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首页> 外文期刊>Environmental toxicology and chemistry >A Self-Organizing Map of the Fathead Minnow Liver Transcriptome to Identify Consistent Toxicogenomic Patterns across Chemical Fingerprints
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A Self-Organizing Map of the Fathead Minnow Liver Transcriptome to Identify Consistent Toxicogenomic Patterns across Chemical Fingerprints

机译：胖头Min鱼肝脏转录组的自组织图谱可识别化学指纹中一致的毒理基因组模式

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摘要

Lack of consistent findings in different experimental settings remains 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 2-dimensional grid according to their similarity in expression across all considered data. In the present study, 19 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 2500 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. Consistency could be improved compared with 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 by applying additional clustering steps and comparing all obtained fingerprints. Our study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments. Environ Toxicol Chem 2020;39:526-537. (c) 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
机译：在不同的实验环境中缺乏一致的发现仍然是毒理基因组学的主要挑战。本研究调查了当基于共同参照系（“毒理基因组学宇宙”）进行分析时，不同微阵列实验结果之间的一致性是否可以提高，该参照系可以使用自组织图（SOM）的机器学习算法生成。该算法根据基因在所有考虑的数据中的表达相似性，将基因排列并聚集在二维网格上。在本研究中，从Gene Expression Omnibus中检索了19个数据集，其中包括54个不同的成人胖头head鱼肝脏暴露实验，并用于训练SOM。产生的毒理基因组学宇宙将58 872个探针聚集到2500个节点，并用于投影，可视化和比较这54个不同实验的指纹。例如，在一项关于炔雌醇暴露的实验室间研究的数据集中，我们可以识别出一种常见的模式，其中有14％的受显着调节的节点共有。与之前报道的受调控基因的5％总重叠相比，一致性可以得到改善。此外，我们可以通过应用额外的聚类步骤并比较所有获得的指纹图谱，来确定毒性基因组学领域中差异表达的节点和簇的特定且一致的雌激素相关模式。我们的研究表明，基于SOM的方法可用于生成可比的毒物基因组指纹，并提高不同实验结果之间的一致性。 Environ Toxicol Chem 2020； 39：526-537。 （c）2019作者。 Wiley Periodicals，Inc.代表SETAC发布的《环境毒理学和化学》。

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