ArticleCommunications biology2022
RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 18 citations in OpenAlex.
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- Distinct patterns of proteostasis network gene expression are associated with different prognoses in melanoma patients.Scientific reports · 2024Article
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- Transcriptomic analysis identifies lactoferrin-induced quiescent circuits in neonatal macrophages.Frontiers in immunology · 2023Article
- Multi-omics integration reveals a core network involved in host defence and hyperkeratinization in psoriasis.Clinical and translational medicine · 2022Article
- Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach.Frontiers in immunology · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Changes 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.
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