Cargando…
Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution
Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, the...
Autores principales: | Chen, Kylie, Moravec, Jiří C, Gavryushkin, Alex, Welch, David, Drummond, Alexei J |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356729/ https://www.ncbi.nlm.nih.gov/pubmed/35733333 http://dx.doi.org/10.1093/molbev/msac143 |
Ejemplares similares
-
Estimating Epidemic Incidence and Prevalence from Genomic Data
por: Vaughan, Timothy G, et al.
Publicado: (2019) -
Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data
por: Moravec, Jiří C., et al.
Publicado: (2023) -
Reliable Confidence Intervals for RelTime Estimates of Evolutionary Divergence Times
por: Tao, Qiqing, et al.
Publicado: (2020) -
Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data
por: Teng, Mingxiang, et al.
Publicado: (2017) -
Theoretical Foundation of the RelTime Method for Estimating Divergence Times from Variable Evolutionary Rates
por: Tamura, Koichiro, et al.
Publicado: (2018)