filterDU-method: Filtering ASpliDU objects
In ASpli: Analysis of Alternative Splicing Using RNA-Seq

Description Usage Arguments Value Author(s) See Also Examples

ASpliDU object can be filtered to retain genes, bins or junction according to their fdr corrected p-value estimated and log-fold-change.

 
  filterDU( 
    du , 
    what = c( 'genes','bins','junctions'),
    fdr = 1,
    logFC = 0,
    absLogFC = TRUE,
    logFCgreater = TRUE
  )

`du`	An ASpliDU object
`what`	A character vector that specifies the kind of features that will be filtered. Accepted values are 'genes', 'bins', 'junctions'. Multiple values can be passed at the same time. The default value is c( 'genes','bins', 'junctions')
`fdr`	A double value representing the maximum accepted value of fdr corrected p-value to pass the filter. The default value is 1, the neutral value for fdr filtering operation.
`logFC`	A double value representing the cut-off for accepted values of log-fold-change to pass the filter. The default value is 0, the neutral value for logFC filtering operation if logFCgreater and absLocFC arguments are both TRUE.
`absLogFC`	A logical value that specifies that the absolute value of log-fold-change will be used in the filter operation. The default value is TRUE.
`logFCgreater`	A logical value that specifies that the log-fold-change value ( or abs(log-fold-change) if absLogFC argument is TRUE) of features must be greater than the cut-off value to pass the filter. The default value is TRUE.

A new ASpliDU object with the results of the filtering operations. The elements of features that were not specified to be filtered are kept from the input ASpliDU object.

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

DUreport.norm, DUreport.offset, jDUreport, gbDUreport,

 # 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') )
  
  # 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 junctions only 
  #du       <- DUreport.norm( counts, targets )
  
  # Filter by FDR
  #duFiltered1 <- filterDU( du, what=c('genes','bins'), 
  #   fdr = 0.01 )


  # Filter by logFC, only those that were up-regulated 
  #duFiltered2 <- filterDU( du, what=c('genes','bins'), 
  #   logFC = log( 1.5, 2 ),  absLogFC = FALSE )