Description Usage Arguments Value Author(s) References See Also Examples
Transcriptome-wide identified clusters are used to generate a metagene profile by binning gene bodies, upstream and downstream regions.
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| 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
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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, 
	                 threshold = 2 ) 
fclusters <- filterClusters( clusters = clusters, 
		             highConfSub = highConfSub, 
        		     coverage = coverage,
			     model = model, 
			     genome = Hsapiens, 
		             refBase = 'T', 
		             minWidth = 12 )
## Not run: meta <- getMetaGene( clusters = fclusters )
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