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| Single-cell GWAS× | Analiza ekspresji różnicowej RNA-seq× | |
|---|---|---|
| Dziedzina | Bioinformatyka | Bioinformatyka |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2019–2022 (rapid emergence with large-scale scRNA-seq atlases) | 2008–2010 (RNA-seq DE methodology established) |
| Twórca≠ | Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022 | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Typ≠ | Integrative genomic analysis pipeline | Quantitative genomics pipeline |
| Źródło pierwotne≠ | Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., ... & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(8), 1224-1234. link ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| Inne nazwy | sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside GWAS summary statistics, it moves beyond tissue-level associations to reveal the precise cellular contexts in which disease-associated genetic variants exert their effects. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGateZbiór danych ↗ |
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