ArticleBioinformatics (Oxford, England)2019
Inference of clonal selection in cancer populations using single-cell sequencing data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors.bioRxiv : the preprint server for biology · 2026Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- Single-cell RNA-seq reveals diverse molecular signatures associated with Methotrexate resistance in primary central nervous system lymphoma cells.Journal of neuro-oncology · 2025Article
- Single-cell DNA sequencing reveals pervasive positive selection throughout preleukemic evolution.Cell genomics · 2025Article
- Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.Toxicology and applied pharmacology · 2024Article
- Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.Nature communications · 2023Article
- Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo.Nature communications · 2021Article
- Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Inference of mutability landscapes of tumors from single cell sequencing data.PLoS computational biology · 2020Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
summaryIntra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. AVAILABILITY AND IMPLEMENTATION: Its source code is available at https://github.com/compbel/SCIFIL.
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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.