Transcriptomes are routinely compared in term of a list of differentially expressed genes followed by functional enrichment analysis. Due to the technology limitations of microarray, the molecular mechanisms of differential expression is poorly understood. Using RNA-seq data, we propose a generalized dSpliceType framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. We applied the method to two public RNA-seq data sets and compared the transcriptomes between treatment and control conditions. The generalized dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model.