DESeq2 (version 1.12.3)

DESeqDataSet-class: DESeqDataSet object and constructors


DESeqDataSet is a subclass of RangedSummarizedExperiment, used to store the input values, intermediate calculations and results of an analysis of differential expression. The DESeqDataSet class enforces non-negative integer values in the "counts" matrix stored as the first element in the assay list. In addition, a formula which specifies the design of the experiment must be provided. The constructor functions create a DESeqDataSet object from various types of input: a RangedSummarizedExperiment, a matrix, count files generated by the python package HTSeq, or a list from the tximport function in the tximport package. See the vignette for examples of construction from different types.


DESeqDataSet(se, design, ignoreRank = FALSE)
DESeqDataSetFromMatrix(countData, colData, design, tidy = FALSE, ignoreRank = FALSE, ...)
DESeqDataSetFromHTSeqCount(sampleTable, directory = ".", design, ignoreRank = FALSE, ...)
DESeqDataSetFromTximport(txi, colData, design, ...)


a RangedSummarizedExperiment with columns of variables indicating sample information in colData, and the counts as the first element in the assays list, which will be renamed "counts". A RangedSummarizedExperiment object can be generated by the function summarizeOverlaps in the GenomicAlignments package.
a formula which expresses how the counts for each gene depend on the variables in colData. Many R formula are valid, including designs with multiple variables, e.g., ~ group + condition, and designs with interactions, e.g., ~ genotype + treatment + genotype:treatment. See results for a variety of designs and how to extract results tables. By default, the functions in this package will use the last variable in the formula for building results tables and plotting. ~ 1 can be used for no design, although users need to remember to switch to another design for differential testing.
use of this argument is reserved for DEXSeq developers only. Users will immediately encounter an error upon trying to estimate dispersion using a design with a model matrix which is not full rank.
for matrix input: a matrix of non-negative integers
for matrix input: a DataFrame or data.frame with at least a single column. Rows of colData correspond to columns of countData
for matrix input: whether the first column of countData is the rownames for the count matrix
arguments provided to SummarizedExperiment including rowRanges and metadata. Note that for Bioconductor 3.1, rowRanges must be a GRanges or GRangesList, with potential metadata columns as a DataFrame accessed and stored with mcols. If a user wants to store metadata columns about the rows of the countData, but does not have GRanges or GRangesList information, first construct the DESeqDataSet without rowRanges and then add the DataFrame with mcols(dds).
for htseq-count: a data.frame with three or more columns. Each row describes one sample. The first column is the sample name, the second column the file name of the count file generated by htseq-count, and the remaining columns are sample metadata which will be stored in colData
for htseq-count: the directory relative to which the filenames are specified. defaults to current directory
for tximport: the simple list output of the tximport function


A DESeqDataSet object.


Note on the error message "assay colnames() must be NULL or equal colData rownames()": this means that the colnames of countData are different than the rownames of colData. Fix this with: colnames(countData) <- NULL


See for htseq-count


Run this code

countData <- matrix(1:100,ncol=4)
condition <- factor(c("A","A","B","B"))
dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), ~ condition)

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