So sánh phương pháp

	Nghiên cứu Hiệp hội Toàn bộ Hệ Gen trong Chuỗi Thời gian	Phân tích biểu hiện gen khác biệt RNA-seq
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2010s	2008–2010 (RNA-seq DE methodology established)
Người khởi xướng	Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010s	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Loại	Longitudinal epigenomic association pipeline	Quantitative genomics pipeline
Công trình gốc	Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., ... & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208.	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.
Tên gọi khác	time-series EWAS, longitudinal EWAS, repeated-measures EWAS, dynamic methylation association study	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Liên quan	3	6
Tóm tắt	A time-series epigenome-wide association study (time-series EWAS) extends the classic cross-sectional EWAS design to longitudinal settings, measuring DNA methylation across the entire epigenome at multiple time points within the same subjects. The goal is to identify CpG sites whose methylation levels change systematically over time, or to characterise how epigenetic associations with an exposure or phenotype evolve across developmental stages, treatment periods, or disease trajectories.	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.