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

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Cellular responses to environmental perturbations manifest as dynamic gene expression changes.
• These temporal gene expression variations can often be modeled as impulse-like patterns.
• Analyzing high-throughput time series data is crucial for understanding these dynamic biological processes.

Purpose of the Study:

• To introduce ImpulseDE, an R package designed to capture impulse-like gene expression patterns.
• To identify differentially expressed genes over time within a single experiment or between two experiments.
• To provide an efficient tool for analyzing time-series gene expression data.

Main Methods:

• ImpulseDE fits a representative impulse model to each gene in high-throughput time series datasets.
• The package identifies differentially expressed genes across time points.
• Computational efficiency is enhanced through the use of clustering and multi-threading.

Main Results:

• ImpulseDE effectively captures impulse-like gene expression dynamics.
• The package successfully identifies differentially expressed genes in both microarray and RNA-Seq data.
• Demonstrates the utility of the impulse model for representing biological gene expression changes.

Conclusions:

• ImpulseDE is a powerful R package for analyzing time-series gene expression data.
• The package accurately represents the underlying biology of gene expression changes.
• ImpulseDE offers an optimized approach for identifying dynamic gene expression patterns.