Comparar métodos

	Análisis de Enriquecimiento de Conjuntos de Genes en Series Temporales	Análisis de scRNA-seq
Campo	Bioinformática	Bioinformática
Familia	Process / pipeline	Process / pipeline
Año de origen	2005 (GSEA foundation); time-series adaptations 2007–2014	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Autor original	Extension of GSEA (Subramanian et al., 2005); time-series adaptations developed through maSigPro (Conesa lab) and related tools	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Tipo	Gene set enrichment method for longitudinal omics data	High-throughput single-cell transcriptomic profiling pipeline
Fuente seminal	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.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
Alias	longitudinal GSEA, dynamic GSEA, time-course GSEA, TS-GSEA	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Relacionados	6	5
Resumen	Time-series gene set enrichment analysis (TS-GSEA) extends the classical GSEA framework to detect biologically coordinated gene sets — pathways, gene ontology terms, or curated signatures — whose collective expression changes meaningfully over time. Rather than comparing two snapshots, it models the full temporal trajectory of gene expression to identify which functional programs are activated, suppressed, or dynamically remodelled during a biological process such as development, treatment response, or disease progression.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.