Confronta i metodi

	Chiamata di varianti a singola cellula	Analisi delle Variazioni del Numero di Copie
Campo	Bioinformatica	Bioinformatica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2016 (Monovar; foundational single-cell SNV calling)	1998–2006
Ideatore	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Tipo	Computational genomics pipeline	Genomic structural variant detection pipeline
Fonte seminale	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.
Alias	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
Correlati	1	6
Sintesi	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.