Maaslin2 {Maaslin2} | R Documentation |
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.
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 )
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. |
Data.frame containing the results from applying the model.
Himel Mallick<hmallick@broadinstitute.org>,
Ali Rahnavard<rah@broadinstitute.org>,
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'), standardize = FALSE)