No abstract

While many transcriptional profiling experiments measure dynamic processes that change over time, few include enough time points to adequately capture temporal changes in expression. This is especially true for data from human subjects, for which relevant samples may be hard to obtain, and for developmental processes where dynamics are critically important. Although most expression data sets sample at a single time point, it is possible to use accompanying temporal information to create a virtual time series by combining data from different individuals.We introduce TEMPO, a pathway-based outlier detection approach for finding pathways showing significant temporal changes in expression patterns from such combined data. We present findings from applications to existing microarray and RNA-seq data sets. TEMPO identifies temporal dysregulation of biologically relevant pathways in patients with autism spectrum disorders, Huntington's disease, Alzheimer's disease, and COPD. Its findings are distinct from those of standard temporal or gene set analysis methodologies.Overall, our experiments demonstrate that there is enough signal to overcome the noise inherent in such virtual time series, and that a temporal pathway approach can identify new functional, temporal, or developmental processes associated with specific phenotypes.Availability: An R package implementing this method and full results tables are available at bcb.cs.tufts.edu/tempo/.