Módszerek összehasonlítása

	Single-cell eQTL Analysis	Pathway Enrichment Analysis
Tudományterület	Bioinformatika	Bioinformatika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2020	2003–2005
Megalkotó	Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Típus	Statistical genomics pipeline	Statistical functional annotation method
Alapmű	Cuomo, A. S. E., et al. (2020). Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Communications, 11(1), 810.	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.
Alternatív nevek	sc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Kapcsolódó	6	6
Összefoglaló	Single-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling discovery of variants whose effects are confined to particular cell states or developmental stages.	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.