# MaAsLin 3 Tutorial

#### 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

The user manual can be found here. 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 Forum

## Contents

- 1. Installing R
- 2. Installing MaAsLin 3
- 3. Microbiome association detection with MaAsLin 3
  - 3.1 MaAsLin 3 input
  - 3.2 Running MaAsLin 3
  - 3.3 MaAsLin 3 output
- 4. Advanced Topics
  - 4.1 Absolute abundance
  - 4.2 Random Effects (repeated sampling)
  - 4.3 Interactions (differences in differences)
  - 4.4 Level contrasts
  - 4.5 Group-wise differences
  - 4.6 Contrast tests
- 5. Command line

## 1. Installing R

R is a programming language specializing in statistical computing and graphics. You can use R just the same as any other programming languages, but it is most useful for statistical analyses, with well-established packages for common tasks such as linear modeling, ’omic data analysis, machine learning, and visualization.

#### Installing R for the first time

You can download and install the free R software environment here. Note that you should download the latest release - this will ensure the R version you have is compatible with MaAsLin 3.

#### (Optional) the RStudio IDE

RStudio is a freely available IDE (integrated development environment) for R. It is a “wrapper” around R with some additional functionalities that make programming in R a bit easier. Feel free to download RStudio and explore its interface - but it is not required for this tutorial.

#### Important: the correct R version

If you already have R installed, then it is possible that the R version you have does not support MaAsLin 3. The easiest way to check this is to launch R/RStudio, and in the console (“Console” pane in RStudio), type in the following command (without the `>` symbol):

    sessionInfo()

The first line of output message should indicate your current R version. For example:

    > sessionInfo()
    R version 4.4.1 (2024-06-14)

For MaAsLin 3 to install, you will need R >= 4.4. If your version is older than that, please refer to section Installing R for the first time to download the latest R. Note that RStudio users will need to have RStudio “switch” to this later version once it is installed. This should happen automatically for Windows and Mac OS users when you relaunch RStudio. For Linux users you might need to bind the correct R executable. Either way, once you have the correct version installed, launch the software and use `sessionInfo()` to make sure that you indeed have R >= 4.4.

## 2. Installing MaAsLin 3

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`.

## 3. Microbiome association detection with MaAsLin 3

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.

### 3.1 MaAsLin 3 input

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.

Samples with NA values for the metadata will be excluded when fitting the models. It is recommended to drop these samples in advance under a missing-at-random assumption or use multiple imputation to run the complete dataset.

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.

    # 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)

    # Factor the categorical variables to test IBD against healthy controls
    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'))

    taxa_table[1:5, 1:5]

    ##            Phocaeicola_vulgatus Faecalibacterium_prausnitzii
    ## CSM5FZ3N_P            0.4265226                  0.060255109
    ## CSM5FZ3R_P            0.5369584                  0.007396904
    ## CSM5FZ3T_P            0.5911821                  0.000000000
    ## CSM5FZ3V_P            0.2661378                  0.029680329
    ## CSM5FZ3X_P            0.6601039                  0.003596740
    ##            Bacteroides_uniformis Prevotella_copri_clade_A Bacteroides_stercoris
    ## CSM5FZ3N_P           0.269241131               0.0000e+00           0.000000000
    ## CSM5FZ3R_P           0.252604847               0.0000e+00           0.008390958
    ## CSM5FZ3T_P           0.000000000               0.0000e+00           0.000000000
    ## CSM5FZ3V_P           0.400426526               0.0000e+00           0.000000000
    ## CSM5FZ3X_P           0.000880428               1.3081e-05           0.001335669

    metadata[1:5, 1:5]

    ##            participant_id    site_name week_num    reads diagnosis
    ## CSM5FZ3N_P          C3001 Cedars-Sinai        0  9961743        CD
    ## CSM5FZ3R_P          C3001 Cedars-Sinai        2 16456391        CD
    ## CSM5FZ3T_P          C3002 Cedars-Sinai        0 10511448        CD
    ## CSM5FZ3V_P          C3001 Cedars-Sinai        6 17808965        CD
    ## CSM5FZ3X_P          C3002 Cedars-Sinai        2 13160893        CD

### 3.2 Running MaAsLin 3

MaAsLin 3 is run by specifying the abundance table (`input_data`), the metadata table (`input_metadata`), the output directory (`output`), and 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 (section 4.2), interaction terms (section 4.3), 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 (section 4.4) and group predictors (section 4.5) can be included by including `group(variable_name)` and `ordered(variable_name)` in the formula. When the data involve paired or matched case-control samples, conditional logistic regression can be run by specifying the grouping variable with `strata(variable_name)`.
- 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.

#### Running MaAsLin 3 on HMP2 data

The following command runs MaAsLin 3 on the HMP2 data, running a multivariable regression model to test for the association between microbial species abundance and prevalence versus IBD diagnosis and dysbiosis scores while controlling for antibiotic usage, age, and the sampling depth (reads). The abundance associations come from fitting multivariate linear models to the log base 2 relative abundances of features in samples in which the feature is present. The prevalence associations come from fitting multivariate logistic models to the presence/absence data for a feature across all samples. Outputs are directed to a folder called `hmp2_output` under the current working directory (`output = "hmp2_output"`).

To show one of each type of plot (see below), the maximum number of significant associations to plot has been increased with `max_pngs=100` (default `30`). All other parameters beyond the first four are the default parameters but are still included for clarity:

- Total sum scaling (`normalization = 'TSS'`) with a log transformation (`'transform = 'LOG'`, base 2) afterwards will be used. These are almost always the recommended choices (and all MaAsLin 3 evaluations were performed with these options), but other normalizations and transformations are allowed (see `normalization` and `transform` in `?maaslin3`).
- A data augmentation procedure is used to avoid linear separability in the logistic regressions (`augment = TRUE`). This is almost always recommended, though it can be turned off (see `augment` in the manual).
- Continuous metadatum variables are z-score standardized (subtract the mean, divide by the standard deviation) with `standardize = TRUE` so that the coefficients will be on the same scale (improving visualization).
- A nominal FDR level of 0.1 (`max_significance = 0.1`) determines which associations are significant, but this can also be changed (see `max_significance` in the manual).
- For the abundance coefficients, to account for compositionality in relative abundance data, significance is determined by comparing against the median coefficient for a metadatum across the features (`median_comparison_abundance`). Since prevalence coefficients do not have the same compositional properties, they are instead compared against 0 (`median_comparison_prevalence`). See below for a discussion of these parameters.
- One CPU is used for fitting (`cores = 1`).

The abundance, prevalence, and variance filtering parameters are not included, and they are 0 by default. Different from other differential abundance tools, low prevalence features need not be filtered out since the prevalence modeling in MaAsLin 3 already accounts for high proportions of zeros. However, filtering low prevalence features might improve power.

    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 = 10,
                        cores = 1)

By default, many lines of information are printed while the models fit, but the verbosity can be changed with the `verbosity` argument (e.g., `verbosity = 'WARN'`).

MaAsLin 3 can also take as input a `SummarizedExperiment` or `TreeSummarizedExperiment` object from BioConductor:

    se <- SummarizedExperiment(
        assays = list(taxa_table = t(taxa_table)),
        colData = metadata
    )

    fit_out <- maaslin3(input_data = se,
                        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 = 10,
                        cores = 1)

#### 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 testing against zero on the relative scale is not equivalent to testing against zero on the absolute scale. The user manual contains a more extensive discussion of when to use `median_comparison_abundance` and `median_comparison_prevalence`, but in summary:

- `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 `TRUE` in which case the median will be subtracted from the coefficients.
- `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.

