quantifyGenes: Quantify expression of genes

In CAGEfightR: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor

Description

Obtain gene-level expression estimates by summing clusters annotated to the same gene. Unannotated transcripts (NAs) are discarded.

Usage

quantifyGenes(object, genes, inputAssay = "counts", sparse = FALSE)

Arguments

object	RangedSummarizedExperiment: Cluster-level expression values.
genes	character: Name of column in rowData holding gene IDs (NAs will be discarded).
inputAssay	character: Name of assay holding values to be quantified, (usually counts).
sparse	logical: If the input is a sparse matrix, TRUE will keep the output matrix sparse while FALSE will coerce it into a normal matrix.

Value

RangedSummarizedExperiment with rows corresponding to genes. Location of clusters within genes is stored as a GRangesList in rowRanges. seqinfo and colData is copied over from object.

See Also

Other Quantification functions: quantifyCTSSs2(), quantifyCTSSs(), quantifyClusters()

Examples

data(exampleUnidirectional)

# Annotate clusters with geneIDs:
library(TxDb.Mmusculus.UCSC.mm9.knownGene)
txdb <- TxDb.Mmusculus.UCSC.mm9.knownGene
exampleUnidirectional <- assignGeneID(exampleUnidirectional,
                                      geneModels=txdb,
                                      outputColumn='geneID')

# Quantify counts within genes:
quantifyGenes(exampleUnidirectional, genes='geneID', inputAssay='counts')

# For exceptionally large datasets,
# the resulting count matrix can be left sparse:
quantifyGenes(exampleUnidirectional,
              genes='geneID',
              inputAssay='counts',
              sparse=TRUE)

CAGEfightR documentation built on Nov. 8, 2020, 5:42 p.m.