# MaAsLin 3 User Manual

#### William Nickols

willnickols@g.harvard.edu

#### Jacob Nearing

nearing@broadinstitute.org

#### Sagun Maharjan

smaharjan@hsph.harvard.edu

## Introduction

MaAsLin 3 is the next generation of MaAsLin (Microbiome Multivariable Associations with Linear Models). This comprehensive R package efficiently determines multivariable associations between clinical metadata and microbial meta-omics features. Relative to MaAsLin 2, MaAsLin 3 introduces the ability to quantify and test for both abundance and prevalence associations while accounting for compositionality. By incorporating generalized linear models, MaAsLin 3 accommodates most modern epidemiological study designs including cross-sectional and longitudinal studies.

If you use the MaAsLin 3 software, please cite our manuscript:

> William A. Nickols, Thomas Kuntz, Jiaxian Shen, Sagun Maharjan, Himel Mallick, Eric A. Franzosa, Kelsey N. Thompson, Jacob T. Nearing, Curtis Huttenhower. MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery. bioRxiv 2024.12.13.628459; doi: https://doi.org/10.1101/2024.12.13.628459

### Support

Check out the MaAsLin 3 tutorial for an overview of analysis options and some example runs. If using vignettes, users should start with the `maaslin3_tutorial.Rmd` vignette and then refer to the `maaslin3_manual.Rmd` vignette as necessary.

If you have questions, please direct them to the MaAsLin 3 Forum

## Contents

- Introduction
- Support
- Contents
- Requirements
- Installation
- Running MaAsLin 3
- Input data
- Output files
- Run a demo
  - In R
  - Plot revisions
  - Command line
  - Contrast testing
- Options
  - Required parameters
  - Model formula
  - Analysis options
  - Compositionality corrections
  - Plotting parameters
  - Technical parameters
- Troubleshooting

## Requirements

MaAsLin3 is an R package that can be run on the command line or as an R function.

## Installation

#### Install using GitHub and devtools

The latest version of MaAsLin 3 can be installed from BiocManager.

    if (!require("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install("biobakery/maaslin3")

To compile vignettes yourself, specify `dependencies = TRUE`.

## Running MaAsLin 3

MaAsLin3 can be run from the command line or as an R function. Both methods require the same arguments, have the same options, and use the same default settings. To run MaAsLin 3, the user must supply a table of per-sample feature abundances (with zeros still included), a table of per-sample metadata, and a model specifying how the metadata should relate to the feature prevalence (how likely the feature is to be present or absent) and abundance (how much of the feature is there if it’s there). MaAsLin 3 will return a table of associations including an effect size and p-value for each feature-metadatum association and a folder of visuals including a summary plot and diagnostic plots for significant associations.

### Input data

MaAsLin3 requires two input files.

1. Feature abundance data frame
   - Formatted with features as columns and samples as rows.
   - The transpose of this format is also okay.
   - Possible features include taxonomy or genes. These can be relative abundances or counts.
   - This can be a filepath to a tab-delimited file.
2. Metadata data frame
   - Formatted with variables as columns and samples as rows.
   - The transpose of this format is also okay.
   - Possible metadata include gender or age.
   - This can be a filepath to a tab-delimited file.

The data file can contain samples not included in the metadata file (along with the reverse case). For both cases, those samples not included in both files will be removed from the analysis. Also, the samples do not need to be in the same order in the two files.

To run MaAsLin 3, it is also necessary to specify a model. The model can come from a formula or vectors of terms. In either case, variable names should not have spaces or unusual characters.

- Formula: The `formula` parameter should be set to any formula that satisfies the `lme4` specifications: fixed effects, random effects, interaction terms, polynomial terms, and more can all be included. If categorical variables are included as fixed effects, each level will be tested against the first factor level. In addition, ordered predictors, group predictors, and strata variables can be included by including `group(variable_name)`, `ordered(variable_name)`, and `strata(variable_name)` respectively in the formula. Ordered and group predictors should stand alone in the formula (i.e., no group predictors in random effects). Only one strata variable can be included.
- Vectors: Alternatively, a vector of variable names can be supplied to the parameters `fixed_effects`, `random_effects`, `group_effects`, `ordered_effects`, and `strata_effects`. Categorical variables should either be ordered as factors beforehand, or `reference` should be provided as a string of ‘variable,reference’ semi-colon delimited for multiple variables (e.g., `variable_1,reference_1;variable_2,reference_2`). NOTE: adding a space between the variable and level might result in the wrong reference level being used.

Because MaAsLin 3 identifies prevalence (presence/absence) associations, sample read depth (number of reads) should be included as a covariate if available. Deeper sequencing will likely increase feature detection in a way that could spuriously correlate with metadata of interest when read depth is not included in the model.

### Output files

MaAsLin 3 generates two types of output files explained below: data and visualizations. In addition, the object returned from `maaslin3` contains all the data and results (see `?maaslin_fit`).

