Compositional Data Analysis in Microbiome Research: Why Relative Abundances Are Not Independent and How to Handle Them

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-20

Microbiome sequence data are inherently compositional. When you sequence a microbial community, the total number of reads in a sample is an artifact of sequencing depth, not a measure of absolute microbial load. Each taxon's abundance is therefore expressed relative to all other taxa in that sample, and the sum of all relative abundances is constrained to a constant. This simple constraint violates a core assumption of standard statistical methods: that measurements are independent. Researchers who apply conventional correlation, regression, or clustering methods directly to relative abundance tables risk producing spurious correlations and incorrect biological conclusions. This article explains why compositionality matters, describes the centered log-ratio (CLR) transformation and related approaches, and provides practical guidance for applying compositional data analysis to taxonomic abundance data in metagenomics and 16S rRNA gene sequencing workflows.

Key Takeaways

• Compositional data analysis treats microbiome sequence data as relative proportions that sum to a constant, so standard statistical methods assuming independent variables produce spurious correlations and incorrect biological conclusions.
• The centered log-ratio (CLR) transformation removes the sum constraint by dividing each taxon's relative abundance by the geometric mean of all abundances in a sample, enabling standard multivariate analysis.
• The additive log-ratio (ALR) transformation uses a reference component, which should be well populated and not rare, and the reference choice can be guided by maximizing Procrustes correlation or minimizing variance.
• The isometric log-ratio (ILR) transformation maps data to an orthonormal basis with exact Euclidean geometry, but interpreting its balances requires understanding which taxa contribute to each coordinate.
• A practical workflow includes generating a count table, assessing quality and compositional structure, applying a log-ratio transformation, then using appropriate statistical methods and validation steps.

The Compositional Nature of Microbiome Data

Why Sequencing Produces Relative, Not Absolute, Abundances

High-throughput DNA sequencing does not count microbes directly. Instead, it generates reads that are bioinformatically assigned to taxa. The number of reads assigned to a given taxon depends on both the actual abundance of that taxon in the community and the total sequencing effort applied to the sample. Two samples sequenced to different depths will produce different read counts for the same underlying community, even if the true microbial composition is identical.

This means that the absolute abundances of microbes are not recoverable from sequence data alone. What you observe is a vector of proportions: each taxon's reads divided by the total reads in that sample. These proportions sum to 1 (or 100%), and this sum constraint is the defining feature of compositional data. The constraint means that if one taxon increases in relative abundance, others must decrease, regardless of whether their absolute abundances changed at all. This forced negative correlation is a mathematical artifact, not a biological signal.

The compositional nature of microbiome data has been recognized in the epidemiological literature for years. Human microbiome studies operate within the realm of compositional data because absolute microbial abundances cannot be recovered from sequence data alone. Each sample consists of proportions of various organisms with a sum constrained to a constant, and this simple feature can lead traditional statistical treatments, when naively applied, to produce errant results and spurious correlations. The origins of compositionality in microbiome data, the theory and usage of compositional data analysis in this setting, and recent attempts at solutions to these problems have been reviewed in the epidemiological literature (Compositional data analysis of the microbiome: fundamentals, tools, and challenges).

The Sparsity and High-Dimensionality Problem

Microbiome sequence datasets are typically high dimensional, with the number of taxa much greater than the number of samples. A typical 16S rRNA gene sequencing study might identify hundreds or thousands of taxa across dozens or hundreds of samples. This creates a situation where the number of variables far exceeds the number of observations, a condition that challenges many standard statistical methods.

Compounding this issue is sparsity. Most taxa are observed in only a small number of samples. A taxon that appears in 5% of samples with a relative abundance of 0.01% contributes little information but still occupies a column in the data matrix. These features of microbiome sequence data interact with compositionality to produce additional challenges in analysis. Despite sophisticated approaches to statistical transformation, the analysis of compositional data may remain a partially intractable problem, limiting inference. Current research needs include better generation of simulated data and further study of how the severity of compositional effects changes when sampling microbial communities of widely differing diversity (Compositional data analysis of the microbiome: fundamentals, tools, and challenges).

Why Standard Methods Fail on Compositional Data

Standard statistical methods assume that each variable can vary independently of others. In compositional data, this assumption is violated by construction. Consider a simple example: you measure the relative abundances of two taxa across 20 samples. If you compute a Pearson correlation between these two relative abundance vectors, you will almost certainly find a negative correlation, even if the two taxa are biologically unrelated. This is because the sum constraint forces one to decrease when the other increases.