### 3.3 MaAsLin 3 output

#### Significant associations

The main output from MaAsLin 3 is the list of significant associations in `significant_results.tsv`. This lists all associations with joint or individual q-values that pass MaAsLin 3’s significance threshold, ordered by increasing individual q-value. The format is:

- `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` and `qval_individual` are the p-value and 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.
- `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.

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Phocaeicola_sartorii | reads | reads | reads | 1.1 | 0 | 0.0787 | 5.42e-44 | 1.06e-40 | 1.08e-43 | 1.14e-40 | NA | prevalence | 1530 | 424
Faecalibacterium_prausnitzii | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.49 | 0 | 0.282 | 3.54e-35 | 3.46e-32 | 7.09e-35 | 3.74e-32 | NA | prevalence | 1530 | 1370
Bifidobacterium_longum | age | age | age | -0.672 | 0 | 0.0594 | 1.01e-29 | 4.94e-27 | 2.02e-29 | 7.12e-27 | NA | prevalence | 1530 | 843
Clostridium_sp_AM49_4BH | diagnosis | CD | diagnosisCD | -1.81 | 0 | 0.163 | 1.11e-28 | 4.34e-26 | 2.22e-28 | 5.87e-26 | NA | prevalence | 1530 | 344
Phocaeicola_dorei | reads | reads | reads | 0.66 | 0 | 0.0634 | 2.56e-25 | 8.35e-23 | 5.13e-25 | 1.08e-22 | NA | prevalence | 1530 | 769
GGB16040_SGB9347 | age | age | age | 0.96 | 0 | 0.0944 | 2.65e-24 | 7.39e-22 | 5.29e-24 | 9.31e-22 | NA | prevalence | 1530 | 114
Firmicutes_bacterium_AF16_15 | diagnosis | UC | diagnosisUC | -1.67 | 0 | 0.166 | 6.77e-24 | 1.65e-21 | 1.35e-23 | 2.04e-21 | NA | prevalence | 1530 | 827
Eubacterium_rectale | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.36 | 0 | 0.237 | 2.47e-23 | 5.36e-21 | 4.94e-23 | 6.51e-21 | NA | prevalence | 1530 | 1210
Phascolarctobacterium_faecium | age | age | age | 0.574 | 0 | 0.0578 | 3.19e-23 | 6.24e-21 | 6.39e-23 | 7.5e-21 | NA | prevalence | 1530 | 503
Dysosmobacter_welbionis | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.4 | 0 | 0.242 | 3.69e-23 | 6.56e-21 | 7.38e-23 | 7.8e-21 | NA | prevalence | 1530 | 1190
Blautia_faecis | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.42 | 0 | 0.249 | 2.66e-22 | 4.33e-20 | 5.31e-22 | 5.1e-20 | NA | prevalence | 1530 | 1120
Fusicatenibacter_saccharivorans | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.35 | 0 | 0.243 | 4.52e-22 | 6.79e-20 | 9.03e-22 | 7.95e-20 | NA | prevalence | 1530 | 1120
Lachnospira_sp_NSJ_43 | diagnosis | CD | diagnosisCD | -1.65 | 0 | 0.177 | 8.94e-21 | 1.16e-18 | 1.79e-20 | 1.45e-18 | NA | prevalence | 1530 | 296
Bacteroides_ovatus | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.13 | 0 | 0.228 | 1.09e-20 | 1.33e-18 | 2.18e-20 | 1.65e-18 | NA | prevalence | 1530 | 1180
Clostridium_sp_AM49_4BH | diagnosis | UC | diagnosisUC | -1.57 | 0 | 0.17 | 2.51e-20 | 2.89e-18 | 5.02e-20 | 3.54e-18 | NA | prevalence | 1530 | 344
Bacteroides_faecis | reads | reads | reads | 0.6 | 0 | 0.0657 | 7.46e-20 | 8.1e-18 | 1.49e-19 | 9.84e-18 | NA | prevalence | 1530 | 434
Dysosmobacter_welbionis | reads | reads | reads | 0.719 | 0 | 0.0798 | 2.1e-19 | 2.05e-17 | 4.2e-19 | 2.61e-17 | NA | prevalence | 1530 | 1190
Roseburia_sp_AF02_12 | diagnosis | CD | diagnosisCD | -1.38 | 0 | 0.154 | 2.44e-19 | 2.27e-17 | 4.87e-19 | 2.86e-17 | NA | prevalence | 1530 | 363
Clostridiales_bacterium | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.46 | 0 | 0.275 | 3.56e-19 | 3.16e-17 | 7.12e-19 | 3.96e-17 | NA | prevalence | 1530 | 985
Alistipes_putredinis | diagnosis | UC | diagnosisUC | -1.42 | 0 | 0.159 | 5.06e-19 | 4.3e-17 | 1.01e-18 | 5.35e-17 | NA | prevalence | 1530 | 873

#### Full output file structure

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

1. Data output files
   - `significant_results.tsv`
     - Described above.
   - `all_results.tsv`
     - Same format as above but for all associations and with an extra column listing any errors from the model fitting.
   - `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. (Make sure to set `include=T` to view the plots when knitting the Rmd.)
   - `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.
     - To see the association plots, set `max_pngs = 250` above and set `eval=TRUE` and `include=TRUE` in this chunk

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

There are a few common issues to check for in the results:

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.
   - With `warn_prevalence=TRUE`, a note will be produced in the error column indicating when prevalence associations are likely induced by abundance associations. These should be checked visually with the diagnosic visuals.
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 and prevalence relationships, 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 diagnostics 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.

#### 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
    taxa_table_copy <- taxa_table
    colnames(taxa_table_copy) <- gsub('_', ' ', colnames(taxa_table_copy)) %>%
        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
    metadata_copy <- metadata
    colnames(metadata_copy) <-
        case_when(colnames(metadata_copy) == 'age' ~ 'Age',
                colnames(metadata_copy) == 'antibiotics' ~ 'Abx',
                colnames(metadata_copy) == 'diagnosis' ~ 'Diagnosis',
                colnames(metadata_copy) == 'dysbiosis_state' ~ 'Dysbiosis',
                colnames(metadata_copy) == 'reads' ~ 'Read depth',
                TRUE ~ colnames(metadata_copy))
    metadata_copy <- metadata_copy %>%
        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 = metadata_copy,
        normalization = "TSS",
        transform = "LOG",
        median_comparison_abundance = TRUE,
        median_comparison_prevalence = FALSE,
        max_significance = 0.1,
        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.

In the association plots, the taxa and metadata have been renamed to be consistent with the results file from earlier.

## 4. Advanced Topics

### 4.1 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: \[\textrm{Absolute abundance microbe}=(\textrm{Total absolute abundance})\cdot(\textrm{Relative abundance microbe})\]

#### Spike-in

The following section shows a synthetic spike-in procedure with simulated data from SparseDOSSA2. The abundance table is like any other abundance table input to MaAsLin 3 except that ‘Feature101’ is the spike-in (which must be present in every sample). The scaling factor data frame has ‘Feature101’ as its only column name with the absolute abundance of the spike-in for each sample (rownames). If the same quantity of the spike-in was added to all samples, this column could be entirely 1 (or any other arbitrary value). When using a spike-in procedure, the scaling factor data frame should have a single column named the same as one of the features in the abundance table, which will be used as the spike-in.

