bmVAE: a variational autoencoder method for clustering single-cell mutation data.
Bioinformatics
; 39(1)2023 01 01.
Article
em En
| MEDLINE
| ID: mdl-36478203
MOTIVATION: Genetic 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. RESULTS: We 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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Neoplasias
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article