This problem extends beyond pairwise correlations. Principal component analysis, clustering, regression models, and differential abundance testing all assume some form of independence or unconstrained variation. When applied to compositional data, these methods can produce clusters that reflect the sum constraint instead of biological differences, identify "differential" taxa that are merely responding to changes in other taxa, and generate correlation networks full of spurious edges.

The consequences are not theoretical. Studies that ignore compositionality can report associations between taxa that do not exist, miss associations that do exist, and produce results that cannot be replicated in independent cohorts. The compositional structure of microbiome data must be addressed in the analysis plan, not as an afterthought.

Core Principles of Compositional Data Analysis

The Log-Ratio Approach

The foundational insight of compositional data analysis is that information about a composition resides in the ratios between parts, not in the parts themselves. If you take the logarithm of the ratio between two taxa, the result is independent of the total sum of the composition. This log-ratio approach forms the basis of all compositional data analysis methods.

The simplest log-ratio transformation is the additive log-ratio (ALR), which uses one component as a reference and computes the log of each other component divided by the reference. The ALR is straightforward to compute and interpret, but it has a limitation: the choice of reference component affects the results, and the full set of additive logratios is not isometric, meaning it does not perfectly reproduce the geometry of all pairwise logratios. However, the lack of isometry can be measured by the Procrustes correlation, and the reference component can be chosen to maximize this correlation. For high-dimensional data there are many potential references, and secondary criteria such as minimizing the variance of the reference component's log-transformed relative abundance values can make interpretation easier. It is preferable that the reference component not be rare but well populated, and substantive biological reasons might also guide the choice if several reference candidates are identified (Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation).

The Centered Log-Ratio Transformation

The centered log-ratio (CLR) transformation is the most commonly used approach in microbiome research. For each sample, the CLR computes the logarithm of each taxon's relative abundance divided by the geometric mean of all relative abundances in that sample. This transformation removes the sum constraint and produces data that can be analyzed with standard multivariate methods.

The CLR has several advantages. It is symmetric, meaning no reference component needs to be chosen. It preserves the geometry of the compositional data, so distances between samples in CLR space reflect true compositional differences. And it can be applied to any number of components, making it suitable for high-dimensional microbiome data.

The CLR has been applied successfully in real-world studies. In an analysis of 16S rRNA gene sequencing data from hospital airborne microbiome samples, the exploration of CLR-transformed data by principal component analysis via singular value decomposition showed that collected samples segregated with an observable separation depending on the monitoring location. Two main sample clusters were identified with regards to bacterial genera, consisting of samples mostly collected in rooms with and without COVID-19 patients. Human pathogenic genera associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera mainly isolated from soil were detected in the other samples (Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples).

The Isometric Log-Ratio Transformation

The isometric log-ratio (ILR) transformation is a more sophisticated approach that maps compositional data to an orthonormal basis in real space. The ILR produces coordinates that are isometric, meaning distances in ILR space exactly match compositional distances. This property makes the ILR suitable for methods that rely on Euclidean geometry, such as principal component analysis and clustering.

The ILR has a practical disadvantage: the resulting coordinates are balances between groups of taxa, and interpreting them requires understanding which taxa contribute to each balance. This interpretation burden can be substantial for researchers who are primarily interested in individual taxa. However, the ILR is the transformation of choice when the analysis method requires true Euclidean geometry.

Practical Workflow for Compositional Analysis

Step 1: Generate a Count Table

The first step in any compositional analysis is to generate a count table from raw sequencing data. This table should have samples as rows and taxa as columns, with each cell containing the number of reads assigned to that taxon in that sample. The count table is the raw material for all downstream analysis.

For 16S rRNA gene sequencing data, the standard approach involves quality filtering, denoising, and taxonomic assignment. Recent best practices recommend replacing operational taxonomic unit-based analyses with methods based on exact sequence variants, also called amplicon sequence variants. These methods provide higher taxonomic resolution and better false discovery rate control than older approaches (Best practices for analysing microbiomes).

For shotgun metagenomics data, the workflow involves quality trimming, host sequence removal, and taxonomic classification against reference databases. The NCBI provides a range of sequence resources and analysis services that can support this work, including databases for taxonomic classification and genome reference sequences (NCBI Data Resources).

