ImpulseDE – detection of differentially expressed genes in time series data using impulse models

Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. Researchers at the University of Bonn have developed ImpulseDE, an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE, the developers demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data.

Performance of ImpulseDE


A Analysis workflow. B Heatmap of overlapping differentially expressed (DE) genes between ImpulseDE applied with and without clustering for q-values ranging from 0 to 0.1. Overlaps were determined using the Jaccard-Coefficient. Colors range from white (no) to red (large overlap). C Venn Diagram showing the qualitative partitioning of genes into distinct groups of overlapping probesets (identified as DE using a q-value cutoff of 0.01) between four approaches. D Impulse model (thick) and natural cubic spline (NCS, thin dashed lines) fits for gene Il21. The NCS is based on a spline basis dimension (sbd) of 16. Expression values and model fits are on log2-scale. Combined data refers to the union of the TH17 and TH0 datasets, where the class affiliations (case or control) were ignored.

Availability: ImpulseDE is available on Bioconductor:

Sander J, Schultze JL, Yosef N. (2016) ImpulseDE: detection of differentially expressed genes in time series data using impulse models. Bioinformatics [Epub ahead of print]. [abstract]

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