EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks
AbstractThe mechanisms that coordinate cellular gene expression are highly complex and intricately interconnected. Thus, it is necessary to move beyond a fully reductionist approach to understanding genetic information flow and begin focusing on the networked connections between gene...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Oxford University Press
2022-02-01
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| Series: | G3: Genes, Genomes, Genetics |
| Online Access: | https://academic.oup.com/g3journal/article-lookup/doi/10.1093/g3journal/jkac042 |
| _version_ | 1818193976065261568 |
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| author | Benafsh Husain Matthew Reed Bender Frank Alex Feltus |
| author_facet | Benafsh Husain Matthew Reed Bender Frank Alex Feltus |
| author_sort | Benafsh Husain |
| collection | DOAJ |
| description |
AbstractThe mechanisms that coordinate cellular gene expression are highly complex and intricately interconnected. Thus, it is necessary to move beyond a fully reductionist approach to understanding genetic information flow and begin focusing on the networked connections between genes that organize cellular function. Continued advancements in computational hardware, coupled with the development of gene correlation network algorithms, provide the capacity to study networked interactions between genes rather than their isolated functions. For example, gene coexpression networks are used to construct gene relationship networks using linear metrics such as Spearman or Pearson correlation. Recently, there have been tools designed to deepen these analyses by differentiating between intrinsic vs extrinsic noise within gene expression values, identifying different modules based on tissue phenotype, and capturing potential nonlinear relationships. In this report, we introduce an algorithm with a novel application of image-based segmentation modalities utilizing blob detection techniques applied for detecting bigenic edges in a gene expression matrix. We applied this algorithm called EdgeCrafting to a bulk RNA-sequencing gene expression matrix comprised of a healthy kidney and cancerous kidney data. We then compared EdgeCrafting against 4 other RNA expression analysis techniques: Weighted Gene Correlation Network Analysis, Knowledge Independent Network Construction, NetExtractor, and Differential gene expression analysis. |
| first_indexed | 2024-12-12T00:54:57Z |
| format | Article |
| id | doaj.art-34fcb8ac090e493a89a930e0c8c39fab |
| institution | Directory Open Access Journal |
| issn | 2160-1836 |
| language | English |
| last_indexed | 2024-12-12T00:54:57Z |
| publishDate | 2022-02-01 |
| publisher | Oxford University Press |
| record_format | Article |
| series | G3: Genes, Genomes, Genetics |
| spelling | doaj.art-34fcb8ac090e493a89a930e0c8c39fab2022-12-22T00:43:53ZengOxford University PressG3: Genes, Genomes, Genetics2160-18362022-02-0112410.1093/g3journal/jkac042EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networksBenafsh Husain0https://orcid.org/0000-0002-5542-5992Matthew Reed Bender1Frank Alex Feltus2https://orcid.org/0000-0002-2123-6114Biomedical Data Science and Informatics Program, Clemson, SC 29631, USABiomedical Data Science and Informatics Program, Clemson, SC 29631, USABiomedical Data Science and Informatics Program, Clemson, SC 29631, USA AbstractThe mechanisms that coordinate cellular gene expression are highly complex and intricately interconnected. Thus, it is necessary to move beyond a fully reductionist approach to understanding genetic information flow and begin focusing on the networked connections between genes that organize cellular function. Continued advancements in computational hardware, coupled with the development of gene correlation network algorithms, provide the capacity to study networked interactions between genes rather than their isolated functions. For example, gene coexpression networks are used to construct gene relationship networks using linear metrics such as Spearman or Pearson correlation. Recently, there have been tools designed to deepen these analyses by differentiating between intrinsic vs extrinsic noise within gene expression values, identifying different modules based on tissue phenotype, and capturing potential nonlinear relationships. In this report, we introduce an algorithm with a novel application of image-based segmentation modalities utilizing blob detection techniques applied for detecting bigenic edges in a gene expression matrix. We applied this algorithm called EdgeCrafting to a bulk RNA-sequencing gene expression matrix comprised of a healthy kidney and cancerous kidney data. We then compared EdgeCrafting against 4 other RNA expression analysis techniques: Weighted Gene Correlation Network Analysis, Knowledge Independent Network Construction, NetExtractor, and Differential gene expression analysis.https://academic.oup.com/g3journal/article-lookup/doi/10.1093/g3journal/jkac042 |
| spellingShingle | Benafsh Husain Matthew Reed Bender Frank Alex Feltus EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks G3: Genes, Genomes, Genetics |
| title | EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks |
| title_full | EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks |
| title_fullStr | EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks |
| title_full_unstemmed | EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks |
| title_short | EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks |
| title_sort | edgecrafting mining embedded latent nonlinear patterns to construct gene relationship networks |
| url | https://academic.oup.com/g3journal/article-lookup/doi/10.1093/g3journal/jkac042 |
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