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.)

S, Das; Meher, Prabina Kumar; Rai, Anil; Bhar, Lalmohan; Mandal, Biswapati

doi:10.1371/journal.pone.0169605

Cited by 52 publications

(56 citation statements)

References 51 publications

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“…Besides, a generalized Mann–Whitney U (MWU) test was also applied for gene set-level inferences. Finally, hub genes were determined by the topological feature of coexpression networks [ 16 ]. Using the proposed analysis method, susceptible pathways and crucial genes will be revealed.…”

Section: Introductionmentioning

confidence: 99%

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

Meng

Cai

Wang

2018

Computational and Mathematical Methods in Medicine

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Background Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated. Methods After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann–Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes. Results A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study. Conclusion This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.

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Section: Introductionmentioning

confidence: 99%

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

Meng

Cai

Wang

2018

Computational and Mathematical Methods in Medicine

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“…Further, exploiting these data and drawing valid biological insights has posed a great challenge to researchers across the globe. For instance, in a gene expression (GE) study, the expression levels of several thousand(s) of genes are measured in a single experiment and further used for identifying the groups of genes which are relevant to the condition/trait under study [ 2 , 3 , 4 ]. Earlier, biologists considered this differential expression (DE) study as the end of their analysis [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Further, the DE analysis produces a list of associated genes ranked by the ascending or descending order of the magnitude of computed test statistic(s)/ p-values (e.g., Z-score, fold change, t-test, etc.) [ 3 , 4 , 5 ]. This is a crucial step undertaken by the experimental biologists and genome researchers to select the informative genes as well as to obtain a global view of expression changes.…”

Section: Introductionmentioning

confidence: 99%

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

McClain

Shesh

2020

Entropy

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Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

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“…The recent advancement in genome sequencing technologies leads to generation of tremendous volume of high-throughput biological data 1 . Meanwhile, exploiting these data and drawing valid biological knowledge has posed a great challenge to scientists across the globe 2 . For instance, in genome wide expression study, the traditional objectives are (a) obtaining the expression levels of several thousand(s) of genes for the samples belonging to at least two different contrasting phenotypic/ environmental conditions, (b) identifying the genes which are relevant to these conditions under study among large pool of genes 3 .…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in genome wide expression study, the traditional objectives are (a) obtaining the expression levels of several thousand(s) of genes for the samples belonging to at least two different contrasting phenotypic/ environmental conditions, (b) identifying the genes which are relevant to these conditions under study among large pool of genes 3 . Moreover, for the later objective, several statistical and machine learning approaches have been developed 2 , 4 . Further, the selected genes are expected to have major causal role for the phenotypic trait under study 5 .…”

Section: Introductionmentioning

confidence: 99%

Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci

Rai

Mishra

et al. 2018

Sci Rep

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The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ (https://cran.r-project.org/web/packages/GSAQ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.

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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.)

Cited by 52 publications

References 51 publications

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci

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