Step 2: Assess Data Quality and Compositional Structure

Before applying any transformation, examine the count table for quality issues. Check for samples with very low read counts, which may need to be removed or rarefied. Check for taxa that appear in very few samples, which may need to be filtered to reduce sparsity. Check for batch effects or other technical artifacts that could confound the analysis.

The compositional structure of the data should be assessed explicitly. One approach is to examine the distribution of library sizes across samples. If library sizes vary substantially, the data are clearly compositional in the sense that read counts are not directly comparable across samples. Another approach is to examine the relationship between total reads and individual taxon counts, which can reveal whether the sum constraint is driving observed patterns.

Step 3: Apply a Log-Ratio Transformation

Choose a log-ratio transformation based on your analysis goals. The CLR is the most versatile choice for exploratory analysis and multivariate methods. The ALR may be preferred when you have a biologically meaningful reference taxon, such as a known commensal organism. The ILR is appropriate when you need exact Euclidean geometry for downstream methods.

For the CLR, the transformation is applied to each sample independently. For each taxon, compute the logarithm of its relative abundance divided by the geometric mean of all relative abundances in that sample. The resulting values are centered around zero, with positive values indicating taxa more abundant than the average and negative values indicating taxa less abundant than the average.

Step 4: Apply Appropriate Statistical Methods

Once the data are transformed, apply statistical methods that are appropriate for the transformed data. For exploratory analysis, principal component analysis and clustering can be applied to CLR-transformed data. For differential abundance testing, use methods specifically designed for compositional data, such as those based on log-ratio models.

Several software tools implement compositional data analysis methods. The Bioconductor project provides packages for reproducible genomic analysis, including tools for compositional data analysis of microbiome data (Bioconductor). The Galaxy Training Network's open-access curriculum enables analysts to run structured bioinformatics pipelines in reproducible, shareable cloud-based environments.galaxyproject.org/)). The nf-core community provides standardized pipelines for reproducible bioinformatics workflows, including metagenomics pipelines (nf-core Documentation).

Step 5: Validate and Interpret Results

Compositional analysis results should be validated through multiple approaches. Check whether findings are robust to the choice of transformation. Examine whether results are driven by a few dominant taxa or reflect community-wide patterns. Compare results with those from alternative methods to identify concordant and discordant findings.

Interpretation should acknowledge the limitations of compositional data. A taxon that appears to increase in relative abundance may have increased in absolute abundance, decreased less than other taxa, or remained constant while others decreased. Without absolute abundance measurements, these possibilities cannot be distinguished. This limitation should be stated explicitly in any report or publication.

At a Glance: Choosing a Compositional Approach

Software Tools and Implementation Options

R Packages for Compositional Data Analysis

The R programming environment hosts the most mature ecosystem of compositional data analysis tools for microbiome research. The coda4microbiome package addresses the compositional nature of microbiome data in both cross-sectional and longitudinal studies. The algorithm relies on the analysis of log-ratios between pairs of components, and variable selection is addressed through penalized regression on the all-pairs log-ratio model, the model containing all possible pairwise log-ratios. When analyzing longitudinal profiles, the model identifies temporal taxonomic signatures by executing penalized regression across integrated area-under-the-curve metrics derived from log-ratio trajectories. In both cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the weighted balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures (coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies).

The CoDaLoMic package implements three models specifically designed for the analysis of microbiome data that are both compositional and longitudinal. Unlike many existing methods that focus solely on pairwise interactions, CoDaLoMic also captures interactions among groups of bacteria, providing a more robust methodological framework for studying microbial relationships at the community level. This computational framework streamlines the evaluation of microbiome stability across physiological health states, uncovering distinct bacterial consortia that share synchronized dynamic patterns. Working with time series data makes it possible to understand also the current state of a microbial community but also its dynamics over time, which is essential for identifying patterns of ecological succession, detecting events of dysbiosis or recovery, and inferring potential causal relationships between taxa. Focusing on interactions among groups of bacteria, instead of analyzing only pairwise relationships, enables a more integrated and functionally meaningful view of the microbiome because many key ecological functions are the result of the collective behavior of functionally related groups of taxa (CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data).

The LinDA method provides linear models for differential abundance analysis of microbiome compositional data. This approach addresses the compositional structure of microbiome data within a regression framework, offering an alternative to methods based on log-ratio transformations (LinDA: linear models for differential abundance analysis of microbiome compositional data).

