scran (version 1.0.3)

convertTo: Convert to other classes

Description

Convert a SCESet object into other classes for entry into other analysis pipelines.

Usage

"convertTo"(x, type=c("edgeR", "DESeq2", "monocle"), fData.col=NULL, pData.col=NULL, ...,  assay, normalize=TRUE, get.spikes=FALSE)

Arguments

x

A SCESet object.

type

A string specifying the analysis for which the object should be prepared.

fData.col

Any set of indices specifying which columns of fData(x) should be retained in the returned object.

pData.col

Any set of indices specifying which columns of pData(x) should be retained.

...

Other arguments to be passed to pipeline-specific constructors.

assay

A string specifying which assay of x should be put in the returned object.

normalize

A logical scalar specifying whether the assay values should be normalized for type="monocle".

get.spikes

A logical scalar specifying whether rows corresponding to spike-in transcripts should be returned.

Value

For type="edgeR", a DGEList object is returned containing the count matrix. Size factors are converted to normalization factors. Gene-specific fData is stored in the genes element, and cell-specific pData is stored in the samples element.For type="DESeq2", a DESeqDataSet object is returned containing the count matrix and size factors. Additional gene- and cell-specific data is stored in the mcols and colData respectively.For type="monocle", a CellDataSet object is returned containing the unlogged expression values. Additional gene- and cell-specific data is stored in the fData and pData respectively.

Details

This function converts a SCESet into various other classes in preparation for entry into other analysis pipelines, as specified by type. Gene- and cell-specific data fields can be retained in the output object by setting fData.col and pData.col, respectively. Other arguments can be passed to the relevant constructors through the ellipsis.

By default, for edgeR and DESeq2, assay is set to "counts" such that count data is stored in the output object. This is consistent with the required inputs to these analysis pipeline (normalization information is stored through size factors). For monocle, counts are divided by the size factors to yield (roughly) log-normally distributed expression values. This can be turned off (i.e., to use the raw values in assay) by setting normalize=FALSE.

In all cases, rows corresponding to spike-in transcripts are removed from the output object by default. As such, rows in the returned object may not correspond directly to rows in x. Users should consider this when retrieving analysis results from these pipelines, e.g., match on row names. This behaviour can be turned off by setting get.spikes=TRUE, such that all rows are retrieved in the output object.

See Also

DGEList, DESeqDataSetFromMatrix, newCellDataSet

Examples

ncells <- 200
ngenes <- 100
count.sizes <- rnbinom(ncells, mu=100, size=5)
dummy <- matrix(count.sizes, ncol=ncells, nrow=ngenes, byrow=TRUE)
rownames(dummy) <- paste0("X", seq_len(ngenes))

X <- newSCESet(countData=data.frame(dummy))
is.spike <- rbinom(ngenes, 1, 0.5)==0L
isSpike(X) <- is.spike
sizeFactors(X) <- 2^rnorm(ncells)
X <- normalize(X)

fData(X)$SYMBOL <- paste0("X", seq_len(ngenes))
X$other <- sample(LETTERS, ncells, replace=TRUE)

convertTo(X, type="edgeR")
convertTo(X, type="DESeq2")
convertTo(X, type="monocle")