方法对比

	单细胞ChIP-seq峰值调用	表观基因组关联研究 (EWAS)
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2019	2008–2011 (term and framework established c. 2011)
提出者	Grosselin et al.; Ku et al. (parallel independent development)	Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application
类型	Epigenomic profiling pipeline	Population-scale epigenomic association study
开创性文献	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.	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.
别名	scChIP-seq peak calling, single-cell chromatin profiling, scChIC-seq analysis, single-cell epigenomic peak detection	EWAS, methylome-wide association study, epigenetic association study, DNA methylation association study
相关	5	5
摘要	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.	An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture.