Biomedical Data Science and Informatics Program, Clemson, SC 29631, USA.
Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29631, USA.
G3 (Bethesda). 2022 Apr 4;12(4). doi: 10.1093/g3journal/jkac042.
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 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.
协调细胞基因表达的机制非常复杂且错综复杂地相互关联。因此，有必要超越完全还原论的方法来理解遗传信息流，并开始关注组织细胞功能的基因之间的网络化连接。计算硬件的持续进步，加上基因相关网络算法的发展，为研究基因之间的网络相互作用提供了能力，而不是孤立的功能。例如，基因共表达网络使用线性度量标准（如 Spearman 或 Pearson 相关性）来构建基因关系网络。最近，有一些工具旨在通过区分基因表达值中的内在噪声与外在噪声、根据组织表型识别不同的模块以及捕捉潜在的非线性关系来深化这些分析。在本报告中，我们介绍了一种算法，该算法将基于图像的分割模式的新颖应用与使用斑点检测技术的双基因边缘检测相结合，用于检测基因表达矩阵中的双基因边缘。我们将这个名为 EdgeCrafting 的算法应用于由健康肾脏和癌症肾脏数据组成的批量 RNA-seq 基因表达矩阵。然后，我们将 EdgeCrafting 与其他 4 种 RNA 表达分析技术进行了比较：加权基因相关网络分析、知识独立网络构建、NetExtractor 和差异基因表达分析。
