t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis
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t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis

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  • Journal Title:
    Marine Genomics
  • Personal Author:
  • NOAA Program & Office:
    NOAA General Documents
  • Description:
    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.
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  • Source:
    Marine Genomics, 51, 100723
  • DOI:
  • ISSN:
    1874-7787
  • Format:
    PDF
  • Publisher:
    Elsevier BV
  • Document Type:
    Journal Article
  • License:
  • Rights Information:
    CC BY
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    Library
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