DESeqDataSetis a subclass of
RangedSummarizedExperiment, used to store the input values, intermediate calculations and results of an analysis of differential expression. The
DESeqDataSetclass 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, ...)
RangedSummarizedExperimentwith 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
RangedSummarizedExperimentobject can be generated by the function
summarizeOverlapsin the GenomicAlignments package.
formulawhich expresses how the counts for each gene depend on the variables in
colData. Many R
formulaare valid, including designs with multiple variables, e.g.,
~ group + condition, and designs with interactions, e.g.,
~ genotype + treatment + genotype:treatment. See
resultsfor 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.
~ 1can be used for no design, although users need to remember to switch to another design for differential testing.
data.framewith at least a single column. Rows of colData correspond to columns of countData
SummarizedExperimentincluding 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
data.framewith 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
colnames(countData) <- NULL
countData <- matrix(1:100,ncol=4) condition <- factor(c("A","A","B","B")) dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), ~ condition)