مقایسهٔ روش‌ها

	تحلیل تنوع میکروبیوم سری‌های زمانی	Pathway Enrichment Analysis
حوزه	زیست‌اطلاعاتی	زیست‌اطلاعاتی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)	2003–2005
پدیدآور	Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues	Mootha et al. (2003); systematised by Subramanian et al. (2005)
نوع	Longitudinal observational / bioinformatics pipeline	Statistical functional annotation method
منبع بنیادین	Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583.	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.
نام‌های دیگر	longitudinal microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
مرتبط	5	6
خلاصه	Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.	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.