t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis
t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis
复制标题
DOI：
10.1016/j.margen.2019.100723
复制
发表时间：
2020-06-01
期刊：
影响因子：
1.9
通讯作者：
Hartline, Daniel K.
中科院分区：
文献类型：
--
作者：
Cieslak, Matthew C.;Castelfranco, Ann M.;Hartline, Daniel K.
高通量RNA测序(RNA-Seq)改变了对单个浮游生物物种和群落的生态生理学评估。然而，这项技术会产生由数百万个短阅读序列组成的复杂数据，这些序列可能很难分析和解释。需要新的生物信息学工作流程来指导实验、环境采样以及开发和测试假说。一种在其他领域得到成功应用的降低复杂性的工具是t-分布式随机邻居嵌入(t-SNE)。它在海洋、远洋和底栖生物系统转录数据中的应用还有待探索。本研究使用先前发表的对桡足类Calanus finmarchicus和Neocalanus flemingeri的常规分析研究，证明了一种用于评估RNA-Seq数据的应用。在一个应用中，比较了不同发育阶段的基因表达谱。在另一项研究中，他们在不同的实验条件下进行了比较。在第三组中，他们在来自不同地点的环境样本中进行了比较。通过参考已发表的使用差异基因表达和基因本体论(GO)分析的结果，验证了t-SNE确定的特征类别。这些分析展示了如何评估单个样本全球基因表达的差异，以及与特定生物过程相关的表达差异，如脂肪代谢和对压力的反应。随着来自浮游生物物种和群落的RNA-Seq数据变得越来越普遍，t-SNE分析应该提供一种强大的工具，用于确定趋势并将样本归类到具有相似转录生理的组中，而不受采集地点或时间的影响。
High-throughput RNA sequencing (RNA-Seq) has transformed the ecophysiological assessment of individual plankton species and communities. However, the technology generates complex data consisting of millions of short-read sequences that can be difficult to analyze and interpret. New bioinformatics workflows are needed to guide experimentation, environmental sampling, and to develop and test hypotheses. One complexity-reducing tool that has been used successfully in other fields is "t-distributed Stochastic Neighbor Embedding" (t-SNE). Its application to transcriptomic data from marine pelagic and benthic systems has yet to be explored. The present study demonstrates an application for evaluating RNA-Seq data using previously published, conventionally analyzed studies on the copepods Calanus finmarchicus and Neocalanus flemingeri. In one application, gene expression profiles were compared among different developmental stages. In another, they were compared among experimental conditions. In a third, they were compared among environmental samples from different locations. The profile categories identified by t-SNE were validated by reference to published results using differential gene expression and Gene Ontology (GO) analyses. The analyses demonstrate how individual samples can be evaluated for differences in global gene expression, as well as differences in expression related to specific biological processes, such as lipid metabolism and responses to stress. As RNA-Seq data from plankton species and communities become more common, t-SNE analysis should provide a powerful tool for determining trends and classifying samples into groups with similar transcriptional physiology, independent of collection site or time.