Compara mètodes

	Anàlisi d'ARNseq de cèl·lula única assistida per aprenentatge automàtic	Anàlisi d'Enriquiment de Vies
Camp	Bioinformàtica	Bioinformàtica
Família	Process / pipeline	Process / pipeline
Any d'origen	2015-2018 (rapid expansion with scVI 2018, Seurat v3 2019)	2003–2005
Autor original	Nir Yosef, Fabian Theis, and colleagues (scVI/scANVI framework; broader community-driven)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tipus	Computational analysis pipeline	Statistical functional annotation method
Font seminal	Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.	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.
Àlies	ML-scRNA-seq, deep learning scRNA-seq, AI-assisted scRNA-seq, ML-guided single-cell transcriptomics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relacionats	6	6
Resum	Machine learning-assisted single-cell RNA sequencing (scRNA-seq) analysis integrates supervised, unsupervised, and deep generative models into the standard scRNA-seq workflow to handle the unique challenges of single-cell data: extreme sparsity, high dimensionality, technical noise, and batch effects across experiments. Methods such as variational autoencoders (scVI), graph neural networks, and transfer learning substantially improve cell-type identification, trajectory inference, and cross-study data integration compared with purely statistical approaches.	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.