DEGSet {DEGreport} | R Documentation

## DEGSet

### Description

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.

### Usage

    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)

### Arguments

`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 `as.DEGSet`.
`...` | Optional parameters of the generic.
`extras` | List of extra tables related to the same comparison when using `as.DEGSet`.
`contrast` | To name the comparison when using `as.DEGSet`.

### Details

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)`.

### Author(s)

Lorena Pantano

### Examples

    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[[1]])
    deg(res[[1]], 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")

[Package DEGreport version 1.22.0 Index]