Package 'Maaslin2'

MaAsLin2 is the next generation of MaAsLin, a multivariable statistical framework for finding associations between clinical metadata and potentially high-dimensional microbial multi-omics data.

Description

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.

Usage

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,
    heatmap_first_n = 50,
    plot_scatter = TRUE,
    max_pngs = 10,
    save_scatter = FALSE,
    save_models = FALSE,
    reference = NULL
)

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.
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.
max_pngs	Set the maximum number of scatter plots for signficant associations to save as png files.
save_scatter	Save all scatter plot ggplot objects to an RData file.
cores	The number of R processes to run in parallel.
save_models	Return the full model outputs and save to an RData file.
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.

Value

List containing the results from applying the model.

Author(s)

Himel Mallick<[email protected]>,
Ali Rahnavard<[email protected]>,
Maintainers: Lauren McIver<[email protected]>,

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

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