    # Abundance table
    taxa_table_name <- system.file("extdata", "abundance_spike_in_ex.tsv",
                                package = "maaslin3")
    spike_in_taxa_table <- read.csv(taxa_table_name, sep = '\t', row.names = 1)

    # Metadata table
    metadata_name <- system.file("extdata", "metadata_spike_in_ex.tsv",
                                package = "maaslin3")
    spike_in_metadata <- read.csv(metadata_name, sep = '\t', row.names = 1)
    for (col in c('Metadata_1', 'Metadata_2', 'Metadata_5')) {
        spike_in_metadata[,col] <- factor(spike_in_metadata[,col])
    }

    # Spike-in table
    unscaled_name <- system.file("extdata",
    "scaling_factors_spike_in_ex.tsv",
                                package = "maaslin3")
    spike_in_unscaled <- read.csv(unscaled_name, sep = '\t', row.names = 1)

    spike_in_taxa_table[c(1:5, 101),1:5]

    ##            Sample1 Sample2 Sample3 Sample4 Sample5
    ## Feature1         0       0       0       0       0
    ## Feature2         0       0       0       0       0
    ## Feature3       554       0    1737       0       0
    ## Feature4         0       0       0       0       0
    ## Feature5         0       0       0       0       0
    ## Feature101   15464    4788    5155    2052   11540

    spike_in_metadata[1:5,]

    ##         Metadata_1 Metadata_2  Metadata_3 Metadata_4 Metadata_5       ID
    ## Sample1          1          1 -0.41011453  0.6352468          0 Subject1
    ## Sample2          0          1 -1.35105918  0.2362369          0 Subject2
    ## Sample3          1          1 -0.48798215 -0.5458514          0 Subject3
    ## Sample4          0          0 -0.07268457 -0.9903428          1 Subject4
    ## Sample5          1          0  0.27201198  1.1333223          1 Subject5

    spike_in_unscaled[1:5, , drop=FALSE]

    ##         Feature101
    ## Sample1        418
    ## Sample2        183
    ## Sample3         38
    ## Sample4       3230
    ## Sample5        243

The following code fits the absolute abundance model. Note that the normalization must be set to `TSS` since the procedure involves scaling the relative abundance ratios. Also, `median_comparison_abundance` is now set to `FALSE` since we want to test the absolute coefficients against zero. The data frame of absolute abundances is included as the `unscaled_abundance` parameter, and the spike-in strategy will automatically be detected based on the column name.

    fit_out <- maaslin3(
        input_data = spike_in_taxa_table,
        input_metadata = spike_in_metadata,
        output = 'spike_in_demo',
        formula = '~ Metadata_1 + Metadata_2 + Metadata_3 +
            Metadata_4 + Metadata_5',
        normalization = 'TSS',
        transform = 'LOG',
        median_comparison_abundance = FALSE,
        unscaled_abundance = spike_in_unscaled)

The absolute abundance coefficients can be inspected:

    rownames(fit_out$fit_data_abundance$results) <- NULL
    head(fit_out$fit_data_abundance$results, 20) %>%
        dplyr::mutate_if(is.numeric, .funs =
                                function(x){(as.character(signif(x, 3)))}) %>%
        knitr::kable() %>%
        kableExtra::kable_styling("striped", position = 'center') %>%
        kableExtra::scroll_box(width = "800px", height = "400px")

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Feature58 | Metadata_2 | 1 | Metadata_21 | -4.9 | 0 | 0.519 | 7.63e-13 | 5.48e-10 | 7.63e-13 | 3.2e-10 | NA | linear | 100 | 58
Feature88 | Metadata_5 | 1 | Metadata_51 | 7.86 | 0 | 0.819 | 2.99e-12 | 1.08e-09 | 5.99e-12 | 1.26e-09 | NA | linear | 100 | 49
Feature10 | Metadata_1 | 1 | Metadata_11 | -4.19 | 0 | 0.669 | 3.96e-07 | 5.29e-05 | 3.96e-07 | 4.16e-05 | NA | linear | 100 | 40
Feature97 | Metadata_4 | Metadata_4 | Metadata_4 | -2.93 | 0 | 0.475 | 6.2e-06 | 0.000446 | 1.24e-05 | 0.000521 | NA | linear | 100 | 25
Feature56 | Metadata_4 | Metadata_4 | Metadata_4 | 5.93 | 0 | 1 | 1.74e-05 | 0.00104 | 1.74e-05 | 0.000665 | NA | linear | 100 | 23
Feature83 | Metadata_3 | Metadata_3 | Metadata_3 | 5.45 | 0 | 1.07 | 7.55e-05 | 0.00388 | 0.000151 | 0.00423 | NA | linear | 100 | 24
Feature49 | Metadata_1 | 1 | Metadata_11 | -5.45 | 0 | 1.02 | 8.22e-05 | 0.00394 | 8.22e-05 | 0.00246 | NA | linear | 100 | 21
Feature49 | Metadata_5 | 1 | Metadata_51 | -5.6 | 0 | 1.23 | 0.000374 | 0.0117 | 0.000748 | 0.0131 | NA | linear | 100 | 21
Feature64 | Metadata_3 | Metadata_3 | Metadata_3 | -3.23 | 0 | 0.828 | 0.000612 | 0.0169 | 0.00122 | 0.0198 | NA | linear | 100 | 32
Feature84 | Metadata_1 | 1 | Metadata_11 | -4.54 | 0 | 0.919 | 0.000801 | 0.0206 | 0.0016 | 0.0232 | NA | linear | 100 | 15
Feature39 | Metadata_4 | Metadata_4 | Metadata_4 | -3.66 | 0 | 0.943 | 0.000867 | 0.0215 | 8.83e-07 | 6.18e-05 | NA | linear | 100 | 27
Feature67 | Metadata_1 | 1 | Metadata_11 | -2.32 | 0 | 0.661 | 0.00115 | 0.0275 | 0.0023 | 0.0321 | NA | linear | 100 | 46
Feature79 | Metadata_4 | Metadata_4 | Metadata_4 | -1.01 | 0 | 0.314 | 0.0045 | 0.0924 | 0.00898 | 0.122 | NA | linear | 100 | 25
Feature79 | Metadata_3 | Metadata_3 | Metadata_3 | -1.07 | 0 | 0.336 | 0.00471 | 0.0941 | 0.0094 | 0.123 | NA | linear | 100 | 25
Feature37 | Metadata_4 | Metadata_4 | Metadata_4 | 1 | 0 | 0.344 | 0.00525 | 0.102 | 0.0105 | 0.133 | NA | linear | 100 | 62
Feature25 | Metadata_1 | 1 | Metadata_11 | -5.25 | 0 | 0.0484 | 0.00587 | 0.109 | 0.0117 | 0.142 | NA | linear | 100 | 7
Feature44 | Metadata_1 | 1 | Metadata_11 | 3.73 | 0 | 0.957 | 0.00594 | 0.109 | 0.0118 | 0.142 | NA | linear | 100 | 13
Feature15 | Metadata_3 | Metadata_3 | Metadata_3 | 1.93 | 0 | 0.601 | 0.00676 | 0.119 | 0.0135 | 0.149 | NA | linear | 100 | 19
Feature42 | Metadata_2 | 1 | Metadata_21 | 4.08 | 0 | 1.02 | 0.00711 | 0.119 | 0.0142 | 0.149 | NA | linear | 100 | 12
Feature56 | Metadata_3 | Metadata_3 | Metadata_3 | 1.86 | 0 | 0.608 | 0.00712 | 0.119 | 0.0142 | 0.149 | NA | linear | 100 | 23

#### Total abundance scaling

The following section shows a synthetic total abundance scaling procedure with simulated data from SparseDOSSA2. The abundance table is like any other abundance table input to MaAsLin 3 without any extra features. The scaling factor data frame has ‘total’ as its only column name with the total absolute abundance for each sample (rownames).

