Splicing is an important process for regulation of gene expression in 
eukaryotes, and it has important functional links to other steps of gene 
expression. Two examples of these linkages include Ceg1, a component of the mRNA 
capping enzyme, and the chromatin elongation factors Spt4-5, both of which have 
recently been shown to play a role in the normal splicing of several genes in 
the yeast Saccharomyces cerevisiae. Using a genomic approach to characterize the 
roles of Spt4-5 in splicing, we used splicing-sensitive DNA microarrays to 
identify specific sets of genes that are mis-spliced in ceg1, spt4, and spt5 
mutants. In the context of a complex, nested, experimental design featuring 22 
dye-swap array hybridizations, comprising both biological and technical 
replicates, we applied five appropriate statistical models for assessing 
differential expression between wild-type and the mutants. To refine selection 
of differential expression genes, we then used a robust model-synthesizing 
approach, Differential Expression via Distance Synthesis, to integrate all five 
models. The resultant list of differentially expressed genes was then further 
analyzed with regard to select attributes: we found that highly transcribed 
genes with long introns were most sensitive to spt mutations. QPCR confirmation 
of differential expression was established for the limited number of genes 
evaluated. In this paper, we showcase splicing array technology, as well as 
powerful, yet general, statistical methodology for assessing differential 
expression, in the context of a real, complex experimental design. Our results 
suggest that the Spt4-Spt5 complex may help coordinate splicing with 
transcription under conditions that present kinetic challenges to spliceosome 
assembly or function.