1. Data output files
   - `all_results.tsv`
     - `feature` and `metadata` are the feature and metadata names.
     - `value` and `name` are the value of the metadata and variable name from the model.
     - `coef` and `stderr` are the fit coefficient and standard error from the model. In abundance models, a one-unit change in the metadatum variable corresponds to a \(2^{\textrm{coef}}\) fold change in the relative abundance of the feature. In prevalence models, a one-unit change in the metadatum variable corresponds to a \(\textrm{coef}\) change in the log-odds of a feature being present.
     - `null_hypothesis` is the value of the null hypothesis against which the coefficient is tested (zero or the per-metadatum median).
     - `pval_individual` is the p-value of the individual association.
     - `qval_individual` is the false discovery rate (FDR) corrected q-value of the individual association. FDR correction is performed over all p-values without errors in the abundance and prevalence modeling together.
     - `pval_joint` and `qval_joint` are the p-value and q-value of the joint prevalence and abundance association. The p-value comes from plugging in the minimum of the association’s abundance and prevalence p-values into the Beta(1,2) CDF. It is interpreted as the probability that either the abundance or prevalence association would be as extreme as observed if there was neither an abundance nor prevalence association between the feature and metadatum.
     - `error` lists any errors from the model fitting.
     - `model` specifies whether the association is abundance or prevalence.
     - `N` and `N_not_zero` are the total number of data points and the total number of non-zero data points for the feature.
   - `significant_results.tsv`
     - This file is a subset of the results in the first file.
     - It only includes associations with joint or individual q-values less than or equal to the threshold.
   - `features`
     - This folder includes the filtered, normalized, and transformed versions of the input feature table.
     - These steps are performed sequentially in the above order.
     - If an option is set such that a step does not change the data, the resulting table will still be output.
   - `models_linear.rds` and `models_logistic.rds`
     - These files contain a list with every model fit object (`linear` for linear models, `logistic` for logistic models).
     - It will only be generated if `save_models` is set to TRUE.
   - `residuals_linear.rds` and `residuals_logstic.rds`
     - These files contain a data frame with residuals for each feature.
   - `fitted_linear.rds` and `fitted_logistic.rds`
     - These files contain a data frame with fitted values for each feature.
   - `ranef_linear.rds` and `ranef_logistic.rds`
     - These files contain a data frame with extracted random effects for each feature (when random effects are specified).
   - `maaslin3.log`
     - This file contains all log information for the run.
     - It includes all settings, warnings, errors, and steps run.
2. Visualization output files
   - `summary_plot.pdf`
     - This file contain a combined coefficient plot and heatmap of the most significant associations. In the heatmap, one star indicates the individual q-value is below the parameter `max_significance`, and two stars indicate the individual q-value is below `max_significance` divided by 10.
   - `association_plots/[metadatum]/[association]/ [metadatum]_[feature]_[association].png`
     - A plot is generated for each significant association up to `max_pngs`.
     - Scatter plots are used for continuous metadata abundance associations.
     - Box plots are used for categorical data abundance associations.
     - Box plots are used for continuous data prevalence associations.
     - Grids are used for categorical data prevalence associations.
     - Data points plotted are after filtering, normalization, and transformation so that the scale in the plot is the scale that was used in fitting.

At the top right of each association plot is the name of the significant association in the results file, the FDR corrected q-value for the individual association, the number of samples in the dataset, and the number of samples with non-zero abundances for the feature. In the plots with categorical metadata variables, the reference category is on the left, and the significant q-values and coefficients in the top right are in the order of the values specified above. Because the displayed coefficients correspond to the full fit model with (possibly) scaled metadata variables, the marginal association plotted might not match the coefficient displayed. However, the plots are intended to provide an interpretable visual while usually agreeing with the full model.

#### Diagnostics

1. When warnings or errors are thrown during the fitting process, they are recorded in the `error` column of `all_results.tsv`. Often, these indicate model fitting failures or poor fits that should not be trusted, but sometimes the warnings can be benign, and the model fit might still be reasonable. Users should check associations of interest if they produce errors.
2. Despite the error checking, significant results could still result from poor model fits. These can usually be diagnosed with the visuals in the `association_plots` directory.
   - Any significant abundance associations with a categorical variable should usually have at least 10 observations in each category.
   - Significant prevalence associations with categorical variables should also have at least 10 samples in which the feature was present and at least 10 samples in which it was absent for each significant category.
   - Significant abundance associations with continuous metadata should be checked visually for influential outliers.
3. The q-values are FDR corrected over all abundance or prevalence relationships (separately), so it may be preferable to FDR correct just the p-values from the variables of interest. This can reduce false positives when there are many significant but uninteresting associations (e.g., many read depth associations).
4. There are also a few rules of thumb to keep in mind.
   - Models should ideally have about 10 times as many samples (all samples for logistic fits, non-zero samples for linear fits) as covariate terms (all continuous variables plus all categorical variable levels).
   - Coefficients (effect sizes) larger than about 15 in absolute value are usually suspect unless very small unstandardized predictors are being included. (A coefficient of 15 corresponds to a fold change >30000!). If you encounter such coefficients, check that (1) no error was thrown, (2) the plots look reasonable, (3) a sufficient number of samples were used in fitting, (4) the q-value is significant, (5) the metadata are not highly collinear, and (6) the random effects are plausible.

