Supplemental Data and Datasets to Publication: Kraemer S, Busch W, Schuettler A (2019): A self-organizing map of the fathead minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints. Environmental Toxicology and Chemistry 0 7457

Project
POF3 - T42 - Exposome - ProxiesOfTheEcoexposome - FHMSOM
Description
Title: A self-organizing map of the fathead-minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints
Abstract: Lack of consistent findings in different experimental settings remains to be a major challenge in toxicogenomics. The present study investigated, whether consistency between findings of different microarray experiments can be improved when the analysis is based on a common reference frame (“toxicogenomic universe”), which can be generated using the machine learning-algorithm of the self-organizing map (SOM). This algorithm arranges and clusters genes on a two-dimensional grid according to their similarity in expression across all considered data. In the present study, nineteen data sets, comprising of 54 different adult fathead minnow liver exposure experiments, were retrieved from Gene Expression Omnibus and used to train a SOM. The resulting toxicogenomic universe aggregates 58,872 probes to 2,500 nodes and was used to project, visualize and compare the fingerprints of these 54 different experiments. For example, we could identify a common pattern, with 14% of significantly regulated nodes in common, in the data sets of an interlaboratory study of ethinylestradiol exposures, previously published by Feswick et al. (2017). Consistency could be improved compared to the 5% total overlap in regulated genes reported before. Furthermore, we could determine a specific and consistent estrogen-related pattern of differentially expressed nodes and clusters in the toxicogenomic universe applying additional clustering steps and comparing all obtained fingerprints. This study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments.
Journal: Environmental Toxicology and Chemistry
Keywords: self-organizing map, estrogens, consistency, gene expression, toxicogenomics
Size
89,6 MB
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Metadata Catalogues
Dublin Core
URI
http://www.ufz.de/record/dmp/archive/7457
Subject
zip-file (containing csv-Tables, Figures (pdf), R-markdown, text-documents)
Coverage
Publication Kraemer S, Busch W, Schuettler A (2019): A self-organizing map of the fathead minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints. Environmental Toxicology and Chemistry (accepted)
Type of data
collection of various data
Data quality
quality assured data
Status of data
processed data
Data format
zip
csv
txt
pdf
rdata
docx
Creators
Krämer, Stefan
Publisher
Helmholtz-Zentrum für Umweltforschung
Rights holders
Helmholtz-Zentrum für Umweltforschung
Citation note
Kraemer S, Busch W, Schuettler A (2019): A self-organizing map of the fathead minnow liver transcriptome to identify consistent toxicogenomic patterns across chemical fingerprints. Environmental Toxicology and Chemistry (accepted)
Contact
Stefan Kraemer (stefan.kraemer@ufz.de)
Time or Period of creation
01.11.2017 - 05.12.2019
Language of the content
English
Target group
publicly available
Update policy
stored data will not be extended
Access permissions
free access