# Print Summary Statistics of Alpha Level Cutoffs

`degSummary.Rd`

Print Summary Statistics of Alpha Level Cutoffs

    degSummary(object, alpha = c(0.1, 0.05, 0.01), contrast = NULL,
      caption = "", kable = FALSE)

## Arguments

object | Can be DEGSet or DESeqDataSet or DESeqResults.
alpha | Numeric vector of desired alpha cutoffs.
contrast | Character vector to use with `results()` function.
caption | Character vector to add as caption to the table.
kable | Whether return a `knitr::kable()` output. Default is data.frame.

## Value

data.frame or `knitr::kable()`.

## References

- original idea of multiple alpha values and code syntax from Michael Steinbaugh.

## Examples

    library(DESeq2)
    data(humanGender)
    idx <- c(1:5, 75:80)
    counts <- assays(humanGender)[[1]]
    dse <- DESeqDataSetFromMatrix(counts[1:1000, idx],
                                  colData(humanGender)[idx,],
                                  design = ~group)
    dse <- DESeq(dse)#> using pre-existing size factors#> estimating dispersions#> gene-wise dispersion estimates#> mean-dispersion relationship#> final dispersion estimates#> fitting model and testingres1 <- results(dse)
    res2 <- degComps(dse, contrast = c("group_Male_vs_Female"))#> 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/bty895degSummary(dse, contrast = "group_Male_vs_Female")#>                                         0.1                         0.05
    #> LFC > 0 (up)   LFC > 0 (up)       : 4, 0.4% LFC > 0 (up)       : 4, 0.4%
    #> LFC < 0 (down) LFC < 0 (down)     : 3, 0.3% LFC < 0 (down)     : 3, 0.3%
    #> outliers       outliers [1]       : 1, 0.1% outliers [1]       : 1, 0.1%
    #> low counts       low counts [2]     : 0, 0%   low counts [2]     : 0, 0%
    #> cutoff                    (mean count < 46)            (mean count < 46)
    #>                                        0.01
    #> LFC > 0 (up)   LFC > 0 (up)       : 4, 0.4%
    #> LFC < 0 (down) LFC < 0 (down)     : 1, 0.1%
    #> outliers       outliers [1]       : 1, 0.1%
    #> low counts       low counts [2]     : 0, 0%
    #> cutoff                    (mean count < 46)degSummary(res1)#>                                         0.1                         0.05
    #> LFC > 0 (up)   LFC > 0 (up)       : 4, 0.4% LFC > 0 (up)       : 4, 0.4%
    #> LFC < 0 (down) LFC < 0 (down)     : 3, 0.3% LFC < 0 (down)     : 3, 0.3%
    #> outliers       outliers [1]       : 1, 0.1% outliers [1]       : 1, 0.1%
    #> low counts       low counts [2]     : 0, 0%   low counts [2]     : 0, 0%
    #> cutoff                    (mean count < 46)            (mean count < 46)
    #>                                        0.01
    #> LFC > 0 (up)   LFC > 0 (up)       : 4, 0.4%
    #> LFC < 0 (down) LFC < 0 (down)     : 1, 0.1%
    #> outliers       outliers [1]       : 1, 0.1%
    #> low counts       low counts [2]     : 0, 0%
    #> cutoff                    (mean count < 46)degSummary(res1, kable = TRUE)#>
    #>
    #> |               |0.1                          |0.05                         |0.01                         |
    #> |:--------------|:----------------------------|:----------------------------|:----------------------------|
    #> |LFC > 0 (up)   |LFC > 0 (up)       : 4, 0.4% |LFC > 0 (up)       : 4, 0.4% |LFC > 0 (up)       : 4, 0.4% |
    #> |LFC < 0 (down) |LFC < 0 (down)     : 3, 0.3% |LFC < 0 (down)     : 3, 0.3% |LFC < 0 (down)     : 1, 0.1% |
    #> |outliers       |outliers [1]       : 1, 0.1% |outliers [1]       : 1, 0.1% |outliers [1]       : 1, 0.1% |
    #> |low counts     |low counts [2]     : 0, 0%   |low counts [2]     : 0, 0%   |low counts [2]     : 0, 0%   |
    #> |cutoff         |(mean count < 46)            |(mean count < 46)            |(mean count < 46)            |degSummary(res2[[1]])#>                                         0.1                         0.05
    #> LFC > 0 (up)   LFC > 0 (up)       : 4, 0.4% LFC > 0 (up)       : 4, 0.4%
    #> LFC < 0 (down) LFC < 0 (down)     : 3, 0.3% LFC < 0 (down)     : 3, 0.3%
    #> outliers       outliers [1]       : 1, 0.1% outliers [1]       : 1, 0.1%
    #> low counts       low counts [2]     : 0, 0%   low counts [2]     : 0, 0%
    #> cutoff                    (mean count < 46)            (mean count < 46)
    #>                                        0.01
    #> LFC > 0 (up)   LFC > 0 (up)       : 4, 0.4%
    #> LFC < 0 (down) LFC < 0 (down)     : 1, 0.1%
    #> outliers       outliers [1]       : 1, 0.1%
    #> low counts       low counts [2]     : 0, 0%
    #> cutoff                    (mean count < 46)