Salīdzināt metodes

	Laika sēriju vienšūnu RNS sekvenēšanas analīze	RNA-seq diferenciālās ekspresijas
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2014-2018 (pseudotime and RNA velocity frameworks)	2008–2010 (RNA-seq DE methodology established)
Autors	Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Tips	Computational bioinformatics pipeline	Quantitative genomics pipeline
Pirmavots	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.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
Citi nosaukumi	scRNA-seq time course analysis, longitudinal scRNA-seq, temporal single-cell transcriptomics, dynamic single-cell gene expression analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Saistītās	6	6
Kopsavilkums	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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.