metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
Usage
step_metagenomeseq(
rec,
zeroMod = NULL,
useCSSoffset = TRUE,
useMixedModel = FALSE,
max_significance = 0.05,
log2FC = 0,
rarefy = FALSE,
rm_zeros = 0,
id = rand_id("metagenomeseq")
)
# S4 method for class 'Recipe'
step_metagenomeseq(
rec,
zeroMod = NULL,
useCSSoffset = TRUE,
useMixedModel = FALSE,
max_significance = 0.05,
log2FC = 0,
rarefy = FALSE,
rm_zeros = 0,
id = rand_id("metagenomeseq")
)
# S4 method for class 'PrepRecipe'
step_metagenomeseq(
rec,
zeroMod = NULL,
useCSSoffset = TRUE,
useMixedModel = FALSE,
max_significance = 0.05,
log2FC = 0,
rarefy = FALSE,
rm_zeros = 0,
id = rand_id("metagenomeseq")
)Arguments
- rec
A Recipe object. The step will be added to the sequence of operations for this Recipe.
- zeroMod
The zero model, the model to account for the change in the number of OTUs observed as a linear effect of the depth of coverage.
- useCSSoffset
Boolean, whether to include the default scaling parameters in the model or not.
- useMixedModel
Estimate the correlation between duplicate features or replicates using duplicateCorrelation.
- max_significance
The q-value threshold for significance.
- log2FC
log2FC cutoff.
- 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.
- rm_zeros
Proportion of samples of the same categorical level with more than 0 counts.
- 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_maaslin(),
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) >= (0.02 * 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__Trdelník
#> ◉ step_filter_taxa() id = filter_taxa__Cronut
#>
#> DA steps:
#>
## Define step with default parameters and prep
rec <-
step_metagenomeseq(rec, rm_zeros = 0.01) |>
prep(parallel = FALSE)
#> Registered S3 method overwritten by 'gplots':
#> method from
#> reorder.factor DescTools
#> Warning: NaNs produced
rec
#> ── DAR Results ─────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 291 taxa and 156 samples
#> ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid)
#> ℹ taxonomic level Phylum
#>
#> Results:
#>
#> ✔ metagenomeseq__Schneeball diff_taxa = 8
#>
#> ℹ 8 taxa are present in all tested methods
#>
## Wearing rarefaction only for this step
rec <-
recipe(metaHIV_phy, "RiskGroup2", "Species") |>
step_metagenomeseq(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_metagenomeseq() id = metagenomeseq__Croissant