DESeq2_tidiers

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Tidying methods for DESeq2 DESeqDataSet objects

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.

Usage
"tidy"(x, colData = FALSE, intercept = FALSE, ...)
"tidy"(x, ...)
Arguments
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)
Details

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

Value

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).

Aliases
  • DESeq2_tidiers
  • tidy.DESeqDataSet
  • tidy.DESeqResults
Examples

# 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")
    }
}

Documentation reproduced from package biobroom, version 1.4.2, License: LGPL

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