DESeq2_tidiers: Tidying methods for DESeq2 DESeqDataSet objects
In biobroom: Turn Bioconductor objects into tidy data frames

Description Usage Arguments Details Value Examples

This reshapes a DESeq2 expressionset object into a tidy format. If the dataset contains hypothesis test results (p-values and estimates), this summarizes one row per gene per possible contrast.

## S3 method for class 'DESeqDataSet'
tidy(x, colData = FALSE, intercept = FALSE, ...)

## S3 method for class 'DESeqResults'
tidy(x, ...)

`x`	DESeqDataSet object
`colData`	whether colData should be included in the tidied output for those in the DESeqDataSet object. If dataset includes hypothesis test results, this is ignored
`intercept`	whether to include hypothesis test results from the (Intercept) term. If dataset does not include hypothesis testing, this is ignored
`...`	extra arguments (not used)

colDat=TRUE adds covariates from colData to the data frame.

If the dataset contains results (p-values and log2 fold changes), the result is a data frame with the columns

`term`	The contrast being tested, as given to `results`
`gene`	gene ID
`baseMean`	mean abundance level
`estimate`	estimated log2 fold change
`stderror`	standard error in log2 fold change estimate
`statistic`	test statistic
`p.value`	p-value
`p.adjusted`	adjusted p-value

If the dataset does not contain results (DESeq has not been run on it), tidy defaults to tidying the counts in the dataset:

`gene`	gene ID
`sample`	sample ID
`count`	number of reads in this gene in this sample

If colData = TRUE, it also merges this with the columns present in colData(x).

# From DESeq2 documentation

if (require("DESeq2")) {
    dds <- makeExampleDESeqDataSet(betaSD = 1)

    tidy(dds)
    # With design included
    tidy(dds, colData=TRUE)

    # add a noise confounding effect
    colData(dds)$noise <- rnorm(nrow(colData(dds)))
    design(dds) <- (~ condition + noise)

    # perform differential expression tests
    ddsres <- DESeq(dds, test = "Wald")
    # now results are per-gene, per-term
    tidied <- tidy(ddsres)
    tidied

    if (require("ggplot2")) {
        ggplot(tidied, aes(p.value)) + geom_histogram(binwidth = .05) +
            facet_wrap(~ term, scale = "free_y")
    }
}