Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Tidsserieanalys av kopienummervariationer× | RNA-seq Differential Expression× | |
|---|---|---|
| Ämnesområde | Bioinformatik | Bioinformatik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2010s–present | 2008–2010 (RNA-seq DE methodology established) |
| Upphovsperson≠ | Developed from foundational CNV methods (Olshen et al. 2004; Ding et al. 2010) extended to longitudinal tumor genomics frameworks | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Typ≠ | Computational genomics pipeline | Quantitative genomics pipeline |
| Ursprungskälla≠ | Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254. link ↗ | 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 ↗ |
| Alias | longitudinal CNV analysis, temporal copy number analysis, time-series CNV profiling, serial CNV analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Närliggande≠ | 5 | 6 |
| Sammanfattning≠ | Time-series copy number variation (CNV) analysis is a computational genomics pipeline that characterizes chromosomal gains and losses across multiple sequential samples from the same individual or tumor. By comparing copy number profiles at successive time points — such as diagnosis, mid-treatment, relapse — it reconstructs the clonal dynamics and evolutionary trajectories driving genome instability, enabling researchers to track how sub-populations expand, contract, or acquire new aberrations over time. | 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. |
| ScholarGateDatamängd ↗ |
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