MaAsLin 3

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 accomodates 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

Introduction
Support
Contents
Requirements
Installation
Running MaAsLin 3
Input data
Output files
Run a demo
Options
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.

for (lib in c('maaslin3', 'dplyr', 'ggplot2', 'knitr')) {
    suppressPackageStartupMessages(require(lib, character.only = 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.

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.
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).

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.
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

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.
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.
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).
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. This should take about a minute.

# 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
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 = 100,
                    save_models = TRUE)

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:

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.”
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 the logging package.
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.
One core is used by default. You can enable parallelization by creating background daemons using the mirai package e.g. call mirai::daemons(2) before running the maaslin3() command to use two cores.

Troubleshooting

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
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.

Manuscript evaluation code

The manuscript evaluation code can be found here.

MaAsLin 3

Support

Contents

Requirements

Installation

Install using GitHub and devtools

Running MaAsLin 3

Input data

Output files

Diagnostics

Run a demo

In R

Command line

Options

Required parameters

Model formula

Feature specific covariates

Analysis options

Compositionality corrections

Absolute abundance

Compositionality corrections continued

Median comparisons

Compositionality corrections continued

Plotting parameters

Technical/miscellaneous parameters

Troubleshooting

Manuscript evaluation code