Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vienšūnu eQTL analīze× | Vienšūnas RNS sekvencēšanas analīze× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2020 | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| Autors≠ | Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020) | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| Tips≠ | Statistical genomics pipeline | High-throughput single-cell transcriptomic profiling pipeline |
| Pirmavots≠ | Cuomo, A. S. E., et al. (2020). Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Communications, 11(1), 810. 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 ↗ |
| Citi nosaukumi | sc-eQTL analysis, single-cell eQTL mapping, scRNA-seq eQTL, cell-type-specific eQTL | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Single-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling discovery of variants whose effects are confined to particular cell states or developmental stages. | 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. |
| ScholarGateDatu kopa ↗ |
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