قارن الطرق

	تحليل تسلسل الحمض النووي الريبوزي أحادي الخلية متعدد الأوميكس	تحليل إثراء المسارات
المجال	المعلوماتية الحيوية	المعلوماتية الحيوية
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2015–2021 (rapid maturation with CITE-seq 2017; Seurat v4 2021)	2003–2005
صاحب الطريقة	Pioneered by Rahul Satija (Seurat), Oliver Stegle and John Marioni (MOFA+), and the broader single-cell genomics community	Mootha et al. (2003); systematised by Subramanian et al. (2005)
النوع	Integrative computational pipeline	Statistical functional annotation method
المصدر التأسيسي	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.
الأسماء البديلة	scMulti-omics, single-cell multi-omics, multimodal single-cell analysis, paired single-cell omics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
ذات صلة	6	6
الملخص	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.	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.