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clusterProfiler: an R package for comparing biological themes among gene clusters
Guangchuang Yu1, Li-Gen Wang, Yanyan Han
1Institute of Life and Health Engineering, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Jinan University, Guangzhou, People's Republic of China.
Omics : a Journal of Integrative Biology
|March 30, 2012
Summary
This study introduces clusterProfiler, an R package for automated biological enrichment analysis of gene clusters from transcriptomics and proteomics data. It simplifies complex data interpretation for researchers studying gene functions across species.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- High-throughput biological data, such as from transcriptomics and proteomics, necessitates advanced analytical methods.
- Integrating and interpreting large-scale gene expression data remains a significant challenge in biological research.
Purpose of the Study:
- To present clusterProfiler, an R package designed for automated biological term classification and gene cluster enrichment analysis.
- To provide a reusable workflow combining analysis and visualization modules for gene set enrichment analysis.
Main Methods:
- Development of an R package, clusterProfiler, leveraging the Bioconductor framework.
- Implementation of automated biological term classification and gene enrichment analysis.
- Integration of analysis and visualization modules into a cohesive workflow.
Main Results:
- clusterProfiler successfully automates the enrichment analysis of gene clusters.
- The package supports analysis for humans, mice, and yeast, with extensible methods for other species and ontologies.
- A reusable workflow is provided for streamlined biological data interpretation.
Conclusions:
- clusterProfiler offers an efficient and automated solution for biological enrichment analysis.
- The package facilitates the interpretation of complex quantitative biological data, aiding in the understanding of gene functions.
- Its flexible design supports broad applicability in various research contexts.
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