junctionDUreport: Differential junction usage estimation
In ASpli: Analysis of Alternative Splicing Using RNA-Seq

Description Usage Arguments Value Author(s) See Also Examples

Estimate differential usage at junction level. When targets has only two conditions, and contrast is not set, the estimation of differential expression and usage is done with an exact test, otherwise is estimated using a generalized linear model.

  junctionDUreport( counts, 
                    targets,
                    appendTo = NULL, 
                    minGenReads = 10, 
                    minRds = 0.05,
                    threshold = 5,
                    offset = FALSE, 
                    offsetUseFitGeneX = TRUE,
                    contrast = NULL, 
                    forceGLM = FALSE)

`counts`	An object of class `ASpliCounts`
`targets`	A dataframe containing sample, bam and experimental factor columns.
`appendTo`	An object of class `ASpliDU` to which append the results of junction differential usage. If `appendTo` is `NULL` a new `ASpliDU` is created
`minGenReads`	Junctions within genes with at least an average of `minGenReads` reads for all conditions are included into the differential junction usage test. Default value is 10 reads.
`minRds`	Junctions within genes with at least an average of `minRds` read density for all conditions are included into the differential bin usage test. Junctions with at least an average of `minRds` read density for any condition are included into the differential junction usage test. Default value is 0.05.
`threshold`	Junction with at least `threshold` counts are included into the differential usage test.
`offset`	Corrects junction counts using an offset matrix derived from gene expression data. Default = FALSE
`offsetUseFitGeneX`	Fit a GLM using gene counts to build the offset matrix. This argument is used only when 'offset' argument is set to TRUE. The default value is TRUE
`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. Default = NULL
`forceGLM`	Force the use of a generalized linear model to estimate differential expression and usage. Default = FALSE