BmVAE: a variational autoencoder method for clustering single-cell mutation data.

ABSTRACT:

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

SUBMITTER: Yan J 

PROVIDER: S-EPMC9825778 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature