Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection
| Autor: | Marko J. Rinta-aho, Mikko J. Sillanpää, Juho A. J. Kontio |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Current (mathematics)
Gene regulatory network Biology Investigations computer.software_genre 01 natural sciences 010104 statistics & probability 03 medical and health sciences Genetics Animals Humans Gene Regulatory Networks 0101 mathematics Selection (genetic algorithm) 030304 developmental biology 0303 health sciences Models Genetic Quantitative Biology::Molecular Networks Gene Expression Profiling Small sample Genomics Gene coexpression Quantitative Biology::Genomics Nonlinear system Pairwise comparison Data mining Estimation methods Transcriptome computer Algorithms |
| Zdroj: | Genetics |
| Popis: | Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods. |
| Databáze: | OpenAIRE |
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