Maaslin2 | R Documentation |

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,
heatmap_first_n = 50,
plot_scatter = TRUE,
max_pngs = 10,
save_scatter = FALSE,
save_models = FALSE,
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. |

`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. |

List 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)
```

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