Methoden vergleichen

	Zeitreihen-RNA-seq-Differentialexpression	Pathway Enrichment Analysis
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2006–2018 (principal methods established)	2003–2005
Urheber	Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Typ	Computational genomics pipeline	Statistical functional annotation method
Wegweisende Quelle	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.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
Aliasnamen	longitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Verwandt	6	6
Zusammenfassung	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.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.