    # Abundance table
    taxa_table_name <- system.file("extdata", "abundance_total_ex.tsv",
        package = "maaslin3")
    total_scaling_taxa_table <- read.csv(taxa_table_name, sep = '\t', row.names = 1)

    # Metadata table
    metadata_name <- system.file("extdata", "metadata_total_ex.tsv",
                                    package = "maaslin3")
    total_scaling_metadata <- read.csv(metadata_name, sep = '\t', row.names = 1)
    for (col in c('Metadata_1', 'Metadata_3', 'Metadata_5')) {
        spike_in_metadata[,col] <- factor(spike_in_metadata[,col])
    }

    # Total abundance table
    unscaled_name <- system.file("extdata", "scaling_factors_total_ex.tsv",
                                    package = "maaslin3")
    total_scaling_unscaled <- read.csv(unscaled_name, sep = '\t', row.names = 1)

    total_scaling_taxa_table[1:5, 1:5]

    ##          Sample1 Sample2 Sample3 Sample4 Sample5
    ## Feature1   11837       0       0       8      68
    ## Feature2       0       0       0       0       1
    ## Feature3       0       0       0       0       0
    ## Feature4       0       0       1       0       0
    ## Feature5       0       0       0       0       0

    total_scaling_metadata[1:5,]

    ##         Metadata_1 Metadata_2 Metadata_3 Metadata_4 Metadata_5       ID
    ## Sample1          0  1.2410844          0  0.4341439          1 Subject1
    ## Sample2          1  0.0176896          1  1.4894610          1 Subject2
    ## Sample3          1 -1.7370341          1 -0.7973592          1 Subject3
    ## Sample4          1  0.1500793          0  3.2816068          0 Subject4
    ## Sample5          0 -0.2819085          0 -0.6731906          0 Subject5

    total_scaling_unscaled[1:5, , drop=FALSE]

    ##             total
    ## Sample1  1352.276
    ## Sample2  1720.637
    ## Sample3 64194.918
    ## Sample4 50256.129
    ## Sample5  1132.367

The following code fits the absolute abundance model. As before, `TSS` must be set to `TRUE`, `median_comparison_abundance` is set to `FALSE`, and the data frame of absolute abundances is included as the `unscaled_abundance` parameter. The total abundance scaling procedure will be detected based on the column name.

    fit_out <- maaslin3(
        input_data = total_scaling_taxa_table,
        input_metadata = total_scaling_metadata,
        output = 'total_scaling_demo',
        formula = '~ Metadata_1 + Metadata_2 + Metadata_3 +
            Metadata_4 + Metadata_5',
        normalization = 'TSS',
        transform = 'LOG',
        median_comparison_abundance = FALSE,
        unscaled_abundance = total_scaling_unscaled)

The absolute abundance coefficients can be inspected:

    rownames(fit_out$fit_data_abundance$results) <- NULL
    head(fit_out$fit_data_abundance$results, n = 20) %>%
        dplyr::mutate_if(is.numeric,
                        .funs = function(x){(as.character(signif(x, 3)))}) %>%
        knitr::kable() %>%
        kableExtra::kable_styling("striped", position = 'center') %>%
        kableExtra::scroll_box(width = "800px", height = "400px")

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Feature94 | Metadata_1 | Metadata_1 | Metadata_1 | 2.83 | 0 | 0.374 | 3.32e-11 | 2.3e-08 | 6.64e-11 | 2.76e-08 | NA | linear | 100 | 94
Feature80 | Metadata_3 | Metadata_3 | Metadata_3 | 2.86 | 0 | 0.316 | 3.32e-10 | 1.15e-07 | 3.32e-10 | 6.89e-08 | NA | linear | 100 | 37
Feature80 | Metadata_5 | Metadata_5 | Metadata_5 | 2.22 | 0 | 0.296 | 1.92e-08 | 4.44e-06 | 3.85e-08 | 5.32e-06 | NA | linear | 100 | 37
Feature66 | Metadata_2 | Metadata_2 | Metadata_2 | 3.83 | 0 | 0.56 | 1.92e-07 | 1.9e-05 | 1.92e-07 | 1.14e-05 | NA | linear | 100 | 34
Feature80 | Metadata_1 | Metadata_1 | Metadata_1 | -2.09 | 0 | 0.311 | 1.69e-07 | 1.9e-05 | 1.69e-07 | 1.14e-05 | NA | linear | 100 | 37
Feature4 | Metadata_1 | Metadata_1 | Metadata_1 | 2.87 | 0 | 0.434 | 1.53e-06 | 0.000106 | 3.06e-06 | 0.000127 | NA | linear | 100 | 27
Feature45 | Metadata_3 | Metadata_3 | Metadata_3 | -1.82 | 0 | 0.408 | 0.000144 | 0.00712 | 0.000288 | 0.00796 | NA | linear | 100 | 32
Feature101 | Metadata_1 | Metadata_1 | Metadata_1 | 1.12 | 0 | 0.287 | 0.000178 | 0.0077 | 0.000178 | 0.00568 | NA | linear | 100 | 100
Feature8 | Metadata_2 | Metadata_2 | Metadata_2 | 3.33 | 0 | 0.656 | 0.000168 | 0.0077 | 0.000337 | 0.00874 | NA | linear | 100 | 20
Feature5 | Metadata_4 | Metadata_4 | Metadata_4 | 4.92 | 0 | 1.23 | 0.00205 | 0.0678 | 0.00205 | 0.0406 | NA | linear | 100 | 17
Feature45 | Metadata_2 | Metadata_2 | Metadata_2 | -2.08 | 0 | 0.645 | 0.00341 | 0.102 | 0.00109 | 0.025 | NA | linear | 100 | 32
Feature68 | Metadata_3 | Metadata_3 | Metadata_3 | -2.27 | 0 | 0.717 | 0.00569 | 0.146 | 0.0113 | 0.168 | NA | linear | 100 | 23
Feature75 | Metadata_5 | Metadata_5 | Metadata_5 | -3.29 | 0 | 1.08 | 0.00645 | 0.16 | 0.0128 | 0.184 | NA | linear | 100 | 26
Feature86 | Metadata_1 | Metadata_1 | Metadata_1 | 0.974 | 0 | 0.337 | 0.00671 | 0.16 | 0.0134 | 0.185 | NA | linear | 100 | 39
Feature44 | Metadata_4 | Metadata_4 | Metadata_4 | -0.565 | 0 | 0.218 | 0.011 | 0.23 | 0.011 | 0.168 | NA | linear | 100 | 100
Feature79 | Metadata_2 | Metadata_2 | Metadata_2 | 1.9 | 0 | 0.724 | 0.013 | 0.257 | 0.0258 | 0.335 | NA | linear | 100 | 40
Feature33 | Metadata_4 | Metadata_4 | Metadata_4 | -0.986 | 0 | 0.391 | 0.0152 | 0.291 | 0.0302 | 0.379 | NA | linear | 100 | 53
Feature75 | Metadata_3 | Metadata_3 | Metadata_3 | 2.71 | 0 | 1.09 | 0.0215 | 0.381 | 0.00702 | 0.127 | NA | linear | 100 | 26
Feature91 | Metadata_2 | Metadata_2 | Metadata_2 | -1.21 | 0 | 0.511 | 0.0263 | 0.444 | 0.0518 | 0.597 | NA | linear | 100 | 29
Feature20 | Metadata_2 | Metadata_2 | Metadata_2 | -5.14 | 0 | 1.67 | 0.0272 | 0.445 | 0.0537 | 0.602 | NA | linear | 100 | 11

#### Computationally estimated absolute abundance

When using a purely computational approach to estimate absolute abundance (e.g., Nishijima et al. Cell 2024 with the function `MLP`), the abundance table can be estimated and then run with the `normalization = 'NONE'` to avoid scaling the results.

