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- Title
EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks.
- Authors
Husain, Benafsh; Bender, Matthew Reed; Feltus, Frank Alex
- Abstract
The 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 RNAsequencing 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.
- Subjects
RNA analysis; PEARSON correlation (Statistics); GENE expression; RANK correlation (Statistics); CELL physiology; GENE regulatory networks
- Publication
G3: Genes | Genomes | Genetics, 2022, Vol 12, Issue 4, p1
- ISSN
2160-1836
- Publication type
Article
- DOI
10.1093/g3journal/jkac042