  Single-cell technologies have revolutionized biomedical research by enabling
scalable measurement of the genome, transcriptome, and proteome of multiple
systems at single-cell resolution. Now widely applied to cancer models, these
assays offer new insights into tumour heterogeneity, which underlies cancer
initiation, progression, and relapse. However, the large quantities of
high-dimensional, noisy data produced by single-cell assays can complicate data
analysis, obscuring biological signals with technical artefacts. In this review
article, we outline the major challenges in analyzing single-cell cancer
genomics data and survey the current computational tools available to tackle
these. We further outline unsolved problems that we consider major
opportunities for future methods development to help interpret the vast
quantities of data being generated.