### 4.2 Random effects (clustered samples)

Certain studies have a natural “grouping” of sample observations, such as by subject in longitudinal designs or by family in family designs. It is important for statistic analysis to address the non-independence between samples belonging to the same group. MaAsLin 3 provides a simple interface for this with random effects, where the user can specify the grouping variable to run a mixed effect model. This grouping variable can be provided with the `random_effects` parameter or specified in the model with `(1|grouping_variable)`. This will add in a “random intercept,” a per-group adjustment to the model intercept. More complicated random effects can be specified in the formula in accordance with `lme4` formula parsing. For example, HMP2 has a longitudinal design where the same subject (column `participant_id`) can have multiple samples. We thus use `participant_id` as a random effect grouping variable in the model.

    # Subset to only CD cases for time; taxa are subset automatically
    fit_out <- maaslin3(
        input_data = taxa_table,
        input_metadata = metadata[metadata$diagnosis == 'CD',],
        output = 'random_effects_output',
        formula = '~ dysbiosis_state + antibiotics +
            age + reads + (1|participant_id)',
        plot_summary_plot = FALSE,
        plot_associations = FALSE)

    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')
    ## boundary (singular) fit: see help('isSingular')

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Phocaeicola_sartorii | reads | reads | reads | 1.56 | 0 | 0.185 | 3.11e-17 | 3.38e-14 | 6.22e-17 | 3.62e-14 | NA | prevalence | 685 | 205
Faecalibacterium_prausnitzii | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -4.89 | 0 | 0.692 | 1.68e-12 | 9.1e-10 | 3.35e-12 | 9.74e-10 | NA | prevalence | 685 | 573
Bacteroides_eggerthii | reads | reads | reads | 1.61 | 0 | 0.246 | 5.55e-11 | 2.01e-08 | 1.11e-10 | 2.15e-08 | NA | prevalence | 685 | 119
Blautia_faecis | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.56 | 0 | 0.4 | 1.5e-10 | 4.06e-08 | 2.99e-10 | 4.34e-08 | NA | prevalence | 685 | 469
Bacteroides_uniformis | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.48 | 0 | 0.556 | 4.19e-10 | 7.58e-08 | 8.38e-10 | 8.12e-08 | NA | prevalence | 685 | 553
Dysosmobacter_welbionis | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.87 | 0 | 0.458 | 3.74e-10 | 7.58e-08 | 7.49e-10 | 8.12e-08 | NA | prevalence | 685 | 524
Fusicatenibacter_saccharivorans | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.49 | 0 | 0.401 | 5.13e-10 | 7.95e-08 | 1.03e-09 | 8.51e-08 | NA | prevalence | 685 | 484
Parabacteroides_distasonis | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.27 | 0 | 0.533 | 8.06e-10 | 1.09e-07 | 1.61e-09 | 1.17e-07 | NA | prevalence | 685 | 484
Clostridium_fessum | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.59 | 0 | 0.592 | 1.29e-09 | 1.39e-07 | 2.57e-09 | 1.49e-07 | NA | prevalence | 685 | 391
Clostridium_sp_AF34_10BH | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.3 | 0 | 0.543 | 1.19e-09 | 1.39e-07 | 2.38e-09 | 1.49e-07 | NA | prevalence | 685 | 377
Eubacterium_rectale | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.82 | 0 | 0.469 | 1.86e-09 | 1.83e-07 | 3.72e-09 | 1.96e-07 | NA | prevalence | 685 | 514
Phocaeicola_dorei | reads | reads | reads | 0.828 | 0 | 0.139 | 2.94e-09 | 2.66e-07 | 5.87e-09 | 2.84e-07 | NA | prevalence | 685 | 310
Clostridiales_bacterium | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.88 | 0 | 0.668 | 6.33e-09 | 5.28e-07 | 1.27e-08 | 5.66e-07 | NA | prevalence | 685 | 407
Clostridium_sp_AT4 | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | 3.07 | -0.594 | 0.593 | 1.89e-08 | 1.47e-06 | 3.79e-08 | 1.47e-06 | NA | abundance | 685 | 229
Escherichia_coli | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | 1.96 | -0.594 | 0.407 | 3.73e-08 | 2.53e-06 | 3.73e-08 | 1.47e-06 | NA | abundance | 685 | 327
Anaerostipes_hadrus | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.31 | 0 | 0.421 | 3.7e-08 | 2.53e-06 | 7.39e-08 | 2.69e-06 | NA | prevalence | 685 | 468
Roseburia_inulinivorans | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -2.25 | 0 | 0.413 | 4.85e-08 | 3.1e-06 | 9.71e-08 | 3.32e-06 | NA | prevalence | 685 | 349
Bacteroides_faecis | reads | reads | reads | 1.05 | 0 | 0.194 | 5.74e-08 | 3.46e-06 | 1.15e-07 | 3.7e-06 | NA | prevalence | 685 | 203
Bacteroides_thetaiotaomicron | dysbiosis_state | dysbiosis_CD | dysbiosis_statedysbiosis_CD | -3.28 | 0 | 0.608 | 6.73e-08 | 3.84e-06 | 1.35e-07 | 4.11e-06 | NA | prevalence | 685 | 453
Ruthenibacterium_lactatiformans | reads | reads | reads | 0.62 | 0 | 0.115 | 7.66e-08 | 4.15e-06 | 1.53e-07 | 4.45e-06 | NA | prevalence | 685 | 336

Note that no `participant_id` terms are included in the outputs; the random intercepts are just used to control for grouping. If you are interested in testing the effect of time in a longitudinal study, the time point variable should be included as a fixed effect in your MaAsLin 3 formula.

#### Alternatives to random effects

Random effects are not the solution to every correlated data problem. In particular, logistic random intercept models may produce poor model fits when there are fewer than 4 average observations per random effect group. The fit coefficients can be large in magnitude and very significant while modeling the data poorly. By default, a warning will be added to the `error` column of the outputs in these scenarios. This warning can be disregarded if the user is confident the model is fit correctly, but recommendations are provided in the table below:

Scenario | \(\geq4\) average observations per group | \(<4\) average observations per group, metadata not collinear with the groups | \(<4\) average observations per group, metadata collinear with the groups
--- | --- | --- | ---
Example | Subjects sampled \(\geq4\) times | Subjects sampled once before and once after treatment | Subjects have fixed disease status and are sampled twice (disease status is constant per-subject, so collinear with the grouping)
Action | Use random effects with `small_random_effects=FALSE` (default) | Use random effects for abundance but fixed effects for prevalence by setting `small_random_effects=TRUE` | Subset or average data to one observation per group
Reason | Random effects are robust and increase power | Prevalence random effects are not robust but fixed effects are robust and control for grouping | Prevalence random effects are not robust and fixed effects cannot be used when groups are collinear with the metadata

### 4.3 Interactions (differences in differences)

One of the benefits of MaAsLin 3’s formula mode is the ability to include interaction terms. Mathematically, an interaction term corresponds to the product of two columns in the design matrix. When a continuous variable is interacted with a categorical term, the interaction term corresponds to the change in the continuous variable’s slope between the categories. For two categorical variables interacted, see below; it is better explained through an example. In the formula, interactions can be specified with the `:` symbol to include only the interaction term(s) or the `*` symbol to include both the interaction term and the non-interacted terms (i.e., `a*b` adds the terms `a`, `b`, and `a:b`).

