Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM.
Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM.
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DOI：
10.1186/s12859-022-05047-5
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发表时间：
2023-03-06
影响因子：
3
通讯作者：
中科院分区：
文献类型：
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细胞对内部和外部线索表现出各种反应。这些反应是可能的，部分原因是每个细胞中都存在一个复杂的基因调控网络（GRN）。在过去的20年里，许多研究小组致力于使用各种推理算法从大规模基因表达数据中重建GRNs的拓扑结构。对GRN参与者的了解可能最终会带来治疗益处。互信息（MI）是该推断/重构流水线内广泛使用的度量，因为它可以检测任何数量的变量（n维）之间的任何相关性（线性和非线性）。然而，使用MI与连续数据（例如，基因表达水平的归一化荧光强度测量）是敏感的数据大小，相关强度和潜在的分布，往往需要费力，有时，特别优化。在这项工作中，我们首先表明，估计MI的二元和三元高斯分布，使用k-最近邻（kNN）MI估计结果显着减少错误的常用方法相比，基于固定分箱。其次，我们证明了实现基于MI的kNN Kraskov-Stoögbauer-Grassberger（KSG）算法导致GRN重建的显着改善，用于流行的推理算法，如上下文相关性（Context Likestry of Relatedness，KSG）。最后，通过广泛的计算机基准测试，我们表明，一种新的推理算法CMIA（条件互信息增强），灵感来自于KSG，与KSG-MI估计量相结合，优于常用的方法。使用包含15个合成网络的三个规范数据集，新开发的GRN重建方法结合了CMIA和KSG-MI估计器，与该领域当前的黄金标准相比，在精确度-召回率方面实现了20-35%的改进。这种新方法将使研究人员能够发现新的基因相互作用或更好地选择候选基因进行实验验证。在线版本包含补充材料，可通过10.1186/s12859-022-05047-5获得。
A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov–Stoögbauer–Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction—which combines CMIA, and the KSG-MI estimator—achieves an improvement of 20–35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations. The online version contains supplementary material available at 10.1186/s12859-022-05047-5.
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影响因子：
3
作者：
Meyer, Patrick E.;Lafitte, Frederic;Bontempi, Gianluca
通讯作者：
Bontempi, Gianluca
影响因子：
14.9
作者：
Faith JJ;Driscoll ME;Fusaro VA;Cosgrove EJ;Hayete B;Juhn FS;Schneider SJ;Gardner TS
通讯作者：
Gardner TS
影响因子：
9.5
作者：
Maetschke SR;Madhamshettiwar PB;Davis MJ;Ragan MA
通讯作者：
Ragan MA
DOI：
10.1073/pnas.0913357107
发表时间：
2010-04-06
影响因子：
11.1
作者：
Marbach, Daniel;Prill, Robert J.;Stolovitzky, Gustavo
通讯作者：
Stolovitzky, Gustavo
影响因子：
48
作者：
Marbach, Daniel;Costello, James C.;Kueffner, Robert;Vega, Nicole M.;Prill, Robert J.;Camacho, Diogo M.;Allison, Kyle R.;Kellis, Manolis;Collins, James J.;Stolovitzky, Gustavo
通讯作者：
Stolovitzky, Gustavo