Comparer des méthodes

	Analyse phylogénétique unicellulaire	Analyse des variations du nombre de copies
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2014-2020 (rapid development period)	1998–2006
Auteur d'origine	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)
Type	Computational phylogenetic inference pipeline	Genomic structural variant detection pipeline
Source fondatrice	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.
Alias	scPhylogeny, single-cell lineage tracing, clonal phylogenetics, single-cell tree inference	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Apparentées	4	6
Résumé	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.