Differential gene expression data formats converter

In DEFormats: Differential gene expression data formats converter

Convert between DESeqDataSet and DGEList objects

The following examples demonstrate how to shuttle data between DESeqDataSet and DGEList containers. Before we start, let's first load the required packages.

library(DESeq2)
library(edgeR)
library(DEFormats)

DGEList to DESeqDataSet

We will illustrate the functionality of the package on a mock expression data set of an RNA-seq experiment. The sample counts table can be generated using the function provided by r Rpackage("DEFormats"):

counts = simulateRnaSeqData()

The resulting object is a matrix with rows corresponding to genomic features and columns to samples.

head(counts)

In order to construct a DGEList object we need to provide, apart from the counts matrix, the sample grouping.

group = rep(c("A", "B"), each = 3)

dge = DGEList(counts, group = group)
dge

A DGEList object can be easily converted to a DESeqDataSet object with the help of the function as.DESeqDataSet.

dds = as.DESeqDataSet(dge)
dds

Just to make sure that the coercions conserve the data and metadata, we now convert dds back to a DGEList and compare the result to the original dge object.

identical(dge, as.DGEList(dds))

Note that because of the use of reference classes in the SummarizedExperiment class which DESeqDataSet extends, identical will return FALSE for any two DESeqDataSet instances, even for ones constructed from the same input:

dds1 = DESeqDataSetFromMatrix(counts, data.frame(condition=group), ~ condition)
dds2 = DESeqDataSetFromMatrix(counts, data.frame(condition=group), ~ condition)

identical(dds1, dds2)

DESeqDataSet to DGEList

Instead of a count matrix, simulateRnaSeqData can also return an annotated RangedSummarizedExperiment object. The advantage of such an object is that, apart from the counts matrix stored in the assay slot, it also contains sample description in colData, and gene information stored in rowRanges as a GRanges object.

se = simulateRnaSeqData(output = "RangedSummarizedExperiment")
se

The se object can be readily used to construct a DESeqDataSet object,

dds = DESeqDataSet(se, design = ~ condition)

which can be converted to a DGEList using the familiar method.

dge = as.DGEList(dds)
dge

Note the additional genes element on the dge list compared to the object from the previous section.

Create DGEList objects from SummarizedExperiment

Similarly as for the DESeqDataSet constructor from r Biocpkg("DESeq2"), it is possible to directly use RangedSummarizedExperiment objects as input to the generic DGEList constructor defined in r Rpackage("DEFormats"). This allows you to use common input objects regardless of whether you are applying r Rpackage("DESeq2") or r Rpackage("edgeR") to perform your analysis, or to easily switch between these two tools in your pipeline.

names(colData(se)) = "group"
dge = DGEList(se)
dge

We renamed the condition column of colData(se) to group in order to allow r Rpackage("edgeR") to automatically pick up the correct sample grouping. Another way of specifying this is through the argument group to DGEList.

FAQ

Coerce DGEList to RangedSummarizedExperiment

Even though there is no direct method to go from a DGEList to a RangedSummarizedExperiment, the coercion can be easily performed by first converting the object to a DESeqDataSet, which is a subclass of RangedSummarizedExperiment, and then coercing the resulting object to its superclass.

dds = as.DESeqDataSet(dge)
rse = as(dds, "RangedSummarizedExperiment")
rse

Session info

sessionInfo()

DEFormats documentation built on Nov. 8, 2020, 5:31 p.m.