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R: DESeqDataSet object and constructors

DESeqDataSet-class

R Documentation

DESeqDataSet object and constructors

Description

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.

Usage

DESeqDataSet(se, design, ignoreRank = FALSE)

DESeqDataSetFromMatrix(countData, colData, design, tidy = FALSE,
  ignoreRank = FALSE, ...)

DESeqDataSetFromHTSeqCount(sampleTable, directory = ".", design,
  ignoreRank = FALSE, ...)

DESeqDataSetFromTximport(txi, colData, design, ...)

Arguments

`se`	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.
`design`	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.
`ignoreRank`	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.
`countData`	for matrix input: a matrix of non-negative integers
`colData`	for matrix input: a `DataFrame` or `data.frame` with at least a single column. Rows of colData correspond to columns of countData
`tidy`	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)`.
`sampleTable`	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`
`directory`	for htseq-count: the directory relative to which the filenames are specified. defaults to current directory
`txi`	for tximport: the simple list output of the `tximport` function

Details

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

Value

A DESeqDataSet object.

References

See http://www-huber.embl.de/users/anders/HTSeq for htseq-count

Examples


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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit


Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/DESeq2/DESeqDataSet.Rd_%03d_medium.png", width=480, height=480)
> ### Name: DESeqDataSet-class
> ### Title: DESeqDataSet object and constructors
> ### Aliases: DESeqDataSet DESeqDataSet-class DESeqDataSetFromHTSeqCount
> ###   DESeqDataSetFromMatrix DESeqDataSetFromTximport
> 
> ### ** Examples
> 
> 
> countData <- matrix(1:100,ncol=4)
> condition <- factor(c("A","A","B","B"))
> dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), ~ condition)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>