Print Summary Statistics of Alpha Level Cutoffs
degSummary(object, alpha = c(0.1, 0.05, 0.01), contrast = NULL, caption = "", kable = FALSE)
object | Can be DEGSet or DESeqDataSet or DESeqResults. |
---|---|
alpha | Numeric vector of desired alpha cutoffs. |
contrast | Character vector to use with |
caption | Character vector to add as caption to the table. |
kable | Whether return a |
data.frame or knitr::kable()
.
original idea of multiple alpha values and code syntax from Michael Steinbaugh.
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)#>#>#>#>#>#>#>#>#>degSummary(dse, contrast = "group_Male_vs_Female")#> 0.1 0.05 0.01 #> LFC > 0 (up) 4, 0.4% 4, 0.4% 4, 0.4% #> LFC < 0 (down) 3, 0.3% 3, 0.3% 1, 0.1% #> outliers 1, 0.1% 1, 0.1% 1, 0.1% #> low counts 0, 0% 0, 0% 0, 0% #> cutoff (mean count < 46) (mean count < 46) (mean count < 46)degSummary(res1)#> 0.1 0.05 0.01 #> LFC > 0 (up) 4, 0.4% 4, 0.4% 4, 0.4% #> LFC < 0 (down) 3, 0.3% 3, 0.3% 3, 0.3% #> outliers 1, 0.1% 1, 0.1% 1, 0.1% #> low counts 0, 0% 0, 0% 0, 0% #> cutoff (mean count < 46) (mean count < 46) (mean count < 46)degSummary(res1, kable = TRUE)#> #> #> | |0.1 |0.05 |0.01 | #> |:--------------|:-----------------|:-----------------|:-----------------| #> |LFC > 0 (up) |4, 0.4% |4, 0.4% |4, 0.4% | #> |LFC < 0 (down) |3, 0.3% |3, 0.3% |3, 0.3% | #> |outliers |1, 0.1% |1, 0.1% |1, 0.1% | #> |low counts |0, 0% |0, 0% |0, 0% | #> |cutoff |(mean count < 46) |(mean count < 46) |(mean count < 46) |degSummary(res2[[1]])#> 0.1 0.05 0.01 #> LFC > 0 (up) 4, 0.4% 4, 0.4% 4, 0.4% #> LFC < 0 (down) 3, 0.3% 3, 0.3% 3, 0.3% #> outliers 1, 0.1% 1, 0.1% 1, 0.1% #> low counts 0, 0% 0, 0% 0, 0% #> cutoff (mean count < 46) (mean count < 46) (mean count < 46)