ArticleF1000Research2015
GOsummaries: an R Package for Visual Functional Annotation of Experimental Data.
Article in F1000Research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
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- Age- and ApoE Genotype-Dependent Transcriptomic Responses to OInternational journal of molecular sciences · 2025Article
- Olfactory and trigeminal routes of HSV-1 CNS infection with regional microglial heterogeneity.Journal of virology · 2024Article
- biotextgraph: graphical summarization of functional similarities from textual information.Bioinformatics (Oxford, England) · 2024Article
- vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis.BMC bioinformatics · 2024Article
- Identification and validation of iron metabolism genes in osteoporosis.BMC medical genomics · 2024Article
- Metagenomics analysis of bacterial community structure from wood- and soil-feeding termites: metabolic pathways and functional structures toward the degradation of lignocellulose and recalcitrant compounds.Frontiers in microbiology · 2024Article
- Extracellular RNA Sensing Mediates Inflammation and Organ Injury in a Murine Model of Polytrauma.Journal of immunology (Baltimore, Md. : 1950) · 2023Article
- Gene Expression Profile as a Predictor of Seizure Liability.International journal of molecular sciences · 2023Article
- Complex I inhibitor of oxidative phosphorylation in advanced solid tumors and acute myeloid leukemia: phase I trials.Nature medicine · 2023Article
- Identification and validation of a novel cuproptosis-related stemness signature to predict prognosis and immune landscape in lung adenocarcinoma by integrating single-cell and bulk RNA-sequencing.Frontiers in immunology · 2023Article
- Network-based identification and prioritization of key transcriptional factors of diabetic kidney disease.Computational and structural biotechnology journal · 2023Article
- Genome-Wide Identification and In Silico Analysis of ZF-HD Transcription Factor Genes inGenes · 2022Article
- Transient cell-in-cell formation underlies tumor relapse and resistance to immunotherapy.eLife · 2022Article
- Astrocyte immunometabolic regulation of the tumour microenvironment drives glioblastoma pathogenicity.Brain : a journal of neurology · 2022Article
- orsum: a Python package for filtering and comparing enrichment analyses using a simple principle.BMC bioinformatics · 2022Article
- Ascorbate peroxidase-mediated in situ labelling of proteins in secreted exosomes.Journal of extracellular vesicles · 2022Article
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2 authors.
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Abstract
Functional characterisation of gene lists using Gene Ontology (GO) enrichment analysis is a common approach in computational biology, since many analysis methods end up with a list of genes as a result. Often there can be hundreds of functional terms that are significantly associated with a single list of genes and proper interpretation of such results can be a challenging endeavour. There are methods to visualise and aid the interpretation of these results, but most of them are limited to the results associated with one list of genes. However, in practice the number of gene lists can be considerably higher and common tools are not effective in such situations. We introduce a novel R package, 'GOsummaries' that visualises the GO enrichment results as concise word clouds that can be combined together if the number of gene lists is larger. By also adding the graphs of corresponding raw experimental data, GOsummaries can create informative summary plots for various analyses such as differential expression or clustering. The case studies show that the GOsummaries plots allow rapid functional characterisation of complex sets of gene lists. The GOsummaries approach is particularly effective for Principal Component Analysis (PCA). By adding functional annotation to the principal components, GOsummaries improves significantly the interpretability of PCA results. The GOsummaries layout for PCA can be effective even in situations where we cannot directly apply the GO analysis. For example, in case of metabolomics or metagenomics data it is possible to show the features with significant associations to the components instead of GO terms. The GOsummaries package is available under GPL-2 licence at Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/GOsummaries.html).
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