    metadata <- metadata %>%
        mutate(dysbiosis_general = ifelse(dysbiosis_state != 'none',
                                        'dysbiosis', 'none')) %>%
        mutate(dysbiosis_general = factor(dysbiosis_general, levels =
                                            c('none', 'dysbiosis')))

    fit_out <- maaslin3(
        input_data = taxa_table,
        input_metadata = metadata,
        output = 'interaction_output',
        formula = '~ diagnosis + diagnosis:dysbiosis_general +
            antibiotics + age + reads')

    full_results <- rbind(fit_out$fit_data_abundance$results,
                        fit_out$fit_data_prevalence$results)
    full_results <- full_results %>%
        dplyr::arrange(qval_joint) %>%
        dplyr::filter(metadata == "diagnosis")
    rownames(full_results) <- NULL
    head(full_results, n = 20) %>%
        dplyr::mutate_if(is.numeric,
                        .funs = function(x){(as.character(signif(x, 3)))}) %>%
        knitr::kable() %>%
        kableExtra::kable_styling("striped", position = 'center') %>%
        kableExtra::scroll_box(width = "800px", height = "400px")

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Faecalibacterium_prausnitzii | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -2.18 | -0.218 | 0.309 | 1.18e-06 | 1.03e-05 | 7.2e-35 | 3.8e-32 | NA | linear | 1530 | 1370
Faecalibacterium_prausnitzii | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -3.48 | 0 | 0.282 | 3.6e-35 | 3.52e-32 | 7.2e-35 | 3.8e-32 | NA | logistic | 1530 | 1370
Clostridium_sp_AM49_4BH | diagnosis | CD | diagnosisCD | -0.68 | -0.0689 | 0.344 | 0.108 | 0.197 | 2.31e-28 | 6.1e-26 | NA | linear | 1530 | 344
Clostridium_sp_AM49_4BH | diagnosis | CD | diagnosisCD | -1.81 | 0 | 0.163 | 1.15e-28 | 4.51e-26 | 2.31e-28 | 6.1e-26 | NA | logistic | 1530 | 344
Firmicutes_bacterium_AF16_15 | diagnosis | UC | diagnosisUC | -0.47 | 0.0724 | 0.246 | 0.0924 | 0.174 | 1.4e-23 | 2.12e-21 | NA | linear | 1530 | 827
Firmicutes_bacterium_AF16_15 | diagnosis | UC | diagnosisUC | -1.67 | 0 | 0.166 | 7.01e-24 | 1.71e-21 | 1.4e-23 | 2.12e-21 | NA | logistic | 1530 | 827
Eubacterium_rectale | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -0.602 | -0.218 | 0.386 | 0.395 | 0.533 | 5.15e-23 | 6.79e-21 | NA | linear | 1530 | 1210
Eubacterium_rectale | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -2.36 | 0 | 0.237 | 2.57e-23 | 5.59e-21 | 5.15e-23 | 6.79e-21 | NA | logistic | 1530 | 1210
Dysosmobacter_welbionis | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | 0.7 | -0.218 | 0.342 | 0.0316 | 0.075 | 7.7e-23 | 8.13e-21 | NA | linear | 1530 | 1190
Dysosmobacter_welbionis | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -2.39 | 0 | 0.241 | 3.85e-23 | 6.84e-21 | 7.7e-23 | 8.13e-21 | NA | logistic | 1530 | 1190
Blautia_faecis | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | 1.03 | -0.218 | 0.474 | 0.0211 | 0.0532 | 5.45e-22 | 5.23e-20 | NA | linear | 1530 | 1120
Blautia_faecis | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -2.42 | 0 | 0.249 | 2.72e-22 | 4.44e-20 | 5.45e-22 | 5.23e-20 | NA | logistic | 1530 | 1120
Fusicatenibacter_saccharivorans | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | 0.311 | -0.218 | 0.4 | 0.263 | 0.393 | 9.33e-22 | 8.21e-20 | NA | linear | 1530 | 1120
Fusicatenibacter_saccharivorans | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -2.35 | 0 | 0.243 | 4.67e-22 | 7.01e-20 | 9.33e-22 | 8.21e-20 | NA | logistic | 1530 | 1120
Lachnospira_sp_NSJ_43 | diagnosis | CD | diagnosisCD | -0.403 | -0.0689 | 0.401 | 0.437 | 0.577 | 1.86e-20 | 1.51e-18 | NA | linear | 1530 | 296
Lachnospira_sp_NSJ_43 | diagnosis | CD | diagnosisCD | -1.65 | 0 | 0.177 | 9.28e-21 | 1.21e-18 | 1.86e-20 | 1.51e-18 | NA | logistic | 1530 | 296
Bacteroides_ovatus | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | 0.278 | -0.218 | 0.429 | 0.316 | 0.454 | 2.25e-20 | 1.7e-18 | NA | linear | 1530 | 1180
Bacteroides_ovatus | diagnosis | CD:dysbiosis_generaldysbiosis | diagnosisCD:dysbiosis_generaldysbiosis | -2.13 | 0 | 0.228 | 1.13e-20 | 1.37e-18 | 2.25e-20 | 1.7e-18 | NA | logistic | 1530 | 1180
Clostridium_sp_AM49_4BH | diagnosis | UC | diagnosisUC | 0.136 | 0.0724 | 0.357 | 0.877 | 0.931 | 5.16e-20 | 3.64e-18 | NA | linear | 1530 | 344
Clostridium_sp_AM49_4BH | diagnosis | UC | diagnosisUC | -1.57 | 0 | 0.169 | 2.58e-20 | 2.97e-18 | 5.16e-20 | 3.64e-18 | NA | logistic | 1530 | 344

In the model above, we have converted `dysbiosis_CD` and `dysbiosis_UC` into `dysbiosis` in the variable `dysbiosis_general` and then specified an interaction between `diagnosis` and `dysbiosis_general` with `diagnosis:dysbiosis_general`. Since `diagnosis` has 3 levels itself (`nonIBD`, `UC`, `CD`), this will produce five terms (`name` column) for each feature:

- `diagnosisCD` is the difference between non-dysbiotic CD and non-IBD
- `diagnosisUC` is the difference between non-dysbiotic UC and non-IBD
- `diagnosisCD:dysbiosis_generaldysbiosis` (the interaction of `diagnosisCD` and `dysbiosis_generaldysbiosis`) is the difference between non-dysbiotic CD and dysbiotic CD
- `diagnosisnonIBD:dysbiosis_generaldysbiosis` (the interaction of `diagnosisnonIBD` and `dysbiosis_generaldysbiosis`) is the difference between dysbiotic and non-dysbiotic non-IBD. Note that these coefficients are all NA since no subjects labeled non-IBD were marked as being dysbiotic.
- `diagnosisUC:dysbiosis_generaldysbiosis` (the interaction of `diagnosisUC` and `dysbiosis_generaldysbiosis`) is the difference between non-dysbiotic UC and dysbiotic UC

Since we have effectively fit the original model in a different way, this output table is equivalent to the original output table but with new names in the columns `metadata`, `value`, and `name` based on the interaction terms.

### 4.4 Level contrasts

Another feature of MaAsLin 3 is the ability to test for level-versus-level differences in ordered predictors with contrast tests. Ordered predictors are categorical variables with a natural ordering such as cancer stage, consumption frequency of a dietary factor, or dosage group. Here, we assess how microbial abundances and prevalences are associated with eating red meat by including `ordered(red_meat)` in the formula. This will perform a contrast test for whether there is a difference between each pair of subsequent levels (e.g., “Yesterday, 3 or more times” versus “Yesterday, 1 to 2 times”) rather than whether there are differences between the levels and the baseline (e.g., “Yesterday, 3 or more times” versus “Not in the last 7 days”). The coefficient, standard error, and p-value all correspond to the difference between the level in `value` and the previous level. Ordered predictors should only be included as fixed effects (i.e., no ordered predictors as random effects etc.).

