Extended Data Fig. 1. Overview of components of the scNOVA computational workflow.

scNOVA employs single cell tri-channel processing (scTRIP) as realized in the MosaiCatcher pipeline to perform haplotype-aware somatic SV discovery²⁴. Modules of scNOVA enable single-cell mulitomics of these somatic SVs, including inference of haplotype-specific NO to investigate local (cis) effect of SVs, and inference of altered gene/pathway activity to investigate global (trans) effect of SVs detectable between geneticlaly distinct subclones. To infer alterations in gene activity, scNOVA integrates deep convolutional neural network (CNN) based machine learning, and negative binomial generalized linear models. The framework dissects intra-sample genetic heterogeneity at single-cell resolution, measures the local haplotype-specific impact of somatic SVs, can be used to explore global gene dysregulation in SV-containing cells, can discriminate between genetically-distinct subclones, and can uncover shared functional consequences of heterogeneous SVs affecting the same chromosomal interval.