MaCroDNA: Accurate integration of single-cell DNA and RNA data for a deeper understanding of tumor heterogeneity

Cancers develop, proliferate, and metastasize as mutations accumulate, and with the advent of single-cell DNA sequencing, researchers can observe these mutations with remarkable temporal and spatial precision. Single-cell RNA sequencing can measure the direct alterations caused by mutations to the quantity or quality of RNA and predicted protein products. However, to connect genomic mutations with their transcriptomic consequences, cells with only DNA data and cells with only RNA data must be mapped to a common domain. We have developed a novel method to perform this mapping called MaCroDNA, which uses maximum weighted bipartite matching of copy number profiles and gene expression from single-cell DNA and RNA-seq, respectively. Using an empirical colorectal cancer data set for which the ground truth is partially known, we demonstrate that MaCroDNA is highly accurate and extremely fast, while the alternative methods for performing this mapping (clonealign and Seurat) are highly inaccurate, contradicting previously reported accuracy results on simulated data.