DCCNV: Enhanced CNV Detection in Single-Cell Sequencing Using Diffusion Process and Contrastive Learning

Mostafa Karami, Bingjun Li, Samson Weiner, Sahand Hamzehei, Sheida Nabavi

Abstract: Detecting copy number variations (CNVs) in single-cell DNA sequencing (scDNA-seq) data is challenging due to substantial noise and variability. To address this, we present DCCNV, a novel method that integrates diffusion processes, contrastive learning, and circular binary segmentation (CBS) for reliable CNV detection. Our method employs adaptive k-nearest neighbors (KNN) and multi-scale diffusion to reduce noise while preserving key biological signals, followed by contrastive learning to distinguish true genomic alterations from technical noise. The CBS algorithm is then used to partition the enhanced signals into discrete copy number segments. We compared the performance of DCCNV with those of several current single-cell CNV detection methods, including DeepCopy, rcCAE, SCOPE, SCONE, HMMcopy, SeCNV, as well as filtering-based CNV detection approaches that employ commonly used filters such as Wavelet, Median, and Gaussian filters. This comparison was conducted using both simulated and real data. The results show that DCCNV outperforms these approaches in terms of accuracy and computational efficiency. The code used in this research is publicly available at https://github.com/NabaviLab/DCCNV.

External IDs:doi:10.1145/3698587.3701395