EMcnv: enhancing CNV detection performance through ensemble strategies with heterogeneous meta-graph neural networks

Copy number variation (CNV) is a crucial biomarker for many complex traits and diseases. Although numerous CNV detection tools are available, no single method consistently achieves optimal performance across diverse sequencing samples, as each tool has distinct advantages and limitations. Therefore, integrating the strengths of these tools to improve CNV detection accuracy is both a promising strategy and a significant challenge. To address this, we propose EMcnv, a novel deep ensemble framework based on meta-learning. EMcnv combines multiple CNV detection strategies through a three-step approach: (i) leveraging meta-learning and meta-path heterogeneous graphs, employing Relational Graph Convolutional Networks as a specific model within the Heterogeneous Graph Neural Networks framework to develop a probabilistic weight meta-model that ensembles various CNV detection strategies; (ii) assigning probabilistic weights to calls from different CNV detection tools and aggregating them into weighted CNV regions (CNVRs); (iii) refining Copy number variations based on weighted CNVRs. We conducted comprehensive experiments on both simulated and real sequencing data using benchmark datasets. The results demonstrate that EMcnv significantly outperforms popular existing methods, underscoring its superiority and importance in CNV detection. To support further research, the source code is available for academic use at https://github.com/Sherwin-xjtu/EMcnv.