# Method to get the default table to use.

`degDefault.Rd`

It can accept a list of new padj values matching the same dimmensions than the current vector. It can calculate the `lfdr` based on fdrtool::fdrtool function.

    degDefault(object)

    degCorrect(object, fdr)

    # S4 method for DEGSet
    degDefault(object)

    # S4 method for DEGSet
    degCorrect(object, fdr)

## Arguments

object | DEGSet
fdr | It can be `fdr-stat`, `fdr-pvalue`, vector of new padj
object | DEGSet

## Examples

    library(DESeq2)
    library(dplyr)#>
    #> Attaching package: ‘dplyr’#> The following object is masked from ‘package:matrixStats’:
    #>
    #>     count#> The following object is masked from ‘package:Biobase’:
    #>
    #>     combine#> The following objects are masked from ‘package:GenomicRanges’:
    #>
    #>     intersect, setdiff, union#> The following object is masked from ‘package:GenomeInfoDb’:
    #>
    #>     intersect#> The following objects are masked from ‘package:IRanges’:
    #>
    #>     collapse, desc, intersect, setdiff, slice, union#> The following objects are masked from ‘package:S4Vectors’:
    #>
    #>     first, intersect, rename, setdiff, setequal, union#> The following objects are masked from ‘package:BiocGenerics’:
    #>
    #>     combine, intersect, setdiff, union#> The following objects are masked from ‘package:stats’:
    #>
    #>     filter, lag#> The following objects are masked from ‘package:base’:
    #>
    #>     intersect, setdiff, setequal, uniondds <- makeExampleDESeqDataSet(betaSD=1)
    colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12)
    design(dds) <-  ~ condition + treatment
    dds <- DESeq(dds)#> estimating size factors#> estimating dispersions#> gene-wise dispersion estimates#> mean-dispersion relationship#> final dispersion estimates#> fitting model and testingres <- degComps(dds, contrast = list("treatment_B_vs_A"))#> Doing 1 element(s).#> Doing results() for each element.#> Doing lcfSrink() for each element.#> using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
    #>
    #> Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
    #> See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
    #> Reference: https://doi.org/10.1093/bioinformatics/bty895degCorrect(res, fdr = "lfdr-stat")#> Step 1... determine cutoff point
    #> Step 2... estimate parameters of null distribution and eta0
    #> Step 3... compute p-values and estimate empirical PDF/CDF
    #> Step 4... compute q-values and local fdr
    #>
    #> Step 1... determine cutoff point
    #> Step 2... estimate parameters of null distribution and eta0
    #> Step 3... compute p-values and estimate empirical PDF/CDF
    #> Step 4... compute q-values and local fdr
    #> #> Comparisons: treatment.B.vs.A
    #> Results in comparison: raw,shrunken
    #> Default is: shrunken