Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Single-cell ChIP-seq Peak Calling× | Single-cell RNA-seq Analyse× | |
|---|---|---|
| Vakgebied | Bio-informatica | Bio-informatica |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2019 | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| Grondlegger≠ | Grosselin et al.; Ku et al. (parallel independent development) | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| Type≠ | Epigenomic profiling pipeline | High-throughput single-cell transcriptomic profiling pipeline |
| Oorspronkelijke bron≠ | Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., ... & Vallot, C. (2019). High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nature Genetics, 51(6), 1060-1066. 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 ↗ |
| Aliassen | scChIP-seq peak calling, single-cell chromatin profiling, scChIC-seq analysis, single-cell epigenomic peak detection | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| Verwant | 5 | 5 |
| Samenvatting≠ | Single-cell ChIP-seq peak calling is a bioinformatics pipeline that identifies genomic regions enriched for histone modifications or transcription factor binding in individual cells. By profiling chromatin states at single-cell resolution, it reveals epigenomic heterogeneity hidden in bulk ChIP-seq experiments, enabling researchers to map regulatory landscapes across distinct cell populations within a complex tissue sample. | 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. |
| ScholarGateGegevensset ↗ |
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