Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.
Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.
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DOI：
10.3389/fpls.2022.831204
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发表时间：
2022
影响因子：
5.6
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
Su L;Xu C;Zeng S;Su L;Joshi T;Stacey G;Xu D
植物组织的基因表达模式是区分植物组织的重要标志，利用基因表达模式可以识别组织特异性高表达基因及其差异功能模块。为此，收集大规模大豆转录组样品，并在统一的分析管道中从原始测序读数开始进行处理。为了解决不同组织中基因表达的异质性，我们利用对抗性去噪自动编码器（AD-AE）模型将基因表达映射到潜在空间中，并采用标准的无监督自动编码器（AE）模型来帮助有效地从噪声数据中提取有意义的生物信号。结果发现，4组1，743，914，2，107和1，451个基因分别在叶、根、种子和根瘤组织中特异性高表达。为了获得每个组织中的关键转录因子（TF），枢纽基因及其功能模块，我们构建了组织特异性基因调控网络（GRNs），并通过使用校正和压缩的基因表达数据的差分相关网络。我们从文献和基因富集分析中验证了我们的结果，这些分析证实了许多已鉴定的组织特异性基因。我们的研究代表了迄今为止大豆组织中最大的基因表达分析。它为组织特异性研究提供了有价值的目标，并有助于发现更广泛的生物模式。代码在https://github.com/LingtaoSu/SoyMeta上以开放源代码公开提供。
Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.
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影响因子：
5.3
作者：
Brown AV;Hudson KA
通讯作者：
Hudson KA
影响因子：
16.6
作者：
Araújo IS;Pietsch JM;Keizer EM;Greese B;Balkunde R;Fleck C;Hülskamp M
通讯作者：
Hülskamp M
影响因子：
14.9
作者：
Grant D;Nelson RT;Cannon SB;Shoemaker RC
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影响因子：
3.7
作者：
Fan, Cheng-Ming;Wang, Xu;Fu, Yong-Fu
通讯作者：
Fu, Yong-Fu
影响因子：
5.8
作者：
Benito, M;Parker, J;Marron, JS
通讯作者：
Marron, JS