Módszerek összehasonlítása

	Egysejtes ChIP-seq csúcskeresés (Peak Calling)	Egysejtű RNS-szekvenálási analízis
Tudományterület	Bioinformatika	Bioinformatika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2019	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Megalkotó	Grosselin et al.; Ku et al. (parallel independent development)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Típus	Epigenomic profiling pipeline	High-throughput single-cell transcriptomic profiling pipeline
Alapmű	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.	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.
Alternatív nevek	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
Kapcsolódó	5	5
Összefoglaló	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.