MaAsLin 3 finds associations between microbiome meta-omics features and complex metadata. It uses a unified framework to test for both abundance (using linear models) and prevalence (using logistic regression).
Usage
step_maaslin(
rec,
min_abundance = 0,
min_prevalence = 0.1,
min_variance = 0,
normalization = "TSS",
transform = "LOG",
max_significance = 0.1,
random_effects = NULL,
correction = "BH",
standardize = TRUE,
reference = NULL,
median_comparison_abundance = TRUE,
rarefy = FALSE,
id = rand_id("maaslin"),
engine_args = list()
)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", "NONE".
- transform
The transform to apply. Default: "LOG" (Base 2). Choices: "LOG", "PLOG", "NONE".
- max_significance
The q-value threshold for significance.
- random_effects
The random effects for the model (vector of character strings).
- 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.
- median_comparison_abundance
Test abundance coefficients against a null value corresponding to the median coefficient for a metadata variable across the features. Recommended for relative abundance (default: TRUE).
- rarefy
Boolean indicating if OTU counts must be rarefyed.
- id
A character string that is unique to this step to identify it.
- engine_args
Named lists of advanced arguments for the native
fitorcontraststage. Arguments managed by dar or exposed above cannot be overridden.
See also
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_deseq(),
step_lefse(),
step_linda(),
step_wilcox()
Examples
data(metaHIV_phy)
## Init Recipe
rec <-
recipe(metaHIV_phy) |>
add_model(~ RiskGroup2, targets = "RiskGroup2", tax_level = "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
#>
#> Statistical model:
#>
#> ℹ ~RiskGroup2
#>
#> Preprocessing steps:
#>
#> ◉ step_subset_taxa() id = subset_taxa__Crocetta_of_Caltanissetta
#> ◉ step_filter_taxa() id = filter_taxa__Makroudh
#>
#> 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__Strudel diff_taxa = 1
#>
#> ℹ 1 taxon-contrast effects are present in all tested methods
#>
## Wearing rarefaction only for this step
rec <-
recipe(metaHIV_phy) |>
add_model(~ RiskGroup2, targets = "RiskGroup2", tax_level = "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
#>
#> Statistical model:
#>
#> ℹ ~RiskGroup2
#>
#> Preprocessing steps:
#>
#>
#> DA steps:
#>
#> ◉ step_maaslin() id = maaslin__Punschkrapfen