### Run a demo

Example input files can be found in the `inst/extdata` folder of the MaAsLin 3 source. The files provided were generated from the Human Microbiome Project 2 (HMP2) data which can be downloaded from https://ibdmdb.org/.

- `HMP2_taxonomy.tsv`: a tab-delimited file with samples as rows and species as columns. It is a subset of the full HMP2 taxonomy that includes just some of the the species abundances.
- `HMP2_metadata.tsv`: a tab-delimited file with samples as rows and metadata as columns. It is a subset of the full HMP2 metadata that includes just some of the fields.

#### In R

The following code identifies associations between patient metadata and microbial species in the HMP2 cohort.

    # Read abundance table
    taxa_table_name <- system.file("extdata", "HMP2_taxonomy.tsv",
                                    package = "maaslin3")
    taxa_table <- read.csv(taxa_table_name, sep = '\t', row.names = 1)

    # Read metadata table
    metadata_name <- system.file("extdata", "HMP2_metadata.tsv",
                                    package = "maaslin3")
    metadata <- read.csv(metadata_name, sep = '\t', row.names = 1)

    metadata$diagnosis <-
        factor(metadata$diagnosis, levels = c('nonIBD', 'UC', 'CD'))
    metadata$dysbiosis_state <-
        factor(metadata$dysbiosis_state, levels = c('none', 'dysbiosis_UC',
                                                    'dysbiosis_CD'))
    metadata$antibiotics <-
        factor(metadata$antibiotics, levels = c('No', 'Yes'))

    # Fit models
    set.seed(1)
    fit_out <- maaslin3(input_data = taxa_table,
                        input_metadata = metadata,
                        output = 'hmp2_output',
                        formula = '~ diagnosis + dysbiosis_state +
                        antibiotics + age + reads',
                        normalization = 'TSS',
                        transform = 'LOG',
                        augment = TRUE,
                        standardize = TRUE,
                        max_significance = 0.1,
                        median_comparison_abundance = TRUE,
                        median_comparison_prevalence = FALSE,
                        max_pngs = 20)

The `maaslin3` wrapper function runs all steps at once, but equivalent results would be produced from running one step at a time:

    maaslin_log_arguments(input_data = taxa_table,
                        input_metadata = metadata,
                        output = 'hmp2_output',
                        formula = '~ diagnosis + dysbiosis_state +
                        antibiotics + age + reads',
                        normalization = 'TSS',
                        transform = 'LOG',
                        augment = TRUE,
                        standardize = TRUE,
                        max_significance = 0.1,
                        median_comparison_abundance = TRUE,
                        median_comparison_prevalence = FALSE,
                        max_pngs = 20)

    read_data_list <- maaslin_read_data(taxa_table,
                                        metadata)
    read_data_list <- maaslin_reorder_data(read_data_list$data,
                                        read_data_list$metadata)

    data <- read_data_list$data
    metadata <- read_data_list$metadata

    formulas <- maaslin_check_formula(data,
                                    metadata,
                                    '~ diagnosis + dysbiosis_state +
                                        antibiotics + age + reads')

    formula <- formulas$formula
    random_effects_formula <- formulas$random_effects_formula

    normalized_data = maaslin_normalize(data,
                                        output = 'hmp2_output',
                                        normalization = 'TSS')

    filtered_data = maaslin_filter(normalized_data,
                                output = 'hmp2_output')

    transformed_data = maaslin_transform(filtered_data,
                                        output = 'hmp2_output',
                                        transform = 'LOG')

    standardized_metadata = maaslin_process_metadata(metadata,
                                                    formula,
                                                    standardize = TRUE)

    maaslin_results = maaslin_fit(filtered_data,
                                transformed_data,
                                standardized_metadata,
                                formula,
                                random_effects_formula,
                                data = data,
                                median_comparison_abundance = TRUE,
                                median_comparison_prevalence = FALSE)

    maaslin_write_results(output = 'hmp2_output',
                        maaslin_results$fit_data_abundance,
                        maaslin_results$fit_data_prevalence,
                        random_effects_formula,
                        max_significance = 0.1)

    # Invisible to avoid dumping plots
    invisible(maaslin_plot_results(output = 'hmp2_output',
                                transformed_data,
                                metadata,
                                maaslin_results$fit_data_abundance,
                                maaslin_results$fit_data_prevalence,
                                normalization = "TSS",
                                transform = "LOG",
                                median_comparison_abundance = TRUE,
                                median_comparison_prevalence = FALSE,
                                max_significance = 0.1,
                                max_pngs = 20))

#### Replotting

Once `maaslin3` has been run once, `maaslin_plot_results_from_output` can be run to (re-)create the plots. This allows the user to plot the associations even without having the R object returned by `maaslin_fit` or `maaslin3` (e.g., if fitting models through the command line). It is recommended to fit models with simple variables names that are robust to formula plotting and then convert these into proper names for plotting. Likewise, `heatmap_vars` and `coef_plot_vars` can be specified at any point, but it is usually easier to see how the names come out first and then choose which metadata variables will go in the coefficient plot and which will go in the heatmap afterwards with `maaslin_plot_results_from_output`.

