Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis
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 cel...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.652623/full |
| _version_ | 1818662124998623232 |
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| author | Dabin Jeong Sangsoo Lim Sangseon Lee Minsik Oh Changyun Cho Hyeju Seong Woosuk Jung Sun Kim Sun Kim Sun Kim |
| author_facet | Dabin Jeong Sangsoo Lim Sangseon Lee Minsik Oh Changyun Cho Hyeju Seong Woosuk Jung Sun Kim Sun Kim Sun Kim |
| author_sort | Dabin Jeong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T04:55:58Z |
| format | Article |
| id | doaj.art-d96075f6a57145638c05b16aeebb3116 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T04:55:58Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-d96075f6a57145638c05b16aeebb31162022-12-21T22:02:43ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-05-011210.3389/fgene.2021.652623652623Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation AnalysisDabin Jeong0Sangsoo Lim1Sangseon Lee2Minsik Oh3Changyun Cho4Hyeju Seong5Woosuk Jung6Sun Kim7Sun Kim8Sun Kim9Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South KoreaBioinformatics Institute, Seoul National University, Seoul, South KoreaBK21 FOUR Intelligence Computing, Seoul National University, Seoul, South KoreaDepartment of Computer Science and Engineering, Seoul National University, Seoul, South KoreaInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South KoreaDepartment of Crop Science, Konkuk University, Seoul, South KoreaDepartment of Crop Science, Konkuk University, Seoul, South KoreaInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South KoreaBioinformatics Institute, Seoul National University, Seoul, South KoreaDepartment of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul, South KoreaGene 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.https://www.frontiersin.org/articles/10.3389/fgene.2021.652623/fullkernel canonical correlation analysisgene regulatory networknetwork dynamicstranscription factorTF cooperationcondition specific network |
| spellingShingle | Dabin Jeong Sangsoo Lim Sangseon Lee Minsik Oh Changyun Cho Hyeju Seong Woosuk Jung Sun Kim Sun Kim Sun Kim Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis Frontiers in Genetics kernel canonical correlation analysis gene regulatory network network dynamics transcription factor TF cooperation condition specific network |
| title | Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis |
| title_full | Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis |
| title_fullStr | Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis |
| title_full_unstemmed | Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis |
| title_short | Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis |
| title_sort | construction of condition specific gene regulatory network using kernel canonical correlation analysis |
| topic | kernel canonical correlation analysis gene regulatory network network dynamics transcription factor TF cooperation condition specific network |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.652623/full |
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