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.

高通量RNA测序（RNA-Seq）已改变了单个浮游生物物种和群落的生态生理评估。但是，该技术会生成由数百万个短读序列组成的复杂数据，这些序列可能难以分析和解释。需要新的生物信息学工作流程来指导实验，环境采样以及发展和检验假设。一种已在其他领域成功使用的降低复杂性的工具是“ t分布随机邻居嵌入”（t-SNE）。它在海洋中上层和底栖系统的转录组数据中的应用尚待探索。本研究证明了使用先前发表的关于the足类Calaus finmarchicus和and足类的常规分析研究评估RNA-Seq数据的应用。新cal。在一项应用中，比较了不同发育阶段的基因表达谱。在另一个实验中，将它们进行了比较。第三，对来自不同地点的环境样本进行了比较。通过使用差异基因表达和基因本体论（GO）分析，参考已发表的结果验证了通过t-SNE鉴定的谱类别。分析表明如何评估单个样本的总体基因表达差异以及与特定生物过程（如脂质代谢和应激反应）相关的表达差异。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，