis of Compositional Data
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Multidimensional Scaling: I. Theory and Method
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High-throughput absolute quantification sequencing reveals the effect of different fertilizer applications on bacterial community in a tomato cultivated coastal saline soil
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Host-Associated Quantitative Abundance Profiling Reveals the Microbial Load Variation of Root Microbiome
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Quantitative microbiome profiling links gut community variation to microbial load
inferredcites →https://doi.org/10.1038/nmeth.2658openalex-snapshot
Differential abundance analysis for microbial marker-gene surveys
inferredcites →https://doi.org/10.1038/nmeth.3869openalex-snapshot
DADA2: High-resolution sample inference from Illumina amplicon data
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A general and flexible method for signal extraction from single-cell RNA-seq data
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Establishing microbial composition measurement standards with reference frames
inferredcites →https://doi.org/10.1038/s41564-019-0483-9openalex-snapshot
Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across PSC/IBD diagnoses
inferredcites →https://doi.org/10.1080/00949655.2019.1677659openalex-snapshot
Hypothesis testing for two population means: parametric or non-parametric test?
inferredcites →https://doi.org/10.1093/bib/bbx104openalex-snapshot
A broken promise: microbiome differential abundance methods do not control the false discovery rate
inferredcites →https://doi.org/10.1093/biomet/73.3.751openalex-snapshot
An Improved Bonferroni Procedure for Multiple Tests of Significance
inferredcites →https://doi.org/10.1093/biomet/ass070openalex-snapshot
A consistent multivariate test of association based on ranks of distances
inferredcites →https://doi.org/10.1093/gigascience/giz107openalex-snapshot
A field guide for the compositional analysis of any-omics data
inferredcites →https://doi.org/10.1101/gr.085464.108openalex-snapshot
Microbial community profiling for human microbiome projects: Tools, techniques, and challenges
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A new method for non‐parametric multivariate analysis of variance
inferredcites →https://doi.org/10.1111/j.2517-6161.1982.tb01195.xopenalex-snapshot
The Statistical Analysis of Compositional Data
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02031.xopenalex-snapshot
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
inferredcites →https://doi.org/10.1126/science.1261359openalex-snapshot
Structure and function of the global ocean microbiome
inferredcites →https://doi.org/10.1128/aem.00062-07openalex-snapshot
Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
inferredcites →https://doi.org/10.1128/aem.03006-05openalex-snapshot
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
inferredcites →https://doi.org/10.1128/msystems.00031-18openalex-snapshot
American Gut: an Open Platform for Citizen Science Microbiome Research
inferredcites →https://doi.org/10.1128/msystems.00092-17openalex-snapshot
Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes
inferredcites →https://doi.org/10.1128/msystems.00191-16openalex-snapshot
Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns
inferredcites →https://doi.org/10.1186/s12864-018-5160-5openalex-snapshot
Analysis and correction of compositional bias in sparse sequencing count data
inferredcites →https://doi.org/10.1186/s13059-014-0550-8openalex-snapshot
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
inferredcites →https://doi.org/10.1186/s13059-018-1406-4openalex-snapshot
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
inferredcites →https://doi.org/10.1186/s40168-016-0175-0openalex-snapshot
Adjusting microbiome profiles for differences in microbial load by spike-in bacteria
inferredcites →https://doi.org/10.1186/s40168-017-0237-yopenalex-snapshot
Normalization and microbial differential abundance strategies depend upon data characteristics
inferredcites →https://doi.org/10.1186/s40168-018-0491-7openalex-snapshot
Absolute quantitation of microbiota abundance in environmental samples
inferredcites →https://doi.org/10.1214/09-aoas312openalex-snapshot
Brownian distance covariance
inferredcites →https://doi.org/10.1371/journal.pbio.1001377openalex-snapshot
The Human Microbiome Project: A Community Resource for the Healthy Human Microbiome
inferredcites →https://doi.org/10.1371/journal.pcbi.1003531openalex-snapshot
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible
inferredcites →https://doi.org/10.1371/journal.pone.0067019openalex-snapshot
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq
inferredcites →https://doi.org/10.1371/journal.pone.0094249openalex-snapshot
Analysis, Optimization and Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys
inferredcites →https://doi.org/10.1371/journal.pone.0129606openalex-snapshot
Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data
inferredcites →https://doi.org/10.1371/journal.pone.0227285openalex-snapshot
Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling
inferredcites →https://doi.org/10.3389/fmicb.2017.02114openalex-snapshot
Analysis of Microbiome Data in the Presence of Excess Zeros
inferredcites →https://doi.org/10.3389/fmicb.2017.02224openalex-snapshot
Microbiome Datasets Are Compositional: And This Is Not Optional
inferredcites →https://doi.org/10.3389/fmicb.2020.599614openalex-snapshot
Patterns of Relative and Quantitative Abundances of Marine Bacteria in Surface Waters of the Subtropical Northwest Pacific Ocean Estimated With High-Throughput Quantification Sequencing
inferredcites →https://doi.org/10.3402/mehd.v26.27663openalex-snapshot
Analysis of composition of microbiomes: a novel method for studying microbial composition
