השוואת שיטות

	ניתוח שונות מספר העתקים בזמן	ניתוח ביטוי דיפרנציאלי ב-RNA-seq
תחום	ביואינפורמטיקה	ביואינפורמטיקה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2010s–present	2008–2010 (RNA-seq DE methodology established)
הוגה השיטה	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)
סוג	Computational genomics pipeline	Quantitative genomics pipeline
מקור מכונן	Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.	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.
כינויים	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
קשורות	5	6
תקציר	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.