เปรียบเทียบวิธี

	การวิเคราะห์เมแทบอโลมิกส์	การแสดงออกแตกต่างกันของ RNA-seq
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1998–2002	2008–2010 (RNA-seq DE methodology established)
ผู้ริเริ่ม	Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
ประเภท	Quantitative omics pipeline	Quantitative genomics pipeline
แหล่งต้นตำรับ	Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.	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.
ชื่อเรียกอื่น	metabolome profiling, metabolic profiling, metabonomics, metabolite profiling	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
ที่เกี่ยวข้อง	6	6
สรุป	Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real 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.