เปรียบเทียบวิธี

	การวิเคราะห์ Single-Cell RNA-seq แบบ Multi-omics	Gene Set Enrichment Analysis (GSEA)
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2015–2021 (rapid maturation with CITE-seq 2017; Seurat v4 2021)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
ผู้ริเริ่ม	Pioneered by Rahul Satija (Seurat), Oliver Stegle and John Marioni (MOFA+), and the broader single-cell genomics community	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
ประเภท	Integrative computational pipeline	Functional genomics / enrichment analysis
แหล่งต้นตำรับ	Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zager, M., Hoffman, P., Stoeckius, M., Papalexi, E., Mimitou, E. P., Jain, J., Srivastava, A., Stuart, T., Fleming, L. M., Yeung, B., Rogers, A. J., McElrath, J. M., Blish, C. A., Gottardo, R., Smibert, P., & Satija, R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573–3587.e29.	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	scMulti-omics, single-cell multi-omics, multimodal single-cell analysis, paired single-cell omics	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
ที่เกี่ยวข้อง	6	5
สรุป	Multi-omics single-cell RNA-seq analysis integrates two or more molecular layers — such as gene expression (scRNA-seq), chromatin accessibility (scATAC-seq), or surface protein abundance (CITE-seq) — measured simultaneously or co-profiled in the same individual cells. By aligning these modalities in a shared low-dimensional space, researchers gain a mechanistically richer picture of cell identity, regulatory state, and phenotype than any single assay can provide.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.