  Alternative splicing of gene transcripts greatly expands the functional
capacity of the genome, and certain splice isoforms may indicate specific
disease states such as cancer. Splice junction microarrays interrogate
thousands of splice junctions, but data analysis is difficult and error prone
because of the increased complexity compared to differential gene expression
analysis. We present Rank Change Detection (RCD) as a method to identify
differential splicing events based upon a straightforward probabilistic model
comparing the over- or underrepresentation of two or more competing isoforms.
RCD has advantages over commonly used methods because it is robust to false
positive errors due to nonlinear trends in microarray measurements. Further,
RCD does not depend on prior knowledge of splice isoforms, yet it takes
advantage of the inherent structure of mutually exclusive junctions, and it is
conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD
specifically identifies the biologically important cases when a splice junction
becomes more or less prevalent compared to other mutually exclusive junctions.
The example data is from different cell lines of glioblastoma tumors assayed
with Agilent microarrays.
