เปรียบเทียบวิธี

	การวิเคราะห์การแสดงออกที่แตกต่างกันของ RNA-seq โดยใช้เครือข่าย	การวิเคราะห์ความอุดมสมบูรณ์ของวิถีชีวภาพ
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2002–2005	2003–2005
ผู้ริเริ่ม	Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
ประเภท	Integrative computational pipeline	Statistical functional annotation method
แหล่งต้นตำรับ	Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article 17.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
ชื่อเรียกอื่น	network-aware DE analysis, gene network differential expression, co-expression network DE, NB-DEA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
ที่เกี่ยวข้อง	5	6
สรุป	Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meaningful gene modules that change together between conditions. This approach substantially reduces false positives and surfaces pathway-level signals invisible to gene-by-gene testing.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.