clusterProfiler: statistical analysis and visualization of functional profiles for genes and gene clusters

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The clusterProfiler package implements methods to analyze and visualize functional profiles of genomic coordinates (supported by ChIPseeker), gene and gene clusters.

clusterProfiler is released within the Bioconductor project and the source code is hosted on GitHub.

Author

Guangchuang Yu, School of Public Health, The University of Hong Kong.

Citation

Please cite the following article when using clusterProfiler:

doi Altmetric citation

Yu G, Wang L, Han Y and He Q*. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

Installation

Install clusterProfiler is easy, follow the guide on the Bioconductor page:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
## biocLite("BiocUpgrade") ## you may need this
biocLite("clusterProfiler")

Overview

Supported Analyses

  • Over-Representation Analysis
  • Gene Set Enrichment Analysis
  • Biological theme comparison

Supported ontologies/pathways

Visualization

  • barplot
  • cnetplot
  • dotplot
  • enrichMap
  • gseaplot
  • plotGOgraph (via topGO package)
  • upsetplot

Useful utilities:

  • bitr (Biological Id TranslatoR)
  • compareCluster (biological theme comparison)
  • dropGO (screen out GO term of specific level or specific term)
  • go2ont (convert GO ID to Ontology)
  • go2term (convert GO ID to descriptive term)
  • gofilter (restrict result at specific GO level)
  • gsfilter (restrict result by gene set size)
  • simplify (remove redundant GO terms, supported via GOSemSim)

Find out details and examples on Documentation.

Projects that depend on clusterProfiler

Bioconductor packages

  • bioCancer: Interactive Multi-Omics Cancers Data Visualization and Analysis
  • CEMiTool: Co-expression Modules identification Tool
  • DAPAR: Tools for the Differential Analysis of Proteins Abundance with R
  • debrowser: Interactive Differential Expresion Analysis Browser
  • eegc: Engineering Evaluation by Gene Categorization (eegc)
  • esATAC: An Easy-to-use Systematic pipeline for ATACseq data analysis
  • LINC: co-expression of lincRNAs and protein-coding genes
  • miRsponge: Identification and analysis of miRNA sponge interaction networks and modules
  • MoonlightR: Identify oncogenes and tumor suppressor genes from omics data
  • TCGAbiolinksGUI: “TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data”

Other applications

  • APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data

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