ArticlePloS one2017
Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- Mapping of flumioxazin tolerance in a snap bean diversity panel leads to the discovery of a master genomic region controlling multiple stress resistance genes.Frontiers in plant science · 2024Article
- Exploring the underlying mechanisms of fisetin in the treatment of hepatic insulin resistance via network pharmacology and in vitro validation.Nutrition & metabolism · 2023Article
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- Expression Plasticity of Transposable Elements Is Highly Associated with Organismal Re-adaptation to Ancestral Environments.Genome biology and evolution · 2022Article
- Identification of a key glioblastoma candidate gene, FUBP3, based on weighted gene co-expression network analysis.BMC neurology · 2022Article
- Gene Co-expression Network Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.PloS one · 2022Article
- In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlyingFrontiers in microbiology · 2022Article
- Transcriptomic comparison of seeds and silique walls from two rapeseed genotypes with contrasting seed oil content.Frontiers in plant science · 2022Article
- Exome Sequencing Reveals Genetic Variability and Identifies Chronic Prognostic Loci in Chinese Sarcoidosis Patients.Frontiers in oncology · 2022Article
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- Genome-wide association study uncovers major genetic loci associated with seed flooding tolerance in soybean.BMC plant biology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.
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