Description Usage Arguments Details Author(s) Examples
S4 class to store data from differentially expression analysis. It should be compatible with different package and stores the information in a way the methods will work with all of them.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | DEGSet(resList, default)
DEGSet(resList, default)
as.DEGSet(object, ...)
## S4 method for signature 'TopTags'
as.DEGSet(object, default = "raw", extras = NULL)
## S4 method for signature 'data.frame'
as.DEGSet(object, contrast, default = "raw", extras = NULL)
## S4 method for signature 'DESeqResults'
as.DEGSet(object, default = "shrunken", extras = NULL)
|
resList |
List with results as elements containing log2FoldChange, pvalues and padj as column. Rownames should be feature names. Elements should have names. |
default |
The name of the element to use by default. |
object |
Different objects to be transformed to DEGSet when using |
... |
Optional parameters of the generic. |
extras |
List of extra tables related to the same comparison when using |
contrast |
To name the comparison when using |
For now supporting only DESeq2::results()
output.
Use constructor degComps()
to create the object.
The list will contain one element for each comparison done. Each element has the following structure:
DEG table
Optional table with shrunk Fold Change when it has been done.
To access the raw table use deg(dgs, "raw")
, to access the
shrunken table use deg(dgs, "shrunken")
or just deg(dgs)
.
Lorena Pantano
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | library(DESeq2)
library(edgeR)
library(limma)
dds <- makeExampleDESeqDataSet(betaSD = 1)
colData(dds)[["treatment"]] <- sample(colData(dds)[["condition"]], 12)
design(dds) <- ~ condition + treatment
dds <- DESeq(dds)
res <- degComps(dds, combs = c("condition"))
deg(res)
deg(res, tidy = "tibble")
# From edgeR
dge <- DGEList(counts=counts(dds), group=colData(dds)[["treatment"]])
dge <- estimateCommonDisp(dge)
res <- as.DEGSet(topTags(exactTest(dge)))
# From limma
v <- voom(counts(dds), model.matrix(~treatment, colData(dds)), plot=FALSE)
fit <- lmFit(v)
fit <- eBayes(fit, robust=TRUE)
res <- as.DEGSet(topTable(fit, n = "Inf"), "A_vs_B")
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