Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

• Published in: Frontiers in genetics Vol. 12; p. 652623
• Main Authors: Jeong, Dabin, Lim, Sangsoo, Lee, Sangseon, Oh, Minsik, Cho, Changyun, Seong, Hyeju, Jung, Woosuk, Kim, Sun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 20.05.2021
• Subjects: condition specific network; gene regulatory network; Genetics; kernel canonical correlation analysis; network dynamics; TF cooperation; transcription factor; kernel canonical correlation analysis; transcription factor; condition specific network; network dynamics; gene regulatory network; TF cooperation
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2021.652623

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.