Big data analytics in single-cell transcriptomics: Five grand opportunities,WIREs Data Mining and Knowledge Discovery

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Big data analytics in single-cell transcriptomics: Five grand opportunities
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-05-11 , DOI: 10.1002/widm.1414
Namrata Bhattacharya _{1,

2} , Colleen C. Nelson _{2,

3} , Gaurav Ahuja ₄ , Debarka Sengupta _{1,

2,

4,

5}

Affiliation

Single-cell omics technologies provide biologists with a new dimension for systematically dissecting the underlying complexities within biological systems. These powerful technologies have triggered a wave of rapid development and deployment of new computational tools capable of teasing out critical insights by analysis of large volumes of omics data at single-cell resolution. Some of the key advancements include identifying molecular signatures imparting cellular identities, their evolutionary relationships, identifying novel and rare cell-types, and establishing a direct link between cellular genotypes and phenotypes. With the sharp increase in the throughput of single-cell platforms, the demand for efficient computational algorithms has become prominent. As such, devising novel computational strategies is critical to ensure optimal use of this wealth of molecular data for gaining newer insights into cellular biology. Here we discuss some of the grand opportunities of computational breakthroughs which would accelerate single-cell research. These are: predicting cellular identity, single-cell guided in silico drug screening for precision medicine, transfer learning methods to handle sparsity and heterogeneity of expression data, establishing genotype–phenotype relationships at single-cell resolution, and developing computational platforms for handling big data.

中文翻译：

单细胞转录组学中的大数据分析：五个重大机遇

单细胞组学技术为生物学家提供了一个新的维度来系统地剖析生物系统内的潜在复杂性。这些强大的技术引发了新计算工具的快速开发和部署浪潮，这些工具能够通过以单细胞分辨率分析大量组学数据来梳理出关键见解。一些关键的进步包括识别赋予细胞特性的分子特征、它们的进化关系、识别新的和稀有的细胞类型，以及在细胞基因型和表型之间建立直接联系。随着单细胞平台吞吐量的急剧增加，对高效计算算法的需求变得突出。因此，设计新颖的计算策略对于确保优化利用这些丰富的分子数据以获得对细胞生物学的新见解至关重要。在这里，我们讨论了一些将加速单细胞研究的计算突破的重大机遇。它们是：预测细胞身份、用于精确医学的单细胞引导计算机药物筛选、转移学习方法来处理表达数据的稀疏性和异质性、在单细胞分辨率下建立基因型-表型关系，以及开发用于处理大数据的计算平台.

更新日期：2021-06-10

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