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Big data analytics in single‐cell transcriptomics: Five grand opportunities

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Abstract 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. This article is categorized under: Algorithmic Development > Biological Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning
Schematic representation of the emergent opportunities for big data analytics in the field of single‐cell transcriptomics
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(a) Standard steps involved in the downstream analysis of scRNA‐seq data. (b) Schematic diagram illustrating some of the widely adopted computational algorithms practiced for integrative analysis of multi‐modal scRNA‐seq data across species, conditions, and technology platforms
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Technologies > Machine Learning
Fundamental Concepts of Data and Knowledge > Big Data Mining
Algorithmic Development > Biological Data Mining

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