自编码
拷贝数变化
推论
变化(天文学)
计算机科学
算法
人工智能
模式识别(心理学)
数学
生物
遗传学
人工神经网络
基因
天体物理学
物理
基因组
作者
Semih Kurt,Mandi Chen,Hosein Toosi,Xinsong Chen,Camilla Engblom,Jeff E. Mold,Johan Hartman,Jens Lagergren
出处
期刊:PubMed
日期:2024-04-27
标识
DOI:10.1093/bioinformatics/btae284
摘要
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.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.CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE.Supplementary data are available at Bioinformatics online.
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