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

View source: R/Maaslin2.R

Maaslin2R Documentation

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<himel.stat.iitk@gmail.com>,
Ali Rahnavard<gholamali.rahnavard@gmail.com>,
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'),
        normalization = 'NONE',
        reference = 'diagnosis,nonIBD',
        standardize = FALSE)

biobakery/maaslin2 documentation built on July 14, 2024, 6:38 a.m.