inferredcites →https://doi.org/10.48550/arxiv.1704.01841openalex-snapshot
Finding the centre: corrections for asymmetry in high-throughput sequencing datasets
inferredcites →https://doi.org/10.5808/gi.2019.17.1.e6openalex-snapshot
Statistical Analysis of Metagenomics Data
inferredcites →https://doi.org/10.6082/2m1m7-6yb46openalex-snapshot
Evolution of Adaptability and the Immune Response to Influenza and HIV

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Pipelines and Databases—Microbiome Analysis
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Detecting Association in Microbiome Compositional Data: A Novel Approach
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Exploring the soil microbiota of maritime pine (Pinus pinaster) groves, nearby woody crops (vineyards) and rainfed cereal crops, and its relation to tree decline
inferred← citeshttps://doi.org/10.1016/b978-0-323-91631-8.00017-2openalex-snapshot
Data science to biologists: Insights into microbiomics
inferred← citeshttps://doi.org/10.1016/b978-0-443-40551-8.00018-4openalex-snapshot
Personalized medicine and microbiome integration
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Relation of the soil microbiota of cork oak groves and surrounding grasslands to tree decline
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Statistical modeling and analysis of cell counts from multiplexed imaging data
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Soil mycobiome and forest endophytic fungi: Is there a relationship between them?
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2021.145246openalex-snapshot
Heavy metal (PTE) ecotoxicology, data review: Traditional vs. a compositional approach
inferred← citeshttps://doi.org/10.1038/s41598-024-62437-wopenalex-snapshot
A strategy for differential abundance analysis of sparse microbiome data with group-wise structured zeros
inferred← citeshttps://doi.org/10.1038/s41598-025-11430-yopenalex-snapshot
Glyphosate and phosphate treatments in soil differentially affect crop microbiomes depending on species, tissue and growth stage
inferred← citeshttps://doi.org/10.1080/19490976.2023.2244139openalex-snapshot
Statistical normalization methods in microbiome data with application to microbiome cancer research
inferred← citeshttps://doi.org/10.1080/19490976.2025.2552347openalex-snapshot
mbSparse: an autoencoder-based imputation method to address sparsity in microbiome data
inferred← citeshttps://doi.org/10.1093/bib/bbae205openalex-snapshot
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys
inferred← citeshttps://doi.org/10.1093/bib/bbag495openalex-snapshot
IRS: iterative reference selection improves normalization of microbiome sequencing data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad178openalex-snapshot
Multiscale adaptive differential abundance analysis in microbial compositional data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad668openalex-snapshot
Compositional analysis of microbiome data using the linear decomposition model (LDM)
inferred← citeshttps://doi.org/10.1093/bioinformatics/btae661openalex-snapshot
ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models
inferred← citeshttps://doi.org/10.1093/biomet/asac029openalex-snapshot
Robust differential abundance test in compositional data
inferred← citeshttps://doi.org/10.1093/nar/gkad527openalex-snapshot
Achieving pan-microbiome biological insights via the dbBact knowledge base
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Shrinkage improves estimation of microbial associations under different normalization methods
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mbImpute: an accurate and robust imputation method for microbiome data
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Statistical modeling and analysis of multiplexed imaging data
inferred← citeshttps://doi.org/10.1101/2025.02.13.638109openalex-snapshot
Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth
inferred← citeshttps://doi.org/10.1111/1755-0998.13730openalex-snapshot
Pitfalls in the statistical analysis of microbiome amplicon sequencing data
inferred← citeshttps://doi.org/10.1186/s13059-021-02400-4openalex-snapshot
mbImpute: an accurate and robust imputation method for microbiome data
inferred← citeshttps://doi.org/10.1186/s13059-023-03143-0openalex-snapshot
Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010044openalex-snapshot
A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota
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RSim: A reference-based normalization method via rank similarity
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An optimal normalization method for high sparse compositional microbiome data
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BMDD: A probabilistic framework for accurate imputation of zero-inflated microbiome sequencing data
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Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?
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Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?
inferred← citeshttps://doi.org/10.3389/fcimb.2022.925444openalex-snapshot
Fecal microbiota in patients with a stoma decreases anaerobic bacteria and alters taxonomic and functional diversities
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Rhizosphere mycobiome diversity in four declining Mediterranean tree species
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A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions
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Impact of Experimental Bias on Compositional Analysis of Microbiome Data
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Additional file 1 of Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets
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Additional file 2 of Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets
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