Σύγκριση μεθόδων

	Ανάλυση Βαϋεσιανής Παραλλαγής Αριθμού Αντιγράφων	Ανάλυση Μεταλλάξεων Αριθμού Αντιγράφων Μοναδιαίου Κυττάρου
Πεδίο	Βιοπληροφορική	Βιοπληροφορική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2004–2007	2011–2015
Δημιουργός	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)
Τύπος	Probabilistic genomic analysis pipeline	Computational genomics pipeline
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	6	6
Σύνοψη	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.