Comparar métodos

	Análise de Enriquecimento de Conjunto de Genes (GSEA)	Análise de RNA-seq de célula única
Área	Bioinformática	Bioinformática
Família	Process / pipeline	Process / pipeline
Ano de origem	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Autor original	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Tipo	Functional genomics / enrichment analysis	High-throughput single-cell transcriptomic profiling pipeline
Fonte 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.
Outros nomes	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Relacionados	5	5
Resumo	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.	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.