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

Overview
Titlet-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis
AuthorsCieslak MC, Castelfranco AM, Roncalli V, Lenz PH, Hartline DK
TypeJournal Article
Journal NameMarine genomics
VolumeN/A
IssueN/A
Year2019
Page(s)100723
CitationCieslak MC, Castelfranco AM, Roncalli V, Lenz PH, Hartline DK. t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis. Marine genomics. 2019 Nov 26; 100723.

Abstract

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.

Properties
Additional details for this publication include:
Property NameValue
Journal CountryNetherlands
Publication TypeJournal Article
Language Abbreng
LanguageEnglish
CopyrightCopyright © 2019 The Author(s). Published by Elsevier B.V. All rights reserved.
DOI10.1016/j.margen.2019.100723
Elocation10.1016/j.margen.2019.100723
PIIS1874-7787(19)30174-6
Journal AbbreviationMar Genomics
Publication Date2019 Nov 26
eISSN1876-7478
ISSN1876-7478
Publication ModelPrint-Electronic
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PMID: PMID:31784353