论文标题
单细胞癌基因组学中的计算建模：方法和未来方向
Computational modelling in single-cell cancer genomics: methods and future directions
论文作者
论文摘要
单细胞技术通过在单细胞分辨率下对多个系统的基因组，转录组和蛋白质组进行可扩展测量，从而彻底改变了生物医学研究。这些测定法现在广泛应用于癌症模型，为肿瘤异质性提供了新的见解，肿瘤异质性是癌症开始，进展和复发的基础。但是，单细胞测定产生的大量高维嘈杂数据会使数据分析复杂化，从而使生物学信号用技术人工制品掩盖。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，并调查了可解决这些问题的当前计算工具。我们进一步概述了未解决的问题，我们考虑了未来方法开发的主要机会，以帮助解释生成的大量数据。
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.
