Cancers are often heterogenous, composed of multiple sub-clonal populations. This increases the likelihood that a subpopulation will be present which is capable of drug resistance and/or metastasis, and can significantly affect patient prognosis. Clustering and differential expression analysis of single-cell RNA sequencing (scRNA-Seq) data can be used to distinguish between cancer cells with differing expression profiles, however it cannot reveal whether these apparent subpopulations are genomically distinct or whether they are simply following different transcriptional programs. We have benchmarked and used a tool known as InferCNV (Tirosh et. al., Nature 2016) to infer copy-number variation (CNV) in breast cancer epithelial cells from over 25 scRNA-Seq datasets representing all major breast cancer subtypes (ER+, HER2+, triple-negative breast cancer (TNBC), metaplastic). Our results indicate diverse genomic heterogeneity within breast tumours ranging from 5+ subpopulations in some ER+ and TNBC samples to monoclonality in other ER+ and some HER2+ breast cancer samples. While some subclonal populations display a uniform transcriptional profile, the majority fall into multiple expression-based clusters representing multiple transcriptional programs running within one population. Interestingly, the entire repertoire of cancer cells in a metastasis were found to share the same CNV profile as one of the subclonal populations in the matched primary tumour, indicating the metastasis arose from that clone. Further characterisation of breast cancer subpopulations will give insights into key differences between them and reveal how CNV affects gene expression, tumour progression and metastatic potential. This may lead to identification of unique therapeutic targets which could be exploited to treat the complete repertoire of cancer cells within a tumour, rather than targeting only the most prominent subpopulation.