Single-cell technologies have revolutionized biomedical research by enabling scalable measurement of the genome, transcriptome, proteome, and epigenome 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 artifacts. 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.

中文翻译：

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单细胞癌症基因组学的计算建模：方法和未来方向。

单细胞技术能够以单细胞分辨率对多个系统的基因组、转录组、蛋白质组和表观基因组进行可扩展的测量，从而彻底改变了生物医学研究。现在广泛应用于癌症模型，这些检测提供了对肿瘤异质性的新见解，这是癌症发生、进展和复发的基础。然而，单细胞分析产生的大量高维、嘈杂的数据会使数据分析复杂化，用技术伪影掩盖生物信号。在这篇评论文章中，我们概述了分析单细胞癌症基因组学数据的主要挑战，并调查了当前可用于解决这些问题的计算工具。