Workflow Platforms and Reproducibility

The Galaxy Training Network curates browser-executed bioinformatics tutorials specifically designed for reproducible, sharable research workflows.

The EMBL-EBI Training program provides learning pathways for bioinformatics data resources and practical analysis education (EMBL-EBI Training). These resources can help researchers build the skills needed to implement compositional data analysis correctly.

Handling Experimental Bias

Microbiome data are subject to experimental bias caused by DNA extraction and PCR amplification, among other sources. This important feature is often ignored when developing statistical methods for analyzing microbiome data. A model for how such biases affect observed taxonomic profiles assumes the main effects of bias without taxon-taxon interactions. Methods that account for experimental bias are robust to main effect biases, but there is also evidence for taxon-taxon interactions. Simulation results indicate that some methods remain robust to a reasonable range of interaction biases, while other methods tend to have an inflated false discovery rate even when there were only main effect biases. Methods that maintain high sensitivity even when other methods cannot control the false discovery rate are preferable for compositional analysis of microbiome data (Impact of Experimental Bias on Compositional Analysis of Microbiome Data).

Common Failure Patterns in Compositional Analysis

Failure Pattern 1: Analyzing Relative Abundances Without Transformation

The most common failure is to apply standard statistical methods directly to relative abundance data. This approach produces spurious correlations because the sum constraint forces negative correlations between taxa. It also produces misleading ordination results because the first principal component often captures the sum constraint instead of biological variation.

Prevention: Always apply a log-ratio transformation before multivariate analysis. The CLR is the default choice for most applications.

Failure Pattern 2: Using Rarefaction as a Solution

Rarefaction, the practice of subsampling all samples to the same read count, is sometimes proposed as a solution to variable sequencing depth. Rarefaction does not solve the compositional problem. It reduces the total information available and introduces additional stochasticity, but the resulting data are still compositional because the rarefied counts still sum to a constant within each sample.

Prevention: Use log-ratio transformations instead of rarefaction. If rarefaction is used for other reasons, such as reducing the influence of variable sequencing depth, apply compositional methods to the rarefied data.

Failure Pattern 3: Interpreting Relative Abundance Changes as Absolute Changes

A taxon that increases in relative abundance may not have increased in absolute abundance. It may have decreased less than other taxa, or remained constant while others decreased. Interpreting relative abundance changes as absolute changes leads to incorrect biological conclusions.

Prevention: State explicitly that results are based on relative abundances. If absolute abundance information is needed, use complementary methods such as quantitative PCR or spike-in controls.

Failure Pattern 4: Ignoring Sparsity in Transformation

The CLR transformation requires the logarithm of each relative abundance. If a taxon has zero counts in a sample, the logarithm is undefined. Common solutions include adding a small pseudocount to all values or replacing zeros with a small value based on the detection limit. The choice of zero-handling method can affect results, and this choice should be documented and justified.

Prevention: Document the zero-handling method and assess the sensitivity of results to this choice. Consider using methods specifically designed for zero-inflated compositional data.

Failure Pattern 5: Applying Compositional Methods Without Understanding Assumptions

Compositional methods make assumptions about the data structure. The CLR assumes that the geometric mean is a meaningful reference, which may not hold when many taxa are absent. Log-ratio-based regression assumes that the log-ratios are linearly related to covariates, which may not hold in all settings.

Prevention: Understand the assumptions of each method and assess whether they are met in your data. Consult the method documentation and relevant literature before applying new methods.

Records and Measurements for Compositional Analysis

Documentation Requirements

Compositional analysis should be documented thoroughly to support reproducibility and interpretation. The following records should be maintained for each analysis:

Quality Metrics to Monitor

Several quality metrics should be monitored throughout the compositional analysis workflow. Library size, the total number of reads per sample, should be recorded and examined for outliers. The number of taxa detected per sample provides a measure of sequencing depth adequacy. The proportion of reads assigned to known taxa versus unclassified reads indicates the quality of taxonomic assignment. The geometric mean of relative abundances, used in the CLR transformation, should be checked for extreme values that might indicate problematic samples.

Options and Tradeoffs in Compositional Methods

CLR Versus ALR Versus ILR

The choice of transformation involves tradeoffs between interpretability, mathematical properties, and practical convenience. The CLR is the most commonly used because it requires no reference component and preserves compositional geometry. However, the CLR can be sensitive to the inclusion of rare taxa, which contribute little information but affect the geometric mean.

