Сравнение методов

	Вызов вариантов на уровне одиночных клеток	Анализ вариаций числа копий
Область	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Год появления	2016 (Monovar; foundational single-cell SNV calling)	1998–2006
Автор метода	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Тип	Computational genomics pipeline	Genomic structural variant detection pipeline
Основополагающий источник	Zafar, H., Wang, Y., Nakhleh, L., Navin, N., & Chen, K. (2016). Monovar: single-nucleotide variant detection in single cells. Nature Methods, 13(6), 505–507.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Другие названия	scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Связанные	1	6
Сводка	Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it reveals intra-tumoral heterogeneity, clonal architecture, and somatic mutation patterns that bulk sequencing obscures. The approach is central to cancer genomics, developmental biology, and any study where cell-to-cell genetic diversity is the primary question.	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.