    # This section is necessary for updating
    # the summary plot and the association plots

    # Rename results file with clean titles
    all_results <- read.csv('hmp2_output/all_results.tsv', sep='\t')
    all_results <- all_results %>%
        mutate(metadata = case_when(metadata == 'age' ~ 'Age',
                                    metadata == 'antibiotics' ~ 'Abx',
                                    metadata == 'diagnosis' ~ 'Diagnosis',
                                    metadata == 'dysbiosis_state' ~ 'Dysbiosis',
                                    metadata == 'reads' ~ 'Read depth'),
            value = case_when(value == 'dysbiosis_CD' ~ 'CD',
                                value == 'dysbiosis_UC' ~ 'UC',
                                value == 'Yes' ~ 'Used', # Antibiotics
                                value == 'age' ~ 'Age',
                                value == 'reads' ~ 'Read depth',
                                TRUE ~ value),
            feature = gsub('_', ' ', feature) %>%
                gsub(pattern = 'sp ', replacement = 'sp. '))

    # Write results
    write.table(all_results, 'hmp2_output/all_results.tsv', sep='\t')

    # Set the new heatmap and coefficient plot variables and order them
    heatmap_vars = c('Dysbiosis UC', 'Diagnosis UC',
                    'Abx Used', 'Age', 'Read depth')
    coef_plot_vars = c('Dysbiosis CD', 'Diagnosis CD')

    # This section is necessary for updating the association plots
    colnames(taxa_table) <- gsub('_', ' ', colnames(taxa_table)) %>%
        gsub(pattern = 'sp ', replacement = 'sp. ')

    # Rename the features in the norm transformed data file
    data_transformed <-
        read.csv('hmp2_output/features/data_transformed.tsv', sep='\t')
    colnames(data_transformed) <-
        gsub('_', ' ', colnames(data_transformed)) %>%
        gsub(pattern = 'sp ', replacement = 'sp. ')
    write.table(data_transformed,
                'hmp2_output/features/data_transformed.tsv',
                sep='\t', row.names = FALSE)

    # Rename the metadata like in the outputs table
    colnames(metadata) <-
        case_when(colnames(metadata) == 'age' ~ 'Age',
                colnames(metadata) == 'antibiotics' ~ 'Abx',
                colnames(metadata) == 'diagnosis' ~ 'Diagnosis',
                colnames(metadata) == 'dysbiosis_state' ~ 'Dysbiosis',
                colnames(metadata) == 'reads' ~ 'Read depth',
                TRUE ~ colnames(metadata))
    metadata <- metadata %>%
        mutate(Dysbiosis = case_when(Dysbiosis == 'dysbiosis_UC' ~ 'UC',
                                    Dysbiosis == 'dysbiosis_CD' ~ 'CD',
                                    Dysbiosis == 'none' ~ 'None') %>%
                factor(levels = c('None', 'UC', 'CD')),
            Abx = case_when(Abx == 'Yes' ~ 'Used',
                            Abx == 'No' ~ 'Not used') %>%
                factor(levels = c('Not used', 'Used')),
            Diagnosis = case_when(Diagnosis == 'nonIBD' ~ 'non-IBD',
                                    TRUE ~ Diagnosis) %>%
                factor(levels = c('non-IBD', 'UC', 'CD')))

    # Recreate the plots
    scatter_plots <- maaslin_plot_results_from_output(
        output = 'hmp2_output',
        metadata,
        normalization = "TSS",
        transform = "LOG",
        median_comparison_abundance = TRUE,
        median_comparison_prevalence = FALSE,
        max_significance = 0.1,
        coef_plot_vars=coef_plot_vars,
        heatmap_vars=heatmap_vars,
        max_pngs = 20)

In the new summary plot below, we can see that the feature names are cleaned up, the metadata names are cleaned up, the set of metadata variables used in the coefficient plot is different, and the metadata used in the heatmap is reordered. (Make sure to set `include=T` to view the plots when knitting the Rmd.)

In the association plots below, the taxa and metadata have been renamed to be consistent with the results file from earlier. To see the plots, set `max_pngs = 250` above and set `eval=TRUE` in this chunk.

