CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics.

ABSTRACT:

Motivation

Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity.

Results

To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE's potential to

SUBMITTER: Kurt S 

PROVIDER: S-EPMC11087824 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature