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Abstract：
Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer\'s disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .
摘要：
加权基因共表达网络分析（WGCNA）是一种广泛用于生成基因共表达网络的方法。然而,使用此工具生成的网络通常会创建大型模块，其中包含难以破译的大量功能注释。我们开发了TGCN,一种创建靶向基因共表达网络的新方法。该方法使用LASSO回归的改进基于基因表达鉴定最佳预测感兴趣性状的转录本。然后，它围绕这些转录本构建共表达模块。使用来自基因型-组织表达项目的13个脑区域的表达来表征算法特性。当我们的方法与WGCNA比较时，TGCN网络导致更精确的模块，具有更具体但丰富的生物学意义。然后，我们通过在宗教订单研究和记忆与衰老项目数据集上创建APP-TGCN来说明其适用性，旨在明确与APP在阿尔茨海默病中作用特异性相关的分子通路。在两个独立的队列中进一步验证了主要生物学发现。总之,我们提供了一个新的框架，用于创建更小的目标网络，在高通量假设驱动的研究中具有生物学相关性和实用性。TGCNR软件包可在Github上获得：https://github.com/aliciagp/TGCN。
