방법 비교

	시간 순서 단일 세포 RNA 시퀀싱 분석	eQTL 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2014-2018 (pseudotime and RNA velocity frameworks)	2001 (term coined); widely adopted after 2005
창시자	Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)	Ritsert C. Jansen & Jan-Peter Nap
유형	Computational bioinformatics pipeline	Association mapping method
원전	Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.	Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391.
별칭	scRNA-seq time course analysis, longitudinal scRNA-seq, temporal single-cell transcriptomics, dynamic single-cell gene expression analysis	eQTL mapping, expression QTL analysis, transcriptomic QTL analysis, eQTL study
관련	6	6
요약	Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA velocity, and differential dynamics testing, researchers reconstruct the temporal trajectory of individual cells and identify the gene regulatory changes that drive biological transitions.	eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions.