ScholarGate
Asistent

Porovnat metody

Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Analýza obohacení časových řad pro dráhy×Diferenciální exprese časových řad RNA-seq×
OborBioinformatikaBioinformatika
RodinaProcess / pipelineProcess / pipeline
Rok vzniku2005–20142006–2018 (principal methods established)
TvůrceBar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inferenceConesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
TypFunctional enrichment analysis with temporal modelingComputational genomics pipeline
Původní zdrojErnst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168. link ↗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 ↗
Další názvytemporal pathway analysis, longitudinal pathway enrichment, dynamic pathway analysis, TPEAlongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
Příbuzné56
ShrnutíTime-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses.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.
ScholarGateDatová sada
  1. v1
  2. 2 Zdroje
  3. PUBLISHED
  1. v1
  2. 2 Zdroje
  3. PUBLISHED

Přejít na hledání Stáhnout prezentaci

ScholarGatePorovnat metody: Time-series pathway enrichment analysis · Time-series RNA-seq differential expression. Získáno 2026-06-19 z https://scholargate.app/cs/compare