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)
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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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