ArticleBioinformatics (Oxford, England)2023
bmVAE: a variational autoencoder method for clustering single-cell mutation data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Deep Generative and Graph-Based Representation Learning for Multiomics Survival Stratification in Ovarian Cancer: Secondary Analysis.JMIR bioinformatics and biotechnology · 2026Article
- Unveiling tissue heterogeneity through genomic interaction-encoded image representation of RNA-sequencing data.American journal of human genetics · 2025Article
- CluVar: clustering of variants using autoencoder for inferring cancer subclones from single cell RNA sequencing data.Briefings in bioinformatics · 2025Article
- SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational autoencoder.Genome biology · 2025Article
- sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models.bioRxiv : the preprint server for biology · 2025Article
- Comparing factor mixture modeling and conditional Gaussian mixture variational autoencoders for cognitive profile clustering.Frontiers in psychology · 2025Article
- scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.Briefings in bioinformatics · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.BMC genomics · 2024Article
- CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.Briefings in bioinformatics · 2024Article
- Inferring single-cell copy number profiles through cross-cell segmentation of read counts.BMC genomics · 2024Article
- Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.Briefings in bioinformatics · 2023Article
- Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data.BMC bioinformatics · 2023Article
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Abstract
motivationGenetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data.
resultsWe introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. AVAILABILITY AND IMPLEMENTATION: bmVAE is freely available at https://github.com/zhyu-lab/bmvae. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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