Robust and generalizable CNV detection for single-cell sequencing assays

Abstract

Abstract Copy number variations (CNVs) are genomic structural variants that are strongly linked to cancer progression and genetic disorders. CNVs can be highly heterogeneous at population and tissue scale; thus, single-cell resolution detection holds great promise for studying clonal evolution and CNV-driven changes. Despite advanced sc-RNA-seq CNV detection methods, accurate methods for epigenomic single-cell modalities lag behind. We developed RIDDLER; a robust, unsupervised method that uses outlier-aware statistical modeling to detect CNVs across multiple single-cell modalities and assays. RIDDLER utilizes a robust regression framework to model the expected distribution of reads genome-wide by accounting for assay-specific biases, identifying CNVs as outliers from that distribution. This versatile framing allows deployment of RIDDLER in multiple modalities with appropriate bias features. We demonstrate the accuracy of RIDDLER in calling single-cell CNVs and dissecting clonal heterogeneity in sc-ATAC-seq and sc-methylation. RIDDLER is more accurate and more robust to data sparsity than competing methods. We illustrate useful applications of RIDDLER for dissection of clonal structure, identification of subclonal accessibility peaks, and multimodal integration from CNV structure. RIDDLER stands out as a scalable, generalizable multi-modal method for accurate CNV detection, empowering studies aiming to link CNV dynamics to epigenetic alterations within the same cell.