The ALR requires a reference component but produces coordinates that are directly interpretable as log-ratios relative to that reference. The choice of reference affects results, and the reference should be chosen based on biological relevance or statistical criteria such as maximizing the Procrustes correlation between the additive logratio geometry and the exact logratio geometry (Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation).

The ILR produces isometric coordinates but at the cost of interpretability. The balances produced by the ILR are contrasts between groups of taxa, and understanding these contrasts requires knowledge of which taxa contribute to each balance.

Compositional Methods Versus Alternative Approaches

Compositional data analysis is not the only framework for analyzing microbiome data. Alternative approaches include methods based on zero-inflated models, which treat the data as a mixture of zeros and positive counts, and methods based on phylogenetic information, which incorporate evolutionary relationships among taxa. Each approach has strengths and limitations, and the choice of framework should be guided by the research question and the characteristics of the data.

The bioinformatic and statistical analysis of NGS-based microbiome data are essential components in microbiome research to explore the complex composition of microbial community and understand the functions of community members in relation to host and environment. Bioinformatic analysis methods generate taxonomy and functional feature count tables along with phylogenetic trees from raw NGS microbiome data, and statistical methods and machine learning approaches are then applied to analyze the outputs to infer the biodiversity of a microbial community and unravel host-microbiome associations. Understanding the advantages and limitations of the analysis methods helps researchers use the methods correctly in microbiome data analysis and may give a new opportunity to develop new analytic techniques for microbiome research (Bioinformatic and Statistical Analysis of Microbiome Data).

Longitudinal and Multi-Omic Considerations

Compositional Analysis of Longitudinal Data

Longitudinal microbiome studies present additional compositional challenges. Abundances measured at different times can correspond to different sub-compositions, meaning that the set of taxa present can change over time within the same individual. This complicates the interpretation of log-ratios computed at different time points.

Methods designed for longitudinal compositional data address this challenge by modeling the trajectories of log-ratios over time. The coda4microbiome package infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories, specifically the area under these trajectories (coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies). The CoDaLoMic package captures interactions among groups of bacteria over time, providing a framework for studying microbial relationships at the community level and identifying groups of taxa that exhibit similar temporal dynamics (CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data).

Integrating Compositional Data with Other Omics

Microbiome studies increasingly integrate multiple omics data sets, such as metabolomics, transcriptomics, and proteomics. These data sets are also compositional in nature, as they consist of counts or concentrations of many components measured simultaneously. The compositional framework can be extended to these data types, and methods for integrating metagenomic and metabolomic data have been developed (Best practices for analysing microbiomes).

Mediation analysis provides a principled framework for understanding how environmental, behavioral, or clinical exposures influence human health through microbiome-mediated biological pathways. Its application is complicated by the compositional, sparse, and high-dimensional nature of microbiome data. Novel analytical toolkits increasingly leverage structural equation frameworks, potential-outcomes causal inference, multivariate distance metrics, Bayesian sparsity priors, and distribution-free models to circumvent compositional distortion. These methods address the underlying assumptions, limitations, and appropriate contexts of use for mediation analysis in microbiome studies (Microbiome mediation analysis: methods, assumptions, and practical considerations).

Advanced Methods and Current Research Directions

Bayesian Approaches for Compositional Data

Bayesian methods offer a flexible framework for compositional data analysis, particularly in settings with small sample sizes and high dimensionality. A Bayesian transfer learning approach under a compositional generalized linear mixed model has been proposed for microbiome data analysis. This method achieves knowledge transfer to the target model by incorporating informative source data and further realizes adaptive selection of transfer parameters to prevent negative transfer. It explicitly accounts for compositional constraints inherent in microbiome data and incorporates phylogenetic relatedness among taxa and dependence among samples. Systematic simulations demonstrate that the proposed methods significantly outperform baseline methods under a variety of source data conditions, and the adaptive transfer strategy enhances the robustness and generalization performance of the method (Adaptive Bayesian Transfer Learning under Compositional Generalized Linear Mixed Model for Microbiome Data Analysis).

Network-Based Approaches

Network analysis of microbiome data aims to identify co-occurrence and co-exclusion patterns among taxa. Compositional methods are particularly important for network analysis because the sum constraint can induce spurious correlations that appear as network edges. Methods that account for compositionality in network construction produce more reliable networks.