#### Contrast testing

We can also perform contrast testing in MaAsLin 3 to check whether a linear combination of the coefficients is significantly different from some constant. Specify a contrast matrix with column names matching the coefficient names and rows with the contrasts of interest. A separate contrast test will be performed for each row.

    contrast_mat <- matrix(c(1, 1, 0, 0, 0, 0, 1, 1),
        ncol = 4, nrow = 2, byrow = TRUE)

    colnames(contrast_mat) <- c("diagnosisUC",
                                "diagnosisCD",
                                "dysbiosis_statedysbiosis_UC",
                                "dysbiosis_statedysbiosis_CD")

    rownames(contrast_mat) <- c("diagnosis_test", "dysbiosis_test")

    contrast_mat

    maaslin_contrast_test(maaslin3_fit = fit_out,
                            contrast_mat = contrast_mat)

#### Command line

MaAsLin 3 can also be run with a command line interface. For example, the first HMP2 analysis can be performed with the following command (the slashes may need to be removed):

    ./R/maaslin3.R \
        inst/extdata/HMP2_taxonomy.tsv \
        inst/extdata/HMP2_metadata.tsv \
        command_line_output \
        --formula='~ diagnosis + dysbiosis_state + antibiotics + age + reads' \
        --reference='diagnosis,nonIBD;dysbiosis_state,none;antibiotics,No'

- Make sure to provide the full path to the MaAsLin3 executable (i.e. `./R/maaslin3.R`).
- In the demo command:
  - `inst/extdata/HMP2_taxonomy.tsv` is the path to your data (or features) file
  - `inst/extdata/HMP2_metadata.tsv` is the path to your metadata file
  - `command_line_output` is the path to the folder to write the output

### Options

From the command line, the following command will print the list of MaAsLin 3 options and default settings:

`$ ./R/maaslin3.R --help`

When running MaAsLin 3 in R, the manual page for each function (e.g., `?maaslin3`) will show the available options and default settings. For both, the options and settings are as follows:

#### Required parameters

- `input_data`: A data frame of feature abundances or read counts or a filepath to a tab-delimited file with abundances. It should be formatted with features as columns and samples as rows (or the transpose). The column and row names should be the feature names and sample names respectively.
- `input_metadata`: A data frame of per-sample metadata or a filepath to a tab-delimited file with metadata. It should be formatted with variables as columns and samples as rows (or the transpose). The column and row names should be the variable names and sample names respectively.
- `output`: The output folder to write results.

#### Model formula

- `formula`: A formula in `lme4` format. Random effects, interactions, and functions of the metadata can be included (note that these functions will be applied after standardization if `standardize = TRUE`). Group, ordered, and strata variables can be specified as: `group(grouping_variable)`, `ordered(ordered_variable)`, and `strata(strata_variable)`. The other variable options below will not be considered if a formula is set.
- `fixed_effects`: A vector of variable names to be included as fixed effects.
  - Fixed effects models are fit with `lm` (linear) or `glm` (logistic).
- `reference`: For a variable with more than two levels supplied with `fixed_effects`, the factor to use as a reference provided as a string of ‘variable,reference’ semi-colon delimited for multiple variables.
- `random_effects`: A vector of variable names to be included as random intercepts. Random intercept models may produce poor model fits when there are fewer than 5 observations per group. In these scenarios, per-group fixed effects should be used and subsequently filtered out. (See `strata_effects` as well.)
  - Random effects models are fit with `lmer` (linear) and `glmer` (logistic), and the significance tests come from `lmerTest` and `glmer` respectively.
- `group_effects`: A factored categorical variable to be included for group testing. An ANOVA-style test will be performed to assess whether any of the variable’s levels are significant, and no coefficients or individual p-values will be returned.
  - Tests are performed with the `anova` function’s `LRT` option (logistic fixed and mixed effects), the `anova` function’s F test (linear fixed effects), or `lmerTest::contest` (linear mixed effects).
- `ordered_effects`: A factored categorical variable to be included. Consecutive levels will be tested for significance against each other with contrast tests, and the resulting associations will correspond to effect sizes, standard errors, and significances of each level versus the previous.
  - Contrast tests are performed with `multcomp::glht` (fixed effects and logistic mixed effects) and `lmerTest::contest` (linear mixed effects).
- `strata_effects`: A single grouping variable to be included in matched case-control studies. If a strata variable is included, no random effects can be included. When a strata variable is included, a conditional logistic regression will be run to account for the strata. The abundance model will be run with a random intercept in place of the strata. Strata can include more than two observations per group. Only variables that differ within the groups can be tested. In general, strata effects are not recommended except for advanced users. Fixed or random intercepts are recommended instead.

#### Feature specific covariates

Particularly for use in metatranscriptomics workflows, a table of feature-specific covariates can be included. A feature’s covariates will be included like a fixed effect metadatum when fitting the model for that feature. The covariate’s name does not need to be included in the formula.

