Comparer des méthodes

	Analyse bayésienne des variations du nombre de copies	Analyse des variations du nombre de copies unicellulaires
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2004–2007	2011–2015
Auteur d'origine	Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)
Type	Probabilistic genomic analysis pipeline	Computational genomics pipeline
Source fondatrice	Colella, S., Yau, C., Taylor, J. M., Mirza, G., Butler, H., Clouston, P., Bassett, A. S., Seller, A., Holmes, C. C., & Ragoussis, J. (2007). QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Research, 35(6), 2013–2025.	Garvin, T., Aboukhalil, R., Kendall, J., Baslan, T., Atwal, G. S., Hicks, J., Wigler, M., & Schatz, M. C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature Methods, 12(11), 1058–1060.
Alias	Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis
Apparentées	6	6
Résumé	Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approach yields posterior probabilities for each copy-number state along the genome, providing statistically principled uncertainty quantification that frequentist segmentation methods lack.	Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-genome sequencing data directly or inferred from read-depth signals in scRNA-seq or scATAC-seq experiments.