Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa GWAS wa seli moja× | Uchanganuzi wa RNA-seq wa seli moja× | |
|---|---|---|
| Nyanja | Bioinformatiki | Bioinformatiki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2019–2022 (rapid emergence with large-scale scRNA-seq atlases) | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| Mwanzilishi≠ | Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022 | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| Aina≠ | Integrative genomic analysis pipeline | High-throughput single-cell transcriptomic profiling pipeline |
| Chanzo asilia≠ | 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 ↗ | 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. DOI ↗ |
| Majina mbadala | sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
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