Abstract
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.
Highlights
The soybean is a valuable source of oil and protein for humans and livestock; it is very important for soil fertility, given the symbiotic interaction with nitrogen-fixing rhizobia
Through a large-scale transcriptome meta-analysis, several hub genes involved in soybean oil accumulation processes were revealed in Qi et al (2018), and a large number of differentially expressed genes (DEGs) related to soybean symbiotic nitrogen fixation were identified (Yuan et al, 2017)
After a systematic review of soybean-related microarray studies in the literature, we found a large number of samples sequenced by the Affymetrix GPL4592 (Affymetrix Glycine max Genome Array) platform
Summary
The soybean is a valuable source of oil and protein for humans and livestock; it is very important for soil fertility, given the symbiotic interaction with nitrogen-fixing rhizobia. 2021) and the most recently published Soybean Expression Atlas (Machado et al, 2020), covering thousands of gene expression data sets from various tissues, developmental stages and conditions. Some similar studies are available (Liu et al, 2015; Huang et al, 2018; Wang J. et al, 2019; Yi et al, 2019) Most of these investigations explored only a few conditions or developmental stages. Such ad hoc approaches can overlook a myriad of interesting transcriptional patterns, which could otherwise be unraveled by integrative methods using a more comprehensive set of samples. A global co-expression network analysis of 1,072 soybean microarray samples was conducted (Wu et al, 2019), which revealed a gene module that is likely involved in the evolution of nodulation in plants. To elucidate the dynamics of transcriptional regulation across the broad range of samples, tissues, and cultivars, 1,298 publicly available soybean transcriptome samples were collected and analyzed by Machado et al (2020)
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