Maaslin2: MaAsLin2 is the next generation of MaAsLin, a multivariable...
In Maaslin2: Maaslin2

Description Usage Arguments Value Author(s) Examples

View source: R/Maaslin2.R

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

Maaslin2(
    input_data,
    input_metadata,
    output,
    min_abundance = 0.0,
    min_prevalence = 0.1,
    min_variance = 0.0,
    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,
    reference = NULL
)

`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.
`min_variance`	Keep features with variance greater than.
`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.
`reference`	The factor to use as a reference for a variable with more than two levels provided as a string of 'variable,reference' semi-colon delimited for multiple variables.

Data.frame containing the results from applying the model.

Himel Mallick<himel.stat.iitk@gmail.com>,
Ali Rahnavard<gholamali.rahnavard@gmail.com>,
Maintainers: Lauren McIver<lauren.j.mciver@gmail.com>,

    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'),
        normalization = 'NONE',
        reference = 'diagnosis,nonIBD',
        standardize = FALSE)