    # Put the red meat consumption responses in order
    metadata <- metadata %>%
        mutate(red_meat = ifelse(
            red_meat == 'No, I did not consume these products in the last 7 days',
                                'Not in the last 7 days',
                                red_meat) %>%
                factor(levels = c('Not in the last 7 days',
                                    'Within the past 4 to 7 days',
                                    'Within the past 2 to 3 days',
                                    'Yesterday, 1 to 2 times',
                                    'Yesterday, 3 or more times'))
        )

    # Create the model with only non-IBD subjects
    fit_out <- maaslin3(
        input_data = taxa_table,
        input_metadata = metadata[metadata$diagnosis == 'nonIBD',],
        output = 'ordered_outputs',
        formula = '~ ordered(red_meat) + antibiotics + age + reads',
        plot_summary_plot = TRUE,
        plot_associations = TRUE,
        heatmap_vars = c('red_meat Within the past 4 to 7 days',
                        'red_meat Within the past 2 to 3 days',
                        'red_meat Yesterday, 1 to 2 times',
                        'red_meat Yesterday, 3 or more times'),
        max_pngs = 30)

If we look at the resulting heatmap, we can identify the Alistipes shahii prevalence association as potentially interesting since it increases in prevalence with more meat consumption in all but one level-versus-level comparison.

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Alistipes_shahii | red_meat | Within the past 4 to 7 days | red_meatWithin the past 4 to 7 days | 1.47 | 0 | 0.353 | 3.16e-05 | 0.00113 | 6.31e-05 | 0.00141 | NA | logistic | 405 | 266
Alistipes_shahii | red_meat | Within the past 2 to 3 days | red_meatWithin the past 2 to 3 days | -0.67 | 0 | 0.316 | 0.0341 | 0.194 | 0.067 | 0.23 | NA | logistic | 405 | 266
Alistipes_shahii | red_meat | Yesterday, 1 to 2 times | red_meatYesterday, 1 to 2 times | 1.07 | 0 | 0.291 | 0.000241 | 0.0064 | 0.000483 | 0.00788 | NA | logistic | 405 | 266
Alistipes_shahii | red_meat | Yesterday, 3 or more times | red_meatYesterday, 3 or more times | 3.29 | 0 | 3.83 | 0.391 | 0.68 | 0.011 | 0.0688 | NA | logistic | 405 | 266

### 4.5 Group-wise differences

MaAsLin 3 can also test for group-wise differences in categorical predictors using an ANOVA or ANOVA-like procedure. Group-wise predictors are categorical variables with or without an ordering such as race, country, or consumption frequency of a dietary factor. When performing a group-wise difference test, we are not comparing any two particular levels; rather, we are investigating whether abundances and prevalences are the same for all levels. Therefore, no coefficient is returned, only a p-value. Here, we test whether there are differences in microbial abundances and prevalences across people with different levels of red meat consumption by including `group(red_meat)`. Group-wise predictors should only be included as fixed effects (i.e., no group-wise predictors as random effects etc.).

    fit_out <- maaslin3(
        input_data = taxa_table,
        input_metadata = metadata[metadata$diagnosis == 'nonIBD',],
        output = 'group_outputs',
        formula = '~ group(red_meat) + antibiotics + age + reads',
        plot_summary_plot = TRUE,
        plot_associations = TRUE,
        heatmap_vars = c('red_meat Within the past 4 to 7 days',
                        'red_meat Within the past 2 to 3 days',
                        'red_meat Yesterday, 1 to 2 times',
                        'red_meat Yesterday, 3 or more times'),
        max_pngs = 200)

If we look at the same Alistipes shahii association from before, we see that no coefficient or standard error is returned for a group predictor, but the q-value is very low. This corroborates the observation earlier that there are differences in Alistipes shahii prevalence with red meat consumption.

feature | metadata | value | name | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Alistipes_shahii | red_meat | NA | red_meat | NA | 0 | NA | 5.86e-09 | 5.88e-07 | 1.17e-08 | 7.29e-07 | NA | logistic | 405 | 266

### 4.6 Contrast tests

We can also perform contrast testing in MaAsLin 3 to check whether a linear combination of the coefficients is significantly different from some constant on the right hand size (RHS). Specify a contrast matrix with column names matching the coefficient names and rows with the contrasts of interest. For each row \(C^T\), the hypothesis \(H_0:C^T\vec{\beta}=RHS\) will be tested. If no RHS is specified, a median test will be performed, and the median test RHS will be returned.

Below, we test whether the UC and CD diagnosis coefficients are the same and whether the UC and CD dysbiosis coefficients are the same. The contrast matrix can easily be extended to test any groups pairwise.

    fit_out <- maaslin3(input_data = taxa_table,
                        input_metadata = metadata,
                        output = 'contrast_test_output',
                        formula = '~ diagnosis + dysbiosis_state +
                            antibiotics + age + reads',
                        plot_summary_plot = FALSE,
                        plot_associations = FALSE,
                        cores = 1)

    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

    ##                diagnosisUC diagnosisCD dysbiosis_statedysbiosis_UC
    ## diagnosis_test           1          -1                           0
    ## dysbiosis_test           0           0                           1
    ##                dysbiosis_statedysbiosis_CD
    ## diagnosis_test                           0
    ## dysbiosis_test                          -1

    contrast_out <- maaslin_contrast_test(maaslin3_fit = fit_out,
                            contrast_mat = contrast_mat)

    head(contrast_out$fit_data_abundance$results, n = 20) %>%
        knitr::kable() %>%
        kableExtra::kable_styling("striped", position = 'center') %>%
        kableExtra::scroll_box(width = "800px", height = "400px")

