MaAsLin2 User Manual

MaAsLin2 is the next generation of MaAsLin (Microbiome Multivariable Association with Linear Models).

MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta-omics features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, along with a variety of filtering, normalization, and transform methods.

If you use the MaAsLin2 software, please cite our manuscript:

Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, Tickle TL, Weingart G, Ren B, Schwager EH, Chatterjee S, Thompson KN, Wilkinson JE, Subramanian A, Lu Y, Waldron L, Paulson JN, Franzosa EA, Bravo HC, Huttenhower C (2021). Multivariable Association Discovery in Population-scale Meta-omics Studies. PLoS Computational Biology, 17(11):e1009442.

Check out the MaAsLin 2 tutorial for an overview of analysis options.

If you have questions, please direct it to :
MaAsLin2 Forum
Google Groups (Read only)



MaAsLin2 finds associations between microbiome multi-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (with support for multiple covariates and repeated measures), filtering, normalization, and transform options to customize analysis for your specific study.


MaAsLin2 is an R package that can be run on the command line or as an R function.


MaAsLin2 can be run from the command line or as an R function. If only running from the command line, you do not need to install the MaAsLin2 package but you will need to install the MaAsLin2 dependencies.

From command line

  1. Download the source: MaAsLin2.tar.gz
  2. Decompress the download:
    • $ tar xzvf maaslin2.tar.gz
  3. Install the Bioconductor dependencies edgeR and metagenomeSeq.
  4. Install the CRAN dependencies:
    • $ R -q -e "install.packages(c('lmerTest','pbapply','car','dplyr','vegan','chemometrics','ggplot2','pheatmap','hash','logging','data.table','glmmTMB','MASS','cplm','pscl'), repos='')"
  5. Install the MaAsLin2 package (only r,equired if running as an R function):
    • $ R CMD INSTALL maaslin2

From R

Install Bioconductor and then install Maaslin2

if(!requireNamespace("BiocManager", quietly = TRUE))

How to Run

MaAsLin2 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.

Input Files

MaAsLin2 requires two input files.

  1. Data (or features) file
    • This file is tab-delimited.
    • Formatted with features as columns and samples as rows.
    • The transpose of this format is also okay.
    • Possible features in this file include taxonomy or genes.
  2. Metadata file
    • This file is tab-delimited.
    • Formatted with features as columns and samples as rows.
    • The transpose of this format is also okay.
    • Possible metadata in this file include gender or age.

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.

NOTE: If running MaAsLin2 as a function, the data and metadata inputs can be of type data.frame instead of a path to a file.

Output Files

MaAsLin2 generates two types of output files: data and visualization.

  1. Data output files
    • all_results.tsv
      • This includes the same data as the data.frame returned.
      • This file contains all results ordered by increasing q-value.
      • The first columns are the metadata and feature names.
      • The next two columns are the value and coefficient from the model.
      • The next column is the standard deviation from the model.
      • The N column is the total number of data points.
      • The column is the total of non-zero data points.
      • The pvalue from the calculation is the second to last column.
      • The qvalue is computed with p.adjust with the correction method.
    • significant_results.tsv
      • This file is a subset of the results in the first file.
      • It only includes associations with q-values <= 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.rds
      • This file contains a list with every model fit object.
      • It will only be generated if save_models is set to TRUE.
    • residuals.rds
      • This file contains a data frame with residuals for each feature.
    • fitted.rds
      • This file contains a data frame with fitted values for each feature.
    • ranef.rds
      • This file contains a data frame with extracted random effects for each feature (when random effects are specified).
    • maaslin2.log
      • This file contains all log information for the run.
      • It includes all settings, warnings, errors, and steps run.
  2. Visualization output files
    • heatmap.pdf
      • This file contains a heatmap of the significant associations.
    • [a-z/0-9]+.pdf
      • A plot is generated for each significant association.
      • Scatter plots are used for continuous metadata.
      • Box plots are for categorical data.
      • Data points plotted are after filtering but prior to normalization and transform.

Run a Demo

Example input files can be found in the inst/extdata folder of the MaAsLin2 source. The files provided were generated from the HMP2 data which can be downloaded from .

HMP2_taxonomy.tsv: is a tab-demilited file with species as columns and samples as rows. It is a subset of the taxonomy file so it just includes the species abundances for all samples.