- `feature_specific_covariate`: A table of feature-specific covariates or a filepath to a tab-delimited file with feature-specific covariates. It should be formatted with features as columns and samples as rows (or the transpose). The row names and column names should be the same as those of the `input_data`: the column and row names should be the feature names and sample names respectively. Typically, this table should be generated by `preprocess_dna_mtx` or `preprocess_taxa_mtx` first.
- `feature_specific_covariate_name`: The name for the feature-specific covariates when fitting the models.
- `feature_specific_covariate_record`: Whether to keep the feature-specific covariates in the outputs when calculating p-values, writing results, and displaying plots.

#### Analysis options

- `min_abundance` (default `0`): Features with abundances of more than `min_abundance` in more than `min_prevalence` of the samples will be included for analysis. The threshold is applied after normalization and before transformation.
- `min_prevalence` (default `0`): See above.
- `max_prevalence` (default `1.01`): Features with abundances more than `min_abundance` in fewer than `max_prevalence` of the samples will be included for analysis. The threshold is applied after normalization and before transformation.
- `zero_threshold` (default `0`): Abundances less than or equal to `zero_threshold` will be treated as zeros. This is primarily to be used when the abundance table has likely low-abundance false positives.
- `min_variance` (default `0`): Features with abundance variances less than or equal to `min_variance` will be dropped. This is primarily used for dropping features that are entirely zero.
- `max_significance` (default `0.1`): The FDR corrected q-value threshold for significance used in selecting which associations to write as significant and to plot.
- `normalization` (default `TSS`): The normalization to apply to the features before transformation and analysis. The option `TSS` (total-sum scaling) is recommended, but `CLR` (centered log ratio) and `NONE` can also be used.
- `transform` (default `LOG`, base 2): The transformation to apply to the features after normalization and before analysis. The option `LOG` is recommended, but `PLOG` (pseudo-log with a pseudo-count of half the dataset minimum non-zero abundance replacing zeros, particularly for metabolomics data) and `NONE` can also be used.
- `correction` (default `BH`): The correction to obtain FDR-corrected q-values from raw p-values. Any valid options for `p.adjust` can be used.
- `standardize` (default `TRUE`): Whether to apply z-scores to continuous metadata variables so they are on the same scale. This is recommended in order to compare coefficients across metadata variables, but note that functions of the metadata specified in the `formula` will apply after standardization.
- `warn_prevalence` (default `TRUE`): Warn when prevalence associations are likely induced by abundance associations. This requires re-fitting the linear models on the TSS log-transformed data. A prevalence coefficient is flagged if its corresponding abundance coefficient is significantly different from 0, of the same sign, and larger in absolute value.
- `small_random_effects` (default `FALSE`): Automatically replace random effects with fixed effects in the logistic prevalence model to handle low numbers of observations per group.
- `bypass_small_group_warning` (default `FALSE`): When `TRUE`, bypass the warning that is normally added to results when there are fewer than 4 average observations per random effect group.
- `augment` (default `TRUE`): To avoid linear separability in the logistic regression, at each input data point, add an extra 0 and an extra 1 observation weighted as the number of predictors divided by two times the number of data points. This is almost always recommended to avoid linear separability while having a minor effect on fit coefficients otherwise.
- `evaluate_only` (default `NULL`): To fit only the abundance or only the prevalence models, `evaluate_only` can be set to `abundance` or `prevalence`.

#### Compositionality corrections

##### Absolute abundance

Most microbiome methodologies have historically focused on relative abundances (proportions out of 1). However, some experimental protocols can enable estimation of absolute abundances (cell count/concentration). MaAsLin 3 can be used with two types of absolute abundance estimation: spike-ins and total abundance scaling. In a spike-in procedure, a small, known quantity of a microbe that otherwise would not be present in the sample is added, and the sequencing procedure is carried out as usual. Then, the absolute abundance of a microbe already in the community is estimated as: \[\textrm{Absolute abundance other microbe}=\frac{\textrm{Relative abundance other microbe}}{\textrm{Relative abundance spike-in microbe}}\cdot (\textrm{Absolute abundance spike-in microbe})\] Alternatively, the total microbial abundance of a sample can be determined (e.g., with qPCR of a marker gene or by cell counting). Then, the absolute abundance of a microbe in the community is estimated as: \[\textrm{Absolute abundance microbe}=(\textrm{Total absolute abundance})\cdot(\textrm{Relative abundance microbe})\]

##### Compositionality corrections continued

- `unscaled_abundance`: A data frame with a single column of absolute abundances or a filepath to such a tab-delimited file. The row names should match the names of the samples in `input_data` and `input_metadata`. When using spike-ins, the single column should have the same name as one of the features in `input_data`, and the values should correspond to the absolute quantity of the spike-in. For example, if the same spike-in quantity is used in each sample, the entire column can be set to 1. When using total abundance scaling, the single column should have the name ‘total’, and the values should correspond to the total abundance of each sample. In both cases, `median_comparison_abundance` should be set to `FALSE` since the spike-in or total abundance normalization accounts for compositionality.

