Print Summary Statistics of Alpha Level Cutoffs

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 testing
res1 <- 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/bty895
degSummary(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)