exportIntegratedSignals: Export integrated signals.
In ASpli: Analysis of Alternative Splicing Using RNA-Seq

Description Usage Arguments Value Author(s) See Also Examples

Export integrated signals in an easy to analyze HTML table.

exportIntegratedSignals( is, output.dir="is", 
                         sr, counts, features, asd,
                         mergedBams, 
                         jCompletelyIncluded = FALSE, zoomRegion = 1.5, 
                         useLog = FALSE, tcex = 1, ntop = NULL, 
                         openInBrowser = FALSE, 
                         makeGraphs = TRUE, bforce=FALSE
                        )

`is`	An object of class `ASpliIntegratedSignals`
`sr`	An object of class `ASpliSplicingReport`
`counts`	An object of class `ASpliCounts`
`features`	An object of class `ASpliFeatures`
`asd`	An object of class `ASpliAS`
`output.dir`	HTML reports output directory
`mergedBams`	Dataframe with two columns, bams and conditions. Bams are paths to merged bams for each condition to be ploted.
`jCompletelyIncluded`	If TRUE only plot junctions completely included in plot region. Else plot any overlapping junction in the region
`zoomRegion`	Magnify plot region by this factor
`useLog`	Plot counts log
`tcex`	Text size
`ntop`	Only show n top signals
`openInBrowser`	Open reports in browser when done
`makeGraphs`	Generate graphs in reports
`bforce`	Force plot generation even if plot already exists

Produces html reports

Andres Rabinovich, Estefania Mancini, Javier Iserte, Marcelo Yanovsky, Ariel Chernomoretz

gbDUreport, jDUreport, ASpliSplicingReport, splicingReport, \ codeASpliIntegratedSignals

  # Create a transcript DB from gff/gtf annotation file.
  # Warnings in this examples can be ignored. 
  library(GenomicFeatures)
  genomeTxDb <- makeTxDbFromGFF( system.file('extdata','genes.mini.gtf', 
                                 package="ASpli") )
  
  # Create an ASpliFeatures object from TxDb
  features <- binGenome( genomeTxDb )
  
  # Define bam files, sample names and experimental factors for targets.
  bamFileNames <- c( "A_C_0.bam", "A_C_1.bam", "A_C_2.bam", 
                     "A_D_0.bam", "A_D_1.bam", "A_D_2.bam" )

  targets <- data.frame( 
               row.names = paste0('Sample_',c(1:6)),
               bam = system.file( 'extdata', bamFileNames, package="ASpli" ),
               factor1 = c( 'C','C','C','D','D','D'))
  
  # Read counts from bam files
  gbcounts  <- gbCounts( features = features, 
                           targets = targets, 
                           minReadLength = 100, maxISize = 50000,
                           libType="SE", 
                           strandMode=0)
jcounts   <- jCounts(counts = gbcounts, 
                     features = features, 
                     minReadLength = 100,
                     libType="SE", 
                     strandMode=0)
                     

  # Test for factor1
  gbPaired <- gbDUreport(gbcounts, contrast = c(1, -1))
  jPaired  <- jDUreport(jcounts, , contrast = c(1, -1))
  
  # Generate a splicing report merging bins and junctions DU
  sr       <- splicingReport(gbPaired, jPaired, gbcounts)
  is       <- integrateSignals(sr, jcounts)
  
  #Make merged bams dataframe
  mergedBamsFileNames <- c( "A_C.bam", "A_D.bam" )
  mergedBams <- data.frame(bams = system.file( 'extdata', mergedBamsFileNames, package="ASpli" ), 
  condition = c("C", "D"), stringsAsFactors = FALSE)
  
  # Export integrated signals
  exportIntegratedSignals(is, output.dir = paste0(tempdir(), "/is"), sr, gbcounts,
                        features, jcounts, mergedBams, makeGraphs = TRUE, bforce = TRUE )