MintCNA: A Unified Framework for Integrative Copy Number Profiling with Single-Cell Multi-Omics Data

Chromosomal copy number alterations (CNAs) are key drivers of tumor evolution, disease progression and therapeutic resistance, and the identification of them is an important step to delineate tumor clonal structure. However, accurately resolving CNA landscapes from single-cell data remains challenging. Most existing tools analyze one omics layer at a time and are susceptible to assay-specific noises, limiting their ability to recover shared or modality-specific CNAs. Recent single-cell multi-omics techniques enable joint sequencing of multiple molecular layers in the same cells, yet in silico methods that fully exploit such complementary multi-modal data for CNA analysis are still missing. Here we present a single-cell multi-omics integration framework, MintCNA, a unified framework for CNA detection from paired multi-omics data. MintCNA integrates traditional statistical modeling with embedded deep learning structure to enhance CNA profiling across multi-omics. We use an attention-guided convolutional autoencoder for data denoising and perform multivariate change-point detection utilizing a sliding-window screening and ranking procedure. Missingness-adjusted CUSUM statistics are constructed which jointly aggregate omics features by a data-adaptive projection to detect genome-wide chromosomal breakpoints. Across various simulations and applications to a colorectal cancer multi-omics dataset, MintCNA consistently outperforms existing single-omics CNA callers in detection accuracy. MintCNA provides a single-cell CNA tool that integrates paired scDNA-seq and scRNA-seq, supporting the study of intra-tumor heterogeneity and tumor evolution.