Alternatively, if the `input_data` abundances have already been scaled to be absolute abundances, the user should set `normalization = NONE` and `median_comparison_abundance = FALSE` and not include anything for `unscaled_abundance`. Then, the absolute abundances will be log transformed, and models will be fit on those values directly.

##### Median comparisons

When `median_comparison_abundance` or `median_comparison_prevalence` are on, the coefficients for a metadatum will be tested against the median coefficient for that metadatum (median across the features). Otherwise, the coefficients will be tested against 0. For abundance associations, this is designed to account for compositionality, the idea that if only one feature has a positive association with a metadatum on the absolute scale (cell count), the other features will have apparent negative associations with that metadatum on the relative scale (proportion of the community) because relative abundances must sum to 1. More generally, associations on the relative scale are not necessarily the same as the associations on the absolute scale in magnitude or sign, so testing against zero on the relative scale is not equivalent to testing against zero on the absolute scale. When testing associations on the relative scale, the coefficients should be tested against 0 (median comparison off). However, since these tests do not correspond to tests for associations on the absolute scale, we instead use a test against the median, which can enable some inference on the absolute scale. There are two interpretations of this test for absolute abundance associations:

1. In linear models without sparsity (or with sparsity under some assumptions), if two features’ associations with a particular metadatum on the log absolute scale differ by some value \(d\), the features’ associations with that metadatum on the log relative scale (total-sum scaling) will also differ by \(d\). That is, the absolute and relative coefficients for a particular feature-metadatum association are different, but the ordering of the relative coefficients is the same as the ordering of the absolute coefficients for a metadatum, and the difference between two coefficients on the relative scale is the same as the difference between the corresponding coefficients on the absolute scale. Therefore, the test against the relative coefficient median can always be interpreted as “a test of whether a particular association is significantly different from the typical (median) association on the absolute scale.”
2. Under the assumption that at least half the features are not changing on the absolute scale, the median true absolute coefficient is 0, so this can be interpreted as a test of whether the feature has a non-zero association on the absolute scale.

By contrast, sparsity should be less affected by compositionality since a feature should still be present even if another increases or decreases in abundance. (Note that, because the read depth is finite, this might not always be true in practice.) Therefore, `median_comparison_prevalence` is off by default, but it can be turned on if the user is interested in testing whether a particular prevalence association is significantly different from the typical prevalence association.

In both cases, if the tested coefficient is within `median_comparison_[abundance/prevalence]_threshold` of the median, it will automatically receive a p-value of 1. This is based on the idea that the association might be statistically significantly different but not substantially different from the median and therefore is likely still a result of compositionality.

To conclude:

- `median_comparison_abundance` is `TRUE` by default and should be used to make inference on the absolute scale when using relative abundance data. When `median_comparison_abundance` is `TRUE`, only the p-values and q-values change. The coefficients returned are still the relative abundance coefficients unless `subtract_median` is set to `TRUE` in which case the median will be subtracted.
- `median_comparison_abundance` should be `FALSE` when (1) testing for significant relative associations, (2) testing for absolute abundance associations under the assumption that the total absolute abundance is not changing, or (3) testing for significant absolute associations when supplying spike-in or total abundances with `unscaled_abundance`.
- `median_comparison_prevalence` is `FALSE` by default.

##### Compositionality corrections continued

- `median_comparison_abundance` (default `TRUE`): Test abundance coefficients against a null value corresponding to the median coefficient for a metadata variable across the features. Otherwise, test against 0. This is recommended for relative abundance data but should not be used for absolute abundance data.
- `median_comparison_prevalence` (default `FALSE`): Test prevalence coefficients against a null value corresponding to the median coefficient for a metadata variable across the features. Otherwise, test against 0. This is only recommended if the analyst is interested in how feature prevalence associations compare to each other or if there is likely strong compositionality-induced sparsity.
- `median_comparison_abundance_threshold` (default `0`): Coefficients within `median_comparison_abundance_threshold` of the median association will automatically be counted as insignificant (p-value set to 1) since they likely represent compositionality-induced associations. This threshold will be divided by the metadata variable’s standard deviation if the metadatum is continuous to ensure the threshold applies to the right scale.
- `median_comparison_prevalence_threshold` (default `0`): Same as `median_comparison_abundance_threshold` but applied to the prevalence associations.
- `subtract_median` (default `FALSE`): Subtract the median from the coefficients.

#### Plotting parameters

- `plot_summary_plot` (default `TRUE`): Generate a summary plot of significant associations.
- `summary_plot_first_n` (default `25`): Include the top `summary_plot_first_n` features with significant associations.
- `coef_plot_vars`: Vector of variable names to be used in the coefficient plot section of the summary plot. Continuous variables should match the metadata column name, and categorical variables should be of the form `"[variable] [level]"`.

