ArticleMolecular biology and evolution2022
Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 19 citations in OpenAlex.
- Phylogenetic and phylodynamic approaches to understanding the evolution of non-small cell lung cancer using single-cell sequencing.Oncogene · 2026Article
- A single-cell lens into the co-evolution of genotypes and phenotypes in cancer.Nature reviews. Cancer · 2026Review
- LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.Bioinformatics (Oxford, England) · 2026Article
- SciPhy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data.Nature communications · 2026Article
- Age-dependent Phylodynamics with Application to Single-cell Lineage Trees.Molecular biology and evolution · 2026Article
- LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.bioRxiv : the preprint server for biology · 2026Article
- Practical Guide and Review of Fossil Tip-Dating in Phylogenetics.Systematic biology · 2026Review
- Bayesian Model Averaging of Parametric Coalescent Models for Phylodynamic Inference.Molecular biology and evolution · 2025Article
- Large-scale single-cell phylogenetic mapping of clonal evolution in the human aging esophagus.bioRxiv : the preprint server for biology · 2025Article
- A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent target sites.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Estimating the mean in the space of ranked phylogenetic trees.Bioinformatics (Oxford, England) · 2024Article
- CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era.Molecular biology and evolution · 2024Article
- Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors.PLoS computational biology · 2023Article
- Phylogenetic inference from single-cell RNA-seq data.Scientific reports · 2023Article
- LinguaPhylo: A probabilistic model specification language for reproducible phylogenetic analyses.PLoS computational biology · 2023Article
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5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, thereby preserving the information about the origin of the sequences. However, single-cell data are more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.