getMetaGene {wavClusteR} | R Documentation |
Transcriptome-wide identified clusters are used to generate a metagene profile by binning gene bodies, upstream and downstream regions.
getMetaGene(clusters, txDB = NULL, upstream = 1e3, downstream = 1e3, nBins = 40, nBinsUD = 10, minLength = 1, genome = 'hg19', tablename = 'ensGene', plot = TRUE, verbose = TRUE, ...)
clusters |
GRanges object containing individual clusters as identified by the getClusters function |
txDB |
TranscriptDb object obtained through a call to the
|
upstream |
An integer corresponding to the number of bases to be considered upstream the gene. Default is 1000 |
downstream |
An integer corresponding to the number of bases to be considered downstream the gene. Default is 1000 |
nBins |
An integer corresponding to the number of bins to be used to partition the genes. Default is 40 |
nBinsUD |
An integer corresponding to the number of bins to be used to partion upstream and downstream regions. Defauls is 10, i.e. the bin size is 100 bases for the default extension |
minLength |
An integer indicating the the minimum required length of a gene in order for it to be considered. Default is 1, i.e. all genes are considered |
genome |
A character specifying the genome abbreviation used by UCSC.
Available abbreviations are returned by a call to |
tablename |
A character specifying the name of the UCSC table
containing the transcript annotations to retrieve. Available table names are
returned by a call to |
plot |
Logical, if TRUE a dotchart with cluster annotations is produced |
verbose |
Logical, if TRUE processing steps are printed |
... |
Additional parameters to be passed to the |
A numeric vector of the same length as nBins
+ 2 *
nBinsUD
containing normalized counts. If plot
, the metagene
profile is also depicted as a line plot.
Federico Comoglio
Comoglio F, Sievers C and Paro R (2015) Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data, BMC Bioinformatics 16, 32.
Comoglio F*, Sievers C* and Paro R, wavClusteR: an R package for PAR-CLIP data analysis, submitted
require(BSgenome.Hsapiens.UCSC.hg19) data( model, package = "wavClusteR" ) filename <- system.file( "extdata", "example.bam", package = "wavClusteR" ) example <- readSortedBam( filename = filename ) countTable <- getAllSub( example, minCov = 10, cores = 1 ) highConfSub <- getHighConfSub( countTable, supportStart = 0.2, supportEnd = 0.7, substitution = "TC" ) coverage <- coverage( example ) clusters <- getClusters( highConfSub = highConfSub, coverage = coverage, sortedBam = example, method = 'mrn', cores = 1, threshold = 2 ) fclusters <- filterClusters( clusters = clusters, highConfSub = highConfSub, coverage = coverage, model = model, genome = Hsapiens, refBase = 'T', minWidth = 12 ) ## Not run: meta <- getMetaGene( clusters = fclusters )