Process / pipelineBioinformatics / omics

Time-series RNA-seq Differential Expression — Temporal Transcriptomics

Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturing dynamic gene expression trajectories rather than a single snapshot contrast. Tools such as maSigPro, ImpulseDE2, and splineTimeR have been developed specifically for this design.

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Sources

  1. Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102. link
  2. Fischer, D. S., Theis, F. J., & Yosef, N. (2018). Impulse model-based differential expression analysis of time series single-cell RNA-seq data. Genome Biology, 19(1), 1–14. link

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Referenced by

ScholarGateTime-series RNA-seq differential expression (Time-series RNA Sequencing Differential Expression Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/bioinformatics/time-series-rna-seq-differential-expression