Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.
Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.
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DOI：
10.1038/srep43946
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发表时间：
2017-03-13
影响因子：
4.6
通讯作者：
Cannistraci CV
中科院分区：
文献类型：
--
作者：
Ciucci S;Ge Y;Durán C;Palladini A;Jiménez-Jiménez V;Martínez-Sánchez LM;Wang Y;Sales S;Shevchenko A;Poser SW;Herbig M;Otto O;Androutsellis-Theotokis A;Guck J;Gerl MJ;Cannistraci CV
基因组科学正在迅速发展，主成分分析(PCA)是探索基因组数据集中差异模式的最常用技术之一。然而，缺少一种方法来启发组学特征网络，这些特征网络对主成分分析获得的样本分离有主要贡献。另一种选择是在单变量选择的显著基因组特征之间建立相关网络，但这忽略了导致主成分分析样本分离的多变量无监督特征压缩。生物学家和医学研究人员往往更喜欢有效的方法，为复杂的算法提供即时解释，这些算法原则上承诺会有所改进，但实际上很难应用和解释。在这里，我们提出了PC-CORR：一种简单的算法，它与任何PCA分离相关联，是一种区分特征的网络。在从系统和精密生物医学的多面体数据中寻找在定义组合和多尺度生物标记物方面有用的功能模块时，可以检查这种网络。我们在脂体学、后基因组学、发育基因组学、群体遗传学、癌症促进剂学和癌症干细胞机械学数据上提供了PC-Corr有效性的证据。最后，PC-CORR是一种通用的函数网络推理方法，可以很容易地用于计算机科学中的大数据探索和物理中的复杂系统分析。
Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.