Vertaile menetelmiä
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| Bayesiläinen kopioanumeroanalyysi× | Single-cell Copy Number Variation Analysis× | |
|---|---|---|
| Tieteenala | Bioinformatiikka | Bioinformatiikka |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2004–2007 | 2011–2015 |
| Kehittäjä≠ | 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) |
| Tyyppi≠ | Probabilistic genomic analysis pipeline | Computational genomics pipeline |
| Alkuperäislähde≠ | 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. DOI ↗ | 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. link ↗ |
| Rinnakkaisnimet | 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 |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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