scholarly journals RegEnrich: An R package for gene regulator enrichment analysis reveals key role of ETS transcription factor family in interferon signaling

2021 ◽  
Author(s):  
Weiyang Tao ◽  
Timothy R.D.J. Radstake ◽  
Aridaman Pandit
Keyword(s):  
Transcription Factor ◽  
Cell Activation ◽  
Network Inference ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Data Driven ◽  
Transcription Factor Family ◽  
Gene Regulatory Network Inference ◽  
Ets Transcription Factor

AbstractChanges in a few key transcriptional regulators can lead to different biological states, including cell activation and differentiation, and diseases. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights and can further help in translational research. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the regulatory interactions between genes and proteins. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best to retrieve the key regulators. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. We found that not only IRF and STAT transcription factor families played an important role in cells responding to IFN, but also several ETS transcription factor family members, such as ELF1 and ETV7, are highly associated with IFN stimulations. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states in a data-driven manner, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

scholarly journals RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Weiyang Tao ◽  
Timothy R. D. J. Radstake ◽  
Aridaman Pandit
Keyword(s):  
Network Inference ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Analysis Data ◽  
R Package ◽  
Gene Regulatory Network Inference ◽  
Go Enrichment ◽  
Different Cell Types

AbstractChanges in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

412 Expression of the Ets transcription factor family in HER2/neu positive breast cancer

2003 ◽  
Vol 1 (5) ◽  
pp. S126
Author(s):  
E. Myers ◽  
A.D.K. Hill ◽  
Y. Buggy ◽  
E.W. Mc Dermott ◽  
N.J. O'Higgins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factor ◽  
Transcription Factor Family ◽  
Ets Transcription Factor ◽  
Positive Breast Cancer

scholarly journals Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals dynUGENE: an R package for uncertainty-aware gene regulatory network inference, simulation, and visualization

2021 ◽  
Author(s):  
Tianyu Lu ◽  
Anjali Silva
Keyword(s):  
Model Selection ◽  
Gene Regulatory Network ◽  
Model Uncertainty ◽  
Regulatory Network ◽  
Network Architecture ◽  
Network Inference ◽  
Source Code ◽  
R Package ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory

Methods for gene regulatory network inference focus on network architecture identification but neglect model selection and simulation. We implement an extension to the dynGENIE3 algorithm that accounts for model uncertainty as an R package, providing users with an easy to use interface for model selection and gene expression profile simulation. Source code is available at https://github.com/tianyu-lu/dynUGENE with a detailed user guide. A webserver with interactive controls is available at https://tianyulu.shinyapps.io/dynUGENE/.

Sign in / Sign up

Export Citation Format

Share Document