مقایسهٔ روشها
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| تحلیل سری زمانی تکسلولی RNA-seq× | تحلیل بیان افتراقی RNA-seq× | |
|---|---|---|
| حوزه | زیستاطلاعاتی | زیستاطلاعاتی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 2014-2018 (pseudotime and RNA velocity frameworks) | 2008–2010 (RNA-seq DE methodology established) |
| پدیدآور≠ | 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) |
| نوع≠ | Computational bioinformatics pipeline | Quantitative genomics pipeline |
| منبع بنیادین≠ | 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. DOI ↗ | 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. DOI ↗ |
| نامهای دیگر | 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 |
| مرتبط | 6 | 6 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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