Network approaches have been applied to understand the relationship between microbiome composition and disease. In a study of cardiometabolic disease, co-occurrence networks of individuals with elevated cardiometabolic parameters showed lower clustering coefficients, higher path lengths, lower degrees, higher modularity, and higher negative cohesion than those with normal parameters. The loss of health-associated gut microbiota, along with decreased network connectivity and increased negative cohesion, characterized the microbiome of individuals with early cardiometabolic risk factors (Gut microbiome dynamics and cardiometabolic disease progression: evidence from a secondary data analysis of pooled clinical datasets).

Ecotype and Community-Level Analysis

Community-level analysis moves beyond taxon-centric approaches to examine the ecological structure of microbial communities. Unsupervised ecotype analysis can identify independent bacterial microbiome states that are not defined by clinical variables. In a study of oral HPV infection, unsupervised ecotype analysis identified three independent bacterial microbiome states, none defined by HPV status. In multivariate PERMANOVA analysis, microbial diversity and virulence ecology explained significantly more community bacterial variation than HPV status (Oral human papillomavirus infection aligns with a coordinated bacterial microbiome inferred virulence ecology).

Limitations and Professional Escalation Criteria

When Compositional Methods Are Not Sufficient

Compositional data analysis addresses the sum constraint but does not solve all problems in microbiome research. The sparsity of microbiome data, with many taxa observed in few samples, remains challenging even after log-ratio transformation. The high dimensionality of the data, with more taxa than samples, limits the power of statistical tests. And the inability to recover absolute abundances from sequence data alone means that relative abundance changes cannot be interpreted as absolute changes.

The analysis of compositional data may remain a partially intractable problem, limiting inference, despite sophisticated approaches to statistical transformation. Current research needs include better generation of simulated data and further study of how the severity of compositional effects changes when sampling microbial communities of widely differing diversity (Compositional data analysis of the microbiome: fundamentals, tools, and challenges).

When to Seek Specialized Assistance

Researchers should consider seeking specialized assistance when they encounter any of the following situations:

1. The research question requires absolute abundance measurements, which cannot be obtained from standard sequencing workflows.
2. The data have extreme sparsity, with most taxa present in fewer than 5% of samples.
3. The study involves complex longitudinal designs with irregular sampling times or missing data.
4. The analysis requires integration of multiple omics data types with compositional structure.
5. The results will inform clinical decisions or regulatory submissions, where statistical rigor is paramount.

In these situations, consultation with a biostatistician or bioinformatician with expertise in compositional data analysis is recommended. The EMBL-EBI Training program and the Galaxy Training Network offer educational resources that can help researchers build the skills needed for advanced compositional analysis (EMBL-EBI Training, Galaxy Training Network).

Reporting Standards for Compositional Analysis

Reports and publications should describe the compositional methods used in sufficient detail to support replication. This includes the choice of transformation, the zero-handling method, the software and version used, and the parameters of any statistical models. The limitations of compositional analysis should be stated explicitly, particularly the inability to distinguish between changes in absolute abundance and changes in relative abundance.

Frequently Asked Questions

What is the difference between relative abundance and absolute abundance in microbiome data?

Relative abundance is the proportion of reads assigned to a taxon within a sample, calculated as the taxon's read count divided by the total reads in that sample. Absolute abundance is the actual number of microbial cells or genomes of that taxon in the sample. Sequencing provides relative abundances because the total number of reads is determined by sequencing depth, not by the total microbial load. The absolute abundances of microbes are not recoverable from sequence data alone (Compositional data analysis of the microbiome: fundamentals, tools, and challenges).

Why does the sum constraint cause spurious correlations?

The sum constraint means that all relative abundances in a sample sum to a constant, typically 1 or 100. If one taxon increases in relative abundance, the others must decrease to maintain the constant sum. This forced negative correlation is a mathematical artifact of the constraint, not a biological relationship. Standard correlation methods cannot distinguish between correlations caused by the sum constraint and correlations caused by genuine biological interactions.

What is the centered log-ratio transformation and when should I use it?

The centered log-ratio (CLR) transformation computes the logarithm of each taxon's relative abundance divided by the geometric mean of all relative abundances in that sample. It removes the sum constraint and produces data that can be analyzed with standard multivariate methods. The CLR is the default choice for exploratory analysis, ordination, and clustering because it requires no reference component and preserves compositional geometry. It has been applied successfully in studies of hospital airborne microbiomes, where CLR-transformed data revealed sample segregation by monitoring location (Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples).

How do I handle zeros in compositional data analysis?

