Computational modelling in single-cell cancer genomics: methods and future directions
Computational modelling in single-cell cancer genomics: methods and future directions
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DOI：
10.1088/1478-3975/abacfe
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发表时间：
2020-05
期刊：
影响因子：
2
通讯作者：
Allen W. Zhang;Kieran R. Campbell
中科院分区：
文献类型：
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作者：
Allen W. Zhang;Kieran R. Campbell
单细胞技术通过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测量，彻底改变了生物医学研究。这些检测方法现已广泛应用于癌症模型，为肿瘤异质性提供了新的见解，而肿瘤异质性是癌症发生、进展和复发的基础。然而，单细胞测定产生的大量高维、嘈杂数据可能会使数据分析变得复杂，并用技术伪影模糊生物信号。在这篇综述文章中，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。我们进一步概述了未解决的问题，我们认为这些问题是未来方法开发的主要机会，以帮助解释正在生成的大量数据。
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.