ArticleFrontiers in genetics2021
GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Inferring single-cell copy number profiles through cross-cell segmentation of read counts.BMC genomics · 2024Article
- Assessing the performance of methods for cell clustering from single-cell DNA sequencing data.PLoS computational biology · 2023Article
- bmVAE: a variational autoencoder method for clustering single-cell mutation data.Bioinformatics (Oxford, England) · 2023Article
- SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data.Frontiers in genetics · 2022Article
- SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder.Frontiers in genetics · 2021Article
- Editorial: Unsupervised Learning Models for Unlabeled Genomic, Transcriptomic & Proteomic Data.Frontiers in genetics · 2021Article
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Authors and funding
4 authors.
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Abstract
Single-cell sequencing (SCS) now promises the landscape of genetic diversity at single cell level, and is particularly useful to reconstruct the evolutionary history of tumor. There are multiple types of noise that make the SCS data notoriously error-prone, and significantly complicate tumor tree reconstruction. Existing methods for tumor phylogeny estimation suffer from either high computational intensity or low-resolution indication of clonal architecture, giving a necessity of developing new methods for efficient and accurate reconstruction of tumor trees. We introduce GRMT (Generative Reconstruction of Mutation Tree from scratch), a method for inferring tumor mutation tree from SCS data. GRMT exploits the
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