เปรียบเทียบวิธี

	การวิเคราะห์ข้อมูล RNA ลำดับเซลล์เดี่ยวด้วยการเรียนรู้ของเครื่อง	การวิเคราะห์ความอุดมสมบูรณ์ของวิถีชีวภาพ
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2015-2018 (rapid expansion with scVI 2018, Seurat v3 2019)	2003–2005
ผู้ริเริ่ม	Nir Yosef, Fabian Theis, and colleagues (scVI/scANVI framework; broader community-driven)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
ประเภท	Computational analysis pipeline	Statistical functional annotation method
แหล่งต้นตำรับ	Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.	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	ML-scRNA-seq, deep learning scRNA-seq, AI-assisted scRNA-seq, ML-guided single-cell transcriptomics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
ที่เกี่ยวข้อง	6	6
สรุป	Machine learning-assisted single-cell RNA sequencing (scRNA-seq) analysis integrates supervised, unsupervised, and deep generative models into the standard scRNA-seq workflow to handle the unique challenges of single-cell data: extreme sparsity, high dimensionality, technical noise, and batch effects across experiments. Methods such as variational autoencoders (scVI), graph neural networks, and transfer learning substantially improve cell-type identification, trajectory inference, and cross-study data integration compared with purely statistical approaches.	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.