University of Lille, 42, rue Paul Duez, Lille, 59000, France.
UMR 8030 ; Génomique Métabolique / Laboratoire iSSB ; CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Évry, 91030 Cedex, France.
BMC Bioinformatics. 2019 Feb 4;19(Suppl 13):466. doi: 10.1186/s12859-018-2481-y.
With the recent advancements in high-throughput experimental procedures, biologists are gathering huge quantities of data. A main priority in bioinformatics and computational biology is to provide system level analytical tools capable of meeting an ever-growing production of high-throughput biological data while taking into account its biological context. In gene expression data analysis, genes have widely been considered as independent components. However, a systemic view shows that they act synergistically in living cells, forming functional complexes and more generally a biological system.
In this paper, we propose LATNET, a signal transformation framework that, starting from an initial large-scale gene expression data, allows to generate new representations based on latent network-based relationships between the genes. LATNET aims to leverage system level relations between the genes as an underlying hidden structure to derive the new transformed latent signals. We present a concrete implementation of our framework, based on a gene regulatory network structure and two signal transformation approaches, to quantify latent network-based activity of regulators, as well as gene perturbation signals. The new gene/regulator signals are at the level of each sample of the input data and, thus, could directly be used instead of the initial expression signals for major bioinformatics analysis, including diagnosis and personalized medicine.
Multiple patterns could be hidden or weakly observed in expression data. LATNET helps in uncovering latent signals that could emphasize hidden patterns based on the relations between the genes and, thus, enhancing the performance of gene expression-based analysis algorithms. We use LATNET for the analysis of real-world gene expression data of bladder cancer and we show the efficiency of our transformation framework as compared to using the initial expression data.
随着高通量实验程序的最新进展，生物学家正在收集大量的数据。生物信息学和计算生物学的主要重点是提供系统级分析工具，能够满足高通量生物数据的不断增长的生产，同时考虑到其生物背景。在基因表达数据分析中，基因通常被视为独立的成分。然而，系统的观点表明，它们在活细胞中协同作用，形成功能复合物，更普遍地形成一个生物系统。
在本文中，我们提出了 LATNET，这是一种信号转换框架，它从初始的大规模基因表达数据开始，允许基于基因之间基于潜在网络的关系生成新的表示。LATNET 旨在利用基因之间的系统级关系作为潜在的隐藏结构，得出新的转换后的潜在信号。我们提出了我们的框架的具体实现，基于基因调控网络结构和两种信号转换方法，以量化基于潜在网络的调节剂的活性，以及基因扰动信号。新的基因/调节剂信号处于输入数据的每个样本的水平，因此，可以直接替代初始表达信号用于主要的生物信息学分析，包括诊断和个性化医疗。
在表达数据中可能隐藏或观察到多种模式。LATNET 有助于揭示潜在信号，这些信号可以根据基因之间的关系强调隐藏模式，从而增强基于基因表达的分析算法的性能。我们使用 LATNET 分析膀胱癌的真实世界基因表达数据，并与使用初始表达数据相比，展示了我们转换框架的效率。
