Maaslin2 {Maaslin2}R Documentation

MaAsLin2 is the next generation of MaAsLin. MaAsLin is a multivariate statistical framework that finds associations between clinical metadata and potentially high-dimensional experimental data.

Description

MaAsLin2 was developed to find associations between microbiome multi'omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods, normalization, and transform options to customize analysis for your specific study.

Usage

Maaslin2(
    input_data,
    input_metadata,
    output,
    min_abundance = 0.0,
    min_prevalence = 0.1,
    normalization = "TSS",
    transform = "LOG",
    analysis_method = "LM",
    max_significance = 0.25,
    random_effects = NULL,
    fixed_effects = NULL,
    correction = "BH",
    standardize = TRUE,
    cores = 1,
    plot_heatmap = TRUE,
    plot_scatter = TRUE,
    heatmap_first_n = 50
)

Arguments

input_data

The tab-delimited input file of features.

input_metadata

The tab-delimited input file of metadata.

output

The output folder to write results.

min_abundance

The minimum abundance for each feature.

min_prevalence

The minimum percent of samples for which a feature is detected at minimum abundance.

max_significance

The q-value threshold for significance.

normalization

The normalization method to apply.

transform

The transform to apply.

analysis_method

The analysis method to apply.

random_effects

The random effects for the model, comma-delimited for multiple effects.

fixed_effects

The fixed effects for the model, comma-delimited for multiple effects.

correction

The correction method for computing the q-value.

standardize

Apply z-score so continuous metadata are on the same scale.

plot_heatmap

Generate a heatmap for the significant associations.

heatmap_first_n

In heatmap, plot top N features with significant associations.

plot_scatter

Generate scatter plots for the significant associations.

cores

The number of R processes to run in parallel.

Value

Data.frame containing the results from applying the model.

Author(s)

Himel Mallick<hmallick@broadinstitute.org>,
Ali Rahnavard<rah@broadinstitute.org>,
Maintainers: Lauren McIver<lauren.j.mciver@gmail.com>,

Examples

    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', transform = "AST",
        fixed_effects = c('diagnosis', 'dysbiosisnonIBD','dysbiosisUC','dysbiosisCD', 'antibiotics', 'age'),
        random_effects = c('site', 'subject'),
        standardize = FALSE)

[Package Maaslin2 version 1.2.0 Index]