School of Computing, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.
Computational and System Biology, Genome Institute of Singapore, A-STAR, 60 Biopolis Street, Singapore, 138672, Singapore.
BMC Bioinformatics. 2017 Dec 28;18(Suppl 16):576. doi: 10.1186/s12859-017-1963-7.
Differential co-expression (DCX) signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression.
We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression.
MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression.
差异共表达（DCX）表示在不同生物学条件下一组基因的共表达程度的变化。它已被用于识别差异共表达网络或相互作用组。已经开发了许多用于单因素差异共表达分析的算法，并应用于各种研究中。然而，在许多研究中，样本的特征是多种因素，如遗传标记、临床变量和治疗。目前还没有用于多因素差异共表达分析的算法或方法。
我们开发了一种新的公式和一种称为 MultiDCoX 的计算上高效的贪婪搜索算法，用于进行多因素差异共表达分析。模拟数据分析表明，该算法可以有效地提取差异共表达（DCX）基因集，并量化每个因素对共表达的影响。对乳腺癌数据集的 MultiDCoX 分析确定了有趣的具有生物学意义的差异共表达（DCX）基因集，以及影响各自差异共表达的遗传和临床因素。
MultiDCoX 是一种高效的空间和时间程序，可用于识别差异共表达基因集，并成功识别单个因素对差异共表达的影响。
