Differential expression analysis based on the Negative Binomial (a.k.a.
Gamma-Poisson) distribution. This function performs a default analysis
through the steps: 1) estimation of size factors: estimateSizeFactors. 2)
estimation of dispersion: estimateDispersions. 3) Negative Binomial GLM
fitting and Wald statistics: nbinomWaldTest. For complete details on each
step, see the manual pages of the respective functions. After the DESeq
function returns a DESeqDataSet object, results tables (log2 fold changes and
p-values) can be generated using the results function. Shrunken LFC can then
be generated using the lfcShrink function.
Usage
step_deseq(
rec,
test = "Wald",
fitType = "local",
betaPrior = FALSE,
type = "ashr",
max_significance = 0.05,
log2FC = 0,
rarefy = FALSE,
id = rand_id("deseq")
)
# S4 method for class 'Recipe'
step_deseq(
rec,
test = "Wald",
fitType = "local",
betaPrior = FALSE,
type = "ashr",
max_significance = 0.05,
log2FC = 0,
rarefy = FALSE,
id = rand_id("deseq")
)
# S4 method for class 'PrepRecipe'
step_deseq(
rec,
test = "Wald",
fitType = "local",
betaPrior = FALSE,
type = "ashr",
max_significance = 0.05,
log2FC = 0,
rarefy = FALSE,
id = rand_id("deseq")
)Arguments
- rec
A Recipe object. The step will be added to the sequence of operations for this Recipe.
- test
Either "Wald" or "LRT", which will then use either Wald significance tests (defined by nbinomWaldTest), or the likelihood ratio test on the difference in deviance between a full and reduced model formula (defined by nbinomLRT).
- fitType
either "parametric", "local", "mean", or "glmGamPoi" for the type of fitting of dispersions to the mean intensity. See estimateDispersions for description.
- betaPrior
whether or not to put a zero-mean normal prior on the non-intercept coefficients See nbinomWaldTest for description of the calculation of the beta prior. In versions >=1.16, the default is set to FALSE, and shrunken LFCs are obtained afterwards using lfcShrink.
- type
"apeglm" is the adaptive Student's t prior shrinkage estimator from the 'apeglm' package; "ashr" is the adaptive shrinkage estimator from the 'ashr' package, using a fitted mixture of normals prior - see the Stephens (2016) reference below for citation; "normal" is the 2014 DESeq2 shrinkage estimator using a Normal prior.
- 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.
- 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_lefse(),
step_maaslin(),
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__Haddekuche
#> ◉ step_filter_taxa() id = filter_taxa__Schuxen
#>
#> DA steps:
#>
## Define step with default parameters and prep
rec <-
step_deseq(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:
#>
#> ✔ deseq__Viennoiserie diff_taxa = 2
#>
#> ℹ 2 taxa are present in all tested methods
#>
## Wearing rarefaction only for this step
rec <-
recipe(metaHIV_phy, "RiskGroup2", "Species") |>
step_deseq(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_deseq() id = deseq__Chatti_Pathiri