mergeBinDUAS-method: Differential usage of bins and PSI/PIR.
In ASpli: Analysis of Alternative Splicing Using RNA-Seq

Description Usage Arguments Value Author(s) See Also Examples

This function merges the results of differential usage of bins, from an ASpliDU object, with PSI/PIR and junction information, from an ASpliAS object. Also, a delta PSI/PIR value is calculated from a contrast.

  mergeBinDUAS( du, 
                as,
                targets, 
                contrast = NULL )

`du`	An object of class `ASpliDU`
`as`	An object of class `ASpliAS`
`targets`	A data frame containing sample, bam files and experimental factor columns.
`contrast`	Define the comparison between conditions to be tested. `contrast` should be a vector with length equal to the number of experimental conditions defined by `targets`. The values of this vector are the coefficients that will be used to weight each condition, the order of the values corresponds to the order given by `getConditions` function. When `contrast` is NULL, defaults to a vector containing -1, as the first value, 1 as the second an zero for all the remaining values, this corresponds to a pair comparison where the first condition is assumed to be a control and the second condition is the treatment, all other conditions are ignored. The default value is NULL.

A data frame containing feature, event, locus, locus_overlap, symbol, gene coordinates, start of bin, end of bin, bin length, log-Fold-Change value, p-value, fdr corrected p-value, J1 inclusion junction, J1 junction counts for each sample, J2 inclusion junction, J2 junction counts for each sample, J3 exclusion junction, J3 junction counts for each sample, PSI or PIR value for each bin, and delta PSI/PIR.

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

ASpliDU , ASpliAS

  # 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",
  #                   "B_C_0.bam", "B_C_1.bam", "B_C_2.bam", 
  #                   "B_D_0.bam", "B_D_1.bam", "B_D_2.bam" )
                     
  #targets <- data.frame( 
  #             row.names = paste0('Sample_',c(1:12)),
  #             bam = system.file( 'extdata', bamFileNames, package="ASpli" ),
  #             factor1 = c( 'A','A','A','A','A','A','B','B','B','B','B','B'),
  #             factor2 = c( 'C','C','C','D','D','D','C','C','C','D','D','D') )
  
  # Load reads from bam files
  #bams <- loadBAM( targets )
  
  # Read counts from bam files
  #counts <- readCounts( features, bams, targets, cores = 1, readLength = 100, 
  #                        maxISize = 50000 )
  
  # Calculate differential usage of genes and bins
  #du <- DUreport.norm( counts, targets , contrast = c(1,-1,-1,1))
  
  # Calculate PSI / PIR for bins and junction.
  #as <- AsDiscover( counts, targets, features, bams, readLength = 100, 
  #                        threshold = 5, cores = 1 )

  #mas <- mergeBinDUAS( du, as, targets, contrast =  c(1,-1,-1,1) )