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
step_maaslin(
rec,
min_abundance = 0,
min_prevalence = 0.1,
min_variance = 0,
normalization = "TSS",
transform = "LOG",
analysis_method = "LM",
max_significance = 0.25,
random_effects = NULL,
correction = "BH",
standardize = TRUE,
reference = NULL,
rarefy = FALSE,
id = rand_id("maaslin")
)
# S4 method for class 'Recipe'
step_maaslin(
rec,
min_abundance = 0,
min_prevalence = 0.1,
min_variance = 0,
normalization = "TSS",
transform = "LOG",
analysis_method = "LM",
max_significance = 0.25,
random_effects = NULL,
correction = "BH",
standardize = TRUE,
reference = NULL,
rarefy = FALSE,
id = rand_id("maaslin")
)
# S4 method for class 'PrepRecipe'
step_maaslin(
rec,
min_abundance = 0,
min_prevalence = 0.1,
min_variance = 0,
normalization = "TSS",
transform = "LOG",
analysis_method = "LM",
max_significance = 0.25,
random_effects = NULL,
correction = "BH",
standardize = TRUE,
reference = NULL,
rarefy = FALSE,
id = rand_id("maaslin")
)Arguments
- rec
A Recipe object. The step will be added to the sequence of operations for this Recipe.
- 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.
- normalization
The normalization method to apply. Default: "TSS". Choices: "TSS", "CLR", "CSS", "NONE", "TMM".
- transform
The transform to apply. Default: "LOG". Choices: "LOG", "LOGIT", "AST", "NONE".
- analysis_method
The analysis method to apply. Default: "LM". Choices: "LM", "CPLM", "ZICP", "NEGBIN", "ZINB".
- max_significance
The q-value threshold for significance.
- random_effects
The random 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.
- 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.
- rarefy
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed.
- id
A character string that is unique to this step to identify it.
See also
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_deseq(),
step_lefse(),
step_metagenomeseq(),
step_wilcox()
Examples
data(metaHIV_phy)
## Init Recipe
rec <-
recipe(metaHIV_phy, "RiskGroup2", "Phylum") |>
step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |>
step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))")
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 451 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Phylum
#>
#> Preporcessing steps:
#>
#> ◉ step_subset_taxa() id = subset_taxa__Mandelkubb
#> ◉ step_filter_taxa() id = filter_taxa__Joulutorttu
#>
#> DA steps:
#>
## Define step with default parameters and prep
rec <-
step_maaslin(rec) |>
prep(parallel = FALSE)
rec
#> ── DAR Results ─────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 76 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Phylum
#>
#> Results:
#>
#> ✔ maaslin__Knish diff_taxa = 65
#>
#> ℹ 65 taxa are present in all tested methods
#>
## Wearing rarefaction only for this step
rec <-
recipe(metaHIV_phy, "RiskGroup2", "Species") |>
step_maaslin(rarefy = TRUE)
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 451 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Species
#>
#> Preporcessing steps:
#>
#>
#> DA steps:
#>
#> ◉ step_maaslin() id = maaslin__Tortita_negra