Table_1_Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.xlsx

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

基因表达谱（gene expression profile）或转录组（transcriptome）可表征细胞状态，因此解析基因调控机制有助于理解细胞对外界胁迫的响应机制。转录因子（transcription factor, TF）与靶基因（target gene, TG）之间的相互作用是细胞内极具代表性的调控机制之一。本文提出一种全新的计算方法，可从转录组数据中构建条件特异性的转录调控网络。TF与TG间的调控相互作用极为复杂，具体呈现为多对多的关联关系。转录因子染色质免疫共沉淀测序（TF Chromatin Immunoprecipitation sequencing, ChIP-seq）产生的实验数据虽具参考价值，但仅能揭示TF与TG间的一对多关联。另一方面，基因共表达网络虽可用于构建条件特异性转录网络，但这类网络中存在大量假阳性关联。本文提出一种全新方法，通过应用核典型相关分析（kernel canonical correlation analysis, kernel CCA），以识别特定生物学条件下的多对多TF-TG调控关系，进而构建条件特异性且具备组合特征的转录调控网络。核典型相关分析（kernel CCA）是一种成熟的统计学方法，用于计算两组特征集合之间的相关性。因此，我们借助核CCA将TF与TG映射至一个新的特征空间，该空间可反映TF与TG之间的关联特性。为验证所提网络构建方法的有效性，我们使用了两类转录组数据：一类是针对人体高脂饮食响应的血液转录组数据，另一类是用于研究拟南芥冷/热胁迫响应的转录组数据集。本方法不仅检测到了既往研究中报道的重要调控相互作用，还发现了全新的TF-TG关联关系——即特定胁迫条件下，一组TF模块调控一组TG模块。