R: MaAsLin2 is the next generation of MaAsLin. MaAsLin is a...

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 1.1.1 Index]