转录组
计算生物学
单细胞测序
单细胞分析
生物
核糖核酸
细胞
DNA测序
RNA序列
人口
仿形(计算机编程)
计算机科学
遗传学
基因
基因表达
表型
外显子组测序
医学
环境卫生
操作系统
作者
Serena Liu,Cole Trapnell
出处
期刊:F1000Research
[Faculty of 1000]
日期:2016-02-17
卷期号:5: 182-182
被引量:243
标识
DOI:10.12688/f1000research.7223.1
摘要
Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.
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