GIMLET: Identifying biological modulators in context-specific gene regulation using local energy statistics
GIMLET: Identifying biological modulators in context-specific gene regulation using local energy statistics
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GIMLET：使用当地能源统计数据识别特定背景基因调控中的生物调节剂
DOI：
10.1007/978-3-030-14160-8_13
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Miyano S
中科院分区：
文献类型：
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作者：
Shimamura T;Matsui Y;Kajino T;Ito S;Takahashi T;Miyano S
转录因子活性的调节在细胞状况和疾病亚型之间动态变化。识别有助于环境特异性基因调控的生物调节剂是系统生物学中具有挑战性的任务之一，这对于理解和控制不同遗传背景和环境条件下的细胞反应是必要的。先前用于从基因表达数据鉴定生物调节剂的方法限于捕获调节剂、其靶基因和调节剂之间的特定类型的三向依赖性;这些方法不能描述复杂的调节结构，例如当多个调节剂、其靶基因和调节剂在功能上相关时。在这里，我们提出了一种统计方法，用于识别生物调制器通过捕获多变量的局部依赖性，基于能量统计，这是一类基于距离的统计。随后，我们的方法分配一个衡量的统计意义，每个候选调制器通过排列测试。我们比较了我们的方法与一个领先的竞争对手识别调制器，并说明其性能通过模拟和真实的数据分析。我们的方法名为使用局部能量统计测试（GIMLET）的全基因组识别调制器，用R（3.2.2）实现，可从github（ https://github.com/tshimam/GIMLET ).
The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R (3.2.2) and is available from github ( https://github.com/tshimam/GIMLET ).