GSEAplot: A Package for Customizing Gene Set Enrichment Analysis in R

Innis, Sarah; Reinaltt, Kelsie; Civelek, Mete; Anderson, W. T.

doi:10.1089/cmb.2020.0426

Cited by 17 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GO analyses were done using Cytoscape software (version 3.7.2) plug-in BiNGO; the cutoff threshold for p value was set as 0.05 ( Shannon et al., 2003 ). GSEA were done using R package fgsea (version 1.16.0); default parameters were applied; 10,000 for nperm, 15 for minSize and 1000 for maxSize ( Innis et al., 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Single-cell transcriptomes underscore genetically distinct tumor characteristics and microenvironment for hereditary kidney cancers

et al. 2022

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Single-cell transcriptomes underscore genetically distinct tumor characteristics and microenvironment for hereditary kidney cancers

et al. 2022

View full text Add to dashboard Cite

“…Gene Ontology (GO) 17 and Kyoto Encyclopedia of Genes and Genomes (KEGG) 18 pathway enrichment analyses were conducted according to these DEGs to examine the distribution of the biological functions between the two clusters. We also conducted gene set enrichment analysis (GSEA, version 4.0.1) between Clusters 1 and 2 for the purpose of examining the different enrichment of pathways 19 . A FDR p < 0.05 was set to illustrate statistical significance.…”

Section: Methodsmentioning

confidence: 99%

“…We also conducted gene set enrichment analysis (GSEA, version 4.0.1) between Clusters 1 and 2 for the purpose of examining the different enrichment of pathways. 19 A FDR p < 0.05 was set to illustrate statistical significance.…”

Section: Functional Enrichment Analysismentioning

confidence: 99%

Development and validation of apoptosis‐related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients

Hong

et al. 2022

Clinical Laboratory Analysis

View full text Add to dashboard Cite

Background: Previous evidence has shown that apoptosis performs integral functions in the tumorigenesis and development of various tumors. Therefore, this study aimed to establish a molecular subtype and prognostic signature based on apoptosis-related genes (ARGs) to understand the molecular mechanisms and predict prognosis in patients with osteosarcoma. Methods:The GEO and TARGET databases were utilized to obtain the expression levels of ARGs and clinical information of osteosarcoma patients. Consensus clustering analysis was used to explore the different molecular subtypes based on ARGs. GO, KEGG, GSEA, ESTIMATE, and ssGSEA analyses were performed to examine the differences in biological functions and immune characteristics between the distinct molecular subtypes. Then, we constructed an ARG signature by LASSO analysis. The prognostic significance of the ARG signature in osteosarcoma was determined by Kaplan-Meier plotter, Cox regression, and nomogram analyses.Results: Two apoptosis-related subtypes were identified. Cluster 1 had a better prognosis, higher immunogenicity, and immune cell infiltration, as well as a better response to immunotherapy than Cluster 2. We discovered that patients in the high-risk cohort had a lower survival rate than those in the low-risk cohort according to the ARG signature. Furthermore, Cox regression analysis confirmed that a high risk score independently acted as an unfavorable prognostic marker. Additionally, the nomogram combining risk scores with clinical characteristics can improve prediction efficiency. Conclusion:We demonstrated that patients suffering from osteosarcoma may be classified into two apoptosis-related subtypes. Moreover, we developed an ARG prognostic signature to predict the prognosis status of osteosarcoma patients.

show abstract

“…We used clusterProfiler in R for both ORA and GSEA 33 . For the ORA we used the enrichGO function using all the available features in the dataset as the gene universe and the filtered differentially expressed genes as the query genes.…”

Section: Single-cell Data Gene Ontology Over-representation Analysis ...mentioning

confidence: 99%

Single-nuclei transcriptomics of mammalian prion diseases identifies dynamic gene signatures shared between species

Dimitriadis

Zhang

Murphy

et al. 2022

Preprint

View full text Add to dashboard Cite

Mammalian prion diseases are fatal and transmissible neurological conditions caused by the propagation of prions, self-replicating multimeric assemblies of misfolded forms of host cellular prion protein (PrP). The most common human form of the disease, sporadic Creutzfeldt-Jakob disease (sCJD), typically presents as a rapidly progressive dementia and has no effective treatments. Prion diseases are transmissible to laboratory rodents affording unprecedented opportunities to understand neurodegeneration in its evolving stages. Murine models are especially useful in prion research as they develop bona fide prion disease and recapitulate all biochemical and neuropathological hallmarks of human prion disease. Despite extensive studies investigating the changes in transcriptional profiles in prion diseases the mechanisms by which prion diseases induce cellular toxicity, including changes in gene expression profiles are yet to be fully characterized. This is at least in part because confounding effects related to brain cellular heterogeneity have not been resolved. Here, we took advantage of the recent developments in single-cell technologies and performed an unbiased whole-transcriptome single-nucleus transcriptomic analysis in prion disease.

show abstract

GSEAplot: A Package for Customizing Gene Set Enrichment Analysis in R

Cited by 17 publications

References 8 publications

Single-cell transcriptomes underscore genetically distinct tumor characteristics and microenvironment for hereditary kidney cancers

Single-cell transcriptomes underscore genetically distinct tumor characteristics and microenvironment for hereditary kidney cancers

Development and validation of apoptosis‐related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients

Single-nuclei transcriptomics of mammalian prion diseases identifies dynamic gene signatures shared between species

Contact Info

Product

Resources

About