Methoden vergleichen

	Phylogenetische Einzelzellanalyse	Analyse von Kopienzahlvariationen (CNV) – Erkennung und Interpretation von CNVs
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2014-2020 (rapid development period)	1998–2006
Urheber	Multiple groups; foundational tools: Trapnell et al. (Monocle, 2014), Jones et al. (Cassiopeia, 2020)	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Typ	Computational phylogenetic inference pipeline	Genomic structural variant detection pipeline
Wegweisende Quelle	Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C., Weissman, J. S., & Yosef, N. (2020). Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21(1), 92.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Aliasnamen	scPhylogeny, single-cell lineage tracing, clonal phylogenetics, single-cell tree inference	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Verwandt	4	6
Zusammenfassung	Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relationships within tumors, developing tissues, or immune repertoires with unprecedented cellular resolution.	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.