قارن الطرق

	تحليل إثراء المجموعات الجينية أحادي الخلية (scGSEA)	تحليل تسلسل الحمض النووي الريبوزي للخلايا المفردة
المجال	المعلوماتية الحيوية	المعلوماتية الحيوية
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2017-2019	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
صاحب الطريقة	Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
النوع	Computational enrichment scoring pipeline	High-throughput single-cell transcriptomic profiling pipeline
المصدر التأسيسي	Aibar, S., Gonzalez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Kalender Atak, Z., Wouters, J., & Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083-1086.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
الأسماء البديلة	scGSEA, single-cell GSEA, cell-level gene set scoring, scRNA-seq pathway scoring	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
ذات صلة	5	5
الملخص	Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogeneous cell populations, cell states, and developmental trajectories captured in single-cell RNA-seq data.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.