RNA序列
计算生物学
单细胞分析
透视图(图形)
生物
细胞
缩放比例
计算机科学
指数增长
基因表达
核糖核酸
基因
转录组
遗传学
人工智能
数学
数学分析
几何学
作者
Valentine Svensson,Roser Vento‐Tormo,Sarah A. Teichmann
出处
期刊:Nature Protocols
[Springer Nature]
日期:2018-03-01
卷期号:13 (4): 599-604
被引量:785
标识
DOI:10.1038/nprot.2017.149
摘要
Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.
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