| feature | test | coef | null_hypothesis | stderr | pval_individual | qval_individual | pval_joint | qval_joint | error | model | N | N_not_zero
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
53 | Bacteroides_xylanisolvens | diagnosis_test | -2.4403105 | 0.1097498 | 0.2968232 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | NA | abundance | 1527 | 959
121 | Enterocloster_clostridioformis | diagnosis_test | -2.7495172 | 0.1097498 | 0.4500242 | 0.0000000 | 0.0000003 | 0.0000000 | 0.0000004 | NA | abundance | 1527 | 312
213 | Odoribacter_splanchnicus | diagnosis_test | -1.1375733 | 0.1097498 | 0.1802169 | 0.0000000 | 0.0000005 | 0.0000000 | 0.0000000 | NA | abundance | 1527 | 757
101 | Collinsella_aerofaciens | diagnosis_test | 1.5445086 | 0.1097498 | 0.2302233 | 0.0000000 | 0.0000016 | 0.0000001 | 0.0000020 | NA | abundance | 1527 | 698
57 | Bifidobacterium_adolescentis | diagnosis_test | 1.8824133 | 0.1097498 | 0.3667912 | 0.0000053 | 0.0001412 | 0.0000000 | 0.0000004 | NA | abundance | 1527 | 427
83 | Clostridium_sp_1001270H_150608_G6 | diagnosis_test | -3.9516621 | 0.1097498 | 0.8484539 | 0.0000053 | 0.0001412 | 0.0000106 | 0.0001781 | NA | abundance | 1527 | 141
193 | Gemmiger_formicilis | diagnosis_test | 1.8824604 | 0.1097498 | 0.3716797 | 0.0000068 | 0.0001583 | 0.0000135 | 0.0001960 | NA | abundance | 1527 | 472
32 | Bacteroides_faecis | dysbiosis_test | 9.7878623 | 0.4497588 | 2.0386351 | 0.0000076 | 0.0001610 | 0.0000152 | 0.0001995 | NA | abundance | 1527 | 434
296 | Veillonella_parvula | dysbiosis_test | -4.0265984 | 0.4497588 | 1.0180798 | 0.0000306 | 0.0005251 | 0.0000612 | 0.0006600 | NA | abundance | 1527 | 547
124 | Escherichia_coli | dysbiosis_test | -2.8138188 | 0.4497588 | 0.7262272 | 0.0000385 | 0.0006200 | 0.0000769 | 0.0007743 | NA | abundance | 1527 | 707
105 | Coprococcus_eutactus | diagnosis_test | 2.7183981 | 0.1097498 | 0.6196658 | 0.0000553 | 0.0008403 | 0.0001106 | 0.0010435 | NA | abundance | 1527 | 177
45 | Bacteroides_salyersiae | diagnosis_test | 2.3003213 | 0.1097498 | 0.5235943 | 0.0000710 | 0.0010497 | 0.0001421 | 0.0013000 | NA | abundance | 1527 | 150
113 | Dysosmobacter_sp_BX15 | diagnosis_test | -0.8551854 | 0.1097498 | 0.2176687 | 0.0000984 | 0.0012768 | 0.0001968 | 0.0016062 | NA | abundance | 1527 | 621
85 | Clostridium_sp_AF20_17LB | diagnosis_test | 1.1123688 | 0.1097498 | 0.2314552 | 0.0001169 | 0.0014807 | 0.0002338 | 0.0018579 | NA | abundance | 1527 | 654
17 | Alistipes_putredinis | diagnosis_test | -0.8184855 | 0.1097498 | 0.2225484 | 0.0002258 | 0.0026699 | 0.0000001 | 0.0000018 | NA | abundance | 1527 | 873
15 | Alistipes_onderdonkii | diagnosis_test | -0.9337860 | 0.1097498 | 0.2608339 | 0.0002541 | 0.0029392 | 0.0005082 | 0.0035693 | NA | abundance | 1527 | 831
28 | Bacteroides_cellulosilyticus | dysbiosis_test | 7.0002025 | 0.4497588 | 1.8108308 | 0.0003975 | 0.0039897 | 0.0007948 | 0.0046158 | NA | abundance | 1527 | 433
79 | Clostridium_fessum | diagnosis_test | -0.6570721 | 0.1097498 | 0.1883810 | 0.0005192 | 0.0046038 | 0.0003954 | 0.0029856 | NA | abundance | 1527 | 926
119 | Enterocloster_bolteae | diagnosis_test | -0.7711082 | 0.1097498 | 0.2273751 | 0.0005606 | 0.0048107 | 0.0011210 | 0.0059392 | NA | abundance | 1527 | 866
27 | Bacteroides_cellulosilyticus | diagnosis_test | -1.2287327 | 0.1097498 | 0.3707219 | 0.0006115 | 0.0051634 | 0.0012225 | 0.0063656 | NA | abundance | 1527 | 433

## 5. 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

## 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

    sessionInfo()

    ## R Under development (unstable) (2025-03-02 r87868)
    ## Platform: x86_64-apple-darwin20
    ## Running under: macOS Monterey 12.7.6
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
    ##
    ## 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] kableExtra_1.4.0 knitr_1.50       ggplot2_3.5.1    dplyr_1.1.4
    ## [5] maaslin3_0.99.16
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] Rdpack_2.6.3                    pbapply_1.7-2
    ##   [3] sandwich_3.1-1                  rlang_1.1.5
    ##   [5] magrittr_2.0.3                  multcomp_1.4-28
    ##   [7] matrixStats_1.5.0               compiler_4.5.0
    ##   [9] mgcv_1.9-1                      systemfonts_1.2.1
    ##  [11] vctrs_0.6.5                     stringr_1.5.1
    ##  [13] pkgconfig_2.0.3                 crayon_1.5.3
    ##  [15] fastmap_1.2.0                   XVector_0.47.2
    ##  [17] labeling_0.4.3                  rmarkdown_2.29
    ##  [19] UCSC.utils_1.3.1                nloptr_2.2.1
    ##  [21] ragg_1.3.3                      purrr_1.0.4
    ##  [23] xfun_0.51                       cachem_1.1.0
    ##  [25] GenomeInfoDb_1.43.4             jsonlite_1.9.1
    ##  [27] DelayedArray_0.33.6             BiocParallel_1.41.2
    ##  [29] parallel_4.5.0                  R6_2.6.1
    ##  [31] bslib_0.9.0                     stringi_1.8.4
    ##  [33] RColorBrewer_1.1-3              boot_1.3-31
    ##  [35] numDeriv_2016.8-1.1             GenomicRanges_1.59.1
    ##  [37] jquerylib_0.1.4                 Rcpp_1.0.14
    ##  [39] SummarizedExperiment_1.37.0     zoo_1.8-13
    ##  [41] IRanges_2.41.3                  Matrix_1.7-3
    ##  [43] splines_4.5.0                   tidyselect_1.2.1
    ##  [45] rstudioapi_0.17.1               abind_1.4-8
    ##  [47] yaml_2.3.10                     TreeSummarizedExperiment_2.15.0
    ##  [49] codetools_0.2-20                lmerTest_3.1-3
    ##  [51] lattice_0.22-6                  tibble_3.2.1
    ##  [53] plyr_1.8.9                      Biobase_2.67.0
    ##  [55] treeio_1.31.0                   withr_3.0.2
    ##  [57] evaluate_1.0.3                  survival_3.8-3
    ##  [59] getopt_1.20.4                   xml2_1.3.8
    ##  [61] Biostrings_2.75.4               pillar_1.10.1
    ##  [63] MatrixGenerics_1.19.1           stats4_4.5.0
    ##  [65] reformulas_0.4.0                generics_0.1.3
    ##  [67] S4Vectors_0.45.4                munsell_0.5.1
    ##  [69] scales_1.3.0                    tidytree_0.4.6
    ##  [71] minqa_1.2.8                     glue_1.8.0
    ##  [73] lazyeval_0.2.2                  tools_4.5.0
    ##  [75] ggnewscale_0.5.1                lme4_1.1-37
    ##  [77] fs_1.6.5                        mvtnorm_1.3-3
    ##  [79] grid_4.5.0                      optparse_1.7.5
    ##  [81] tidyr_1.3.1                     ape_5.8-1
    ##  [83] rbibutils_2.3                   colorspace_2.1-1
    ##  [85] SingleCellExperiment_1.29.2     nlme_3.1-167
    ##  [87] GenomeInfoDbData_1.2.14         patchwork_1.3.0
    ##  [89] cli_3.6.4                       textshaping_1.0.0
    ##  [91] S4Arrays_1.7.3                  viridisLite_0.4.2
    ##  [93] svglite_2.1.3                   gtable_0.3.6
    ##  [95] yulab.utils_0.2.0               logging_0.10-108
    ##  [97] sass_0.4.9                      digest_0.6.37
    ##  [99] BiocGenerics_0.53.6             SparseArray_1.7.7
    ## [101] TH.data_1.1-3                   farver_2.1.2
    ## [103] htmltools_0.5.8.1               lifecycle_1.0.4
    ## [105] httr_1.4.7                      MASS_7.3-65

    # Clean-up
    unlink('hmp2_output', recursive = TRUE)
    unlink('spike_in_demo', recursive = TRUE)
    unlink('total_scaling_demo', recursive = TRUE)
    unlink('random_effects_output', recursive = TRUE)
    unlink('interaction_output', recursive = TRUE)
    unlink('ordered_outputs', recursive = TRUE)
    unlink('group_outputs', recursive = TRUE)
    unlink('contrast_test_output', recursive = TRUE)