HMP2_metadata.tsv: is a tab-delimited file with samples as rows and metadata as columns. It is a subset of the metadata file so that it just includes some of the fields.

Command line

$ Maaslin2.R --fixed_effects="diagnosis,dysbiosisnonIBD,dysbiosisUC,dysbiosisCD,antibiotics,age" --random_effects="site,subject" --standardize=FALSE inst/extdata/HMP2_taxonomy.tsv inst/extdata/HMP2_metadata.tsv demo_output

In R

input_data <- system.file(
    'extdata','HMP2_taxonomy.tsv', package="Maaslin2")
input_metadata <-system.file(
    'extdata','HMP2_metadata.tsv', package="Maaslin2")
fit_data <- Maaslin2(
    input_data, input_metadata, 'demo_output',
    fixed_effects = c('diagnosis', 'dysbiosisnonIBD','dysbiosisUC','dysbiosisCD', 'antibiotics', 'age'),
    random_effects = c('site', 'subject'),
    reference = "diagnosis,nonIBD",
    standardize = FALSE)
Session Info

Session info from running the demo in R can be displayed with the following command.



Run MaAsLin2 help to print a list of the options and the default settings.

$ Maaslin2.R --help Usage: ./R/Maaslin2.R [options]

Options: -h, --help Show this help message and exit

    The minimum abundance for each feature [ Default: 0 ]

    The minimum percent of samples for which a feature 
    is detected at minimum abundance [ Default: 0.1 ]

-b MIN_VARIANCE, --min_variance=MIN_VARIANCE
    Keep features with variance greater than [ Default: 0.0 ]

    The q-value threshold for significance [ Default: 0.25 ]

    The normalization method to apply [ Default: TSS ]
    [ Choices: TSS, CLR, CSS, NONE, TMM ]

    The transform to apply [ Default: LOG ]
    [ Choices: LOG, LOGIT, AST, NONE ]

    The analysis method to apply [ Default: LM ]
    [ Choices: LM, CPLM, NEGBIN, ZINB ]

    The random effects for the model, comma-delimited
    for multiple effects [ Default: none ]

    The fixed effects for the model, comma-delimited
    for multiple effects [ Default: all ]

    The correction method for computing the 
    q-value [ Default: BH ]

    Apply z-score so continuous metadata are 
    on the same scale [ Default: TRUE ]

-l PLOT_HEATMAP, --plot_heatmap=PLOT_HEATMAP
    Generate a heatmap for the significant 
    associations [ Default: TRUE ]

-i HEATMAP_FIRST_N, --heatmap_first_n=HEATMAP_FIRST_N
    In heatmap, plot top N features with significant 
    associations [ Default: TRUE ]

-o PLOT_SCATTER, --plot_scatter=PLOT_SCATTER
    Generate scatter plots for the significant
    associations [ Default: TRUE ]

-g MAX_PNGS, --max_pngs=MAX_PNGS
    The maximum number of scatter plots for signficant associations 
    to save as png files [ Default: 10 ]

    Save all scatter plot ggplot objects
    to an RData file [ Default: FALSE ]

-e CORES, --cores=CORES
    The number of R processes to run in parallel
    [ Default: 1 ]

-j SAVE_MODELS --save_models=SAVE_MODELS
    Return the full model outputs and save to an RData file
    [ Default: FALSE ]

    The factor to use as a reference level for a categorical variable 
    provided as a string of 'variable,reference', semi-colon delimited for 
    multiple variables. Not required if metadata is passed as a factor or 
    for variables with less than two levels but can be set regardless.
    [ Default: NA ]


  1. Question: When I run from the command line I see the error Maaslin2.R: command not found. How do I fix this?
    • Answer: Provide the full path to the executable when running Maaslin2.R.
  2. Question: When I run as a function I see the error Error in library(Maaslin2): there is no package called 'Maaslin2'. How do I fix this?
    • Answer: Install the R package and then try loading the library again.
  3. Question: When I try to install the R package I see errors about dependencies not being installed. Why is this?
    • Answer: Installing the R package will not automatically install the packages MaAsLin2 requires. Please install the dependencies and then install the MaAsLin2 R package.

biobakery/Maaslin2 documentation built on July 16, 2024, 3:53 p.m.