The CLR transformation requires the logarithm of each relative abundance, and the logarithm of zero is undefined. Common solutions include adding a small pseudocount to all values, replacing zeros with a value based on the detection limit, or using methods specifically designed for zero-inflated compositional data. The choice of zero-handling method can affect results, so the method should be documented and sensitivity analyses should be conducted to assess the impact of this choice.

What software packages are available for compositional data analysis of microbiome data?

Several R packages implement compositional data analysis methods for microbiome data. The coda4microbiome package identifies microbial signatures using penalized regression on pairwise log-ratios for both cross-sectional and longitudinal studies (coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies). The CoDaLoMic package models longitudinal and compositional data with a focus on interactions among groups of bacteria (CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data). The LinDA package provides linear models for differential abundance analysis (LinDA: linear models for differential abundance analysis of microbiome compositional data). The Bioconductor project provides additional packages for reproducible genomic analysis (Bioconductor).

Can I use standard statistical methods after CLR transformation?

Yes, the CLR transformation produces data that can be analyzed with standard multivariate methods such as principal component analysis, clustering, and regression. However, the transformed data still have properties that require attention, including high dimensionality and sparsity. Methods that account for these properties, such as penalized regression and methods designed for high-dimensional data, are often more appropriate than standard methods.

How does compositionality affect differential abundance testing?

Differential abundance testing aims to identify taxa whose abundances differ between groups. When applied to relative abundance data, standard tests can produce false positives because the sum constraint forces some taxa to appear differentially abundant even when their absolute abundances are unchanged. Compositional methods for differential abundance testing use log-ratios or other transformations to account for the sum constraint. Methods that account for experimental bias in addition to compositionality provide more reliable results (Impact of Experimental Bias on Compositional Analysis of Microbiome Data).

What are the limitations of compositional data analysis?

Compositional data analysis addresses the sum constraint but does not solve all problems in microbiome research. The sparsity of microbiome data, with many taxa observed in few samples, remains challenging. The high dimensionality of the data limits statistical power. And the inability to recover absolute abundances from sequence data alone means that relative abundance changes cannot be interpreted as absolute changes. The analysis of compositional data may remain a partially intractable problem, limiting inference (Compositional data analysis of the microbiome: fundamentals, tools, and challenges).

Related Bioinformatics Guides

• Microbiome Data Analysis in R: A Practical Guide for Compositional Data
• Genomic Data Analysis Tools: A Comparative Guide for Researchers
• Longitudinal Microbiome Data Analysis: Methods and Best Practices
• Metabolomics Data Analysis in R: A Practical Workflow
• Data Stewardship vs. Data Governance: Roles and Responsibilities in Research

References and Further Reading

• NCBI Data Resources. National Center for Biotechnology Information.
• EMBL-EBI Training. European Bioinformatics Institute.
• Bioconductor. Bioconductor Project.
• Galaxy Training Network. Galaxy Project.
• nf-core Documentation. nf-core.
• The Carpentries Lessons. The Carpentries.
• Compositional data analysis of the microbiome: fundamentals, tools, and challenges.. Annals of epidemiology, 2016.
• Best practices for analysing microbiomes.. Nature reviews. Microbiology, 2018.
• coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies.. BMC bioinformatics, 2023.
• Population-level analysis of gut microbiome variation.. Science (New York, N.Y.), 2016.
• Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data.. Canadian journal of microbiology, 2016.
• Bioinformatic and Statistical Analysis of Microbiome Data.. Methods in molecular biology (Clifton, N.J.), 2023.
• Impact of Experimental Bias on Compositional Analysis of Microbiome Data.. Genes, 2023.
• Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples.. International journal of environmental research and public health, 2022.
• CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data.. 2026.
• Adaptive Bayesian Transfer Learning under Compositional Generalized Linear Mixed Model for Microbiome Data Analysis. 2026.
• Gut microbiome dynamics and cardiometabolic disease progression: evidence from a secondary data analysis of pooled clinical datasets.. 2026.
• Microbiome mediation analysis: methods, assumptions, and practical considerations.. 2026.
• Oral human papillomavirus infection aligns with a coordinated bacterial microbiome inferred virulence ecology.. 2026.
• Bugs as features (part 1): concepts and foundations for the compositional data analysis of the microbiome-gut-brain axis. Nature Mental Health, 2022.
• Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation. bioRxiv, 2021.
• LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biology, 2022.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.