.png){kind=icon}
.png){kind=icon}
Return
Using clusterProfiler to characterize multiomics data
.png){kind=icon}
PRE
AI
DOI：10.1038/s41596-024-01020-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within similar to 2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/.
Keywords:
SET ENRICHMENT ANALYSIS
R/BIOCONDUCTOR PACKAGE
R PACKAGE
GENE
VISUALIZATION
ANNOTATION
ALIGNMENT
TERMS
Journal
IF:
16
Papers:
4.0K
Citations:
5.6W