CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics
Semih Kurt, Mandi Chen, Hosein Toosi, Xinsong Chen, Camilla Engblom, Jeff Mold, Johan Hartman, Jens LagergrenAbstract
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 copy number variations (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 advance our understanding of genetic alterations and their impact on disease advancement.
Availability and implementation
CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE
Supplementary information
Supplementary data are available at Bioinformatics online.