ScholarGate
Асистент

Порівняння методів

Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.

Аналіз збагачення шляхів часових рядів×Диференційна експресія генів у часових рядах РНК-сек×
ГалузьБіоінформатикаБіоінформатика
РодинаProcess / pipelineProcess / pipeline
Рік появи2005–20142006–2018 (principal methods established)
Автор методуBar-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
ТипFunctional enrichment analysis with temporal modelingComputational genomics pipeline
Основоположне джерелоErnst, 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 ↗
Інші назвиtemporal pathway analysis, longitudinal pathway enrichment, dynamic pathway analysis, TPEAlongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
Пов'язані56
ПідсумокTime-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.
ScholarGateНабір даних
  1. v1
  2. 2 Джерела
  3. PUBLISHED
  1. v1
  2. 2 Джерела
  3. PUBLISHED

Перейти до пошуку Завантажити слайди

ScholarGateПорівняння методів: Time-series pathway enrichment analysis · Time-series RNA-seq differential expression. Отримано 2026-06-19 з https://scholargate.app/uk/compare