By default, the (up to) two metadata variables with the most significant associations will be plotted in the coefficient plot, and the rest will be plotted in the heatmap. Because predicting the output variable names can be tricky, it is recommended to first run `maaslin3` without setting `coef_plot_vars` or `heatmap_vars`, look at the names of the variables in the summary plot, and then rerun with `maaslin_plot_results_from_output` after updating `coef_plot_vars` and `heatmap_vars` with the desired variables. * `heatmap_vars`: Vector of variable names to be used in the heatmap section of the summary plot. Continuous variables should match the metadata column name, and categorical variables should be of the form `"[variable] [level]"`. * `plot_associations` (default `TRUE`): Whether to generate plots for significant associations. * `max_pngs` (default `30`): The top `max_pngs` significant associations will be plotted.

#### Technical/miscellaneous parameters

- `save_models` (default `FALSE`): Whether to return the fit models and save them to an RData file.
- `verbosity` (default `'FINEST'`): The level of verbosity for MaAsLin 3’s internal logger.
- `save_plots_rds` (default `FALSE`): Whether to save the plots as RDS files.
- `assay.type` (default `1`): A string or index to select the assay when using a `SummarizedExperiment` object.
- Enable parallelization by creating background daemons using mirai e.g. call `mirai::daemons(2)` before running `maaslin3()` to use two cores.

## Tool comparison

Tool | Models differential abundance | \

Models differential prevalence | Models allowed | Accounts for
compositionality | Can use experimental absolute abundance information | |:—:|:—:|:—:|:—:|:—:|:—:| | MaAsLin 3 | Yes | Yes, for any model type | Any lme4 model
(general mixed models), level-vs-level differences, group-wise differences,
feature-specific covariates, contrast tests | Yes | Yes | | MaAsLin 2 | Yes | Only insofar as it affects abundance | Fixed and random
effects (subset of general mixed models) | No | No | | ANCOM-BC2 | Yes | Only when completely absent in a group | Any lme4 model
(general mixed models), level-vs-level differences, group-wise differences,
contrast tests | Yes | No | | ALDEx2 | Yes | Only insofar as it affects abundance | Fixed
effects | Yes | No | | LinDA | Yes | Only insofar as it affects abundance | Any lme4 model
(general mixed models), group-wise differences, contrast tests | Yes | No |

## Troubleshooting

1. Question: When I run from the command line I see the error `maaslin3.R: command not found`. How do I fix this?
   - Answer: Provide the full path to the executable when running maaslin3.R
2. Question: When I run as a function I see the error `Error in library(maaslin3): there is no package called 'maaslin3'`. How do I fix this?
   - Answer: Install the R package and then try loading the library again.

    sessionInfo()

    ## R version 4.6.0 Patched (2026-05-01 r89994)
    ## Platform: aarch64-apple-darwin23
    ## Running under: macOS Tahoe 26.3.1
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] knitr_1.51     ggplot2_4.0.3  dplyr_1.2.1    maaslin3_1.5.1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] sandwich_3.1-1     sass_0.4.10        generics_0.1.4     lattice_0.22-9
    ##  [5] digest_0.6.39      magrittr_2.0.5     evaluate_1.0.5     grid_4.6.0
    ##  [9] RColorBrewer_1.1-3 mvtnorm_1.3-7      fastmap_1.2.0      jsonlite_2.0.0
    ## [13] Matrix_1.7-5       ggnewscale_0.5.2   optparse_1.8.2     survival_3.8-6
    ## [17] multcomp_1.4-30    scales_1.4.0       TH.data_1.1-5      textshaping_1.0.5
    ## [21] codetools_0.2-20   jquerylib_0.1.4    cli_3.6.6          rlang_1.2.0
    ## [25] splines_4.6.0      withr_3.0.2        cachem_1.1.0       yaml_2.3.12
    ## [29] otel_0.2.0         tools_4.6.0        parallel_4.6.0     nanonext_1.9.0
    ## [33] vctrs_0.7.3        R6_2.6.1           zoo_1.8-15         lifecycle_1.0.5
    ## [37] MASS_7.3-65        ragg_1.5.2         pkgconfig_2.0.3    pillar_1.11.1
    ## [41] bslib_0.11.0       gtable_0.3.6       data.table_1.18.4  glue_1.8.1
    ## [45] Rcpp_1.1.1-1.1     systemfonts_1.3.2  collapse_2.1.6     xfun_0.57
    ## [49] tibble_3.3.1       tidyselect_1.2.1   dichromat_2.0-0.1  farver_2.1.2
    ## [53] patchwork_1.3.2    htmltools_0.5.9    mirai_2.7.0        labeling_0.4.3
    ## [57] rmarkdown_2.31     compiler_4.6.0     S7_0.2.2

    # Clean-up generated outputs
    unlink('hmp2_output', recursive = TRUE)