DEGSet {DEGreport} | R Documentation |
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.
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
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")