Introduction

This package is designed for reactome pathway-based analysis. Reactome is an open-source, open access, manually curated and peer-reviewed pathway database.

Citation

If you use ReactomePA(Yu and He 2016) in published research, please cite:

G Yu, QY He*. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479. doi: 10.1039/C5MB00663E

Supported organisms

Currently ReactomePA supports several model organisms, including ‘celegans’, ‘fly’, ‘human’, ‘mouse’, ‘rat’, ‘yeast’ and ‘zebrafish’. The input gene ID should be Entrez gene ID. We recommend using clusterProfiler::bitr to convert biological IDs. For more detail, please refer to bitr: Biological Id TranslatoR.

Pathway Enrichment Analysis

Enrichment analysis is a widely used approach to identify biological themes. Here, we implement hypergeometric model to assess whether the number of selected genes associated with reactome pathway is larger than expected. The p values were calculated based the hypergeometric model(Boyle et al. 2004).

## [1] "4312"  "8318"  "10874" "55143" "55388" "991"
##                          ID
## R-HSA-69620     R-HSA-69620
## R-HSA-2500257 R-HSA-2500257
## R-HSA-141424   R-HSA-141424
## R-HSA-141444   R-HSA-141444
## R-HSA-69618     R-HSA-69618
## R-HSA-68877     R-HSA-68877
##                                                                                        Description
## R-HSA-69620                                                                 Cell Cycle Checkpoints
## R-HSA-2500257                                              Resolution of Sister Chromatid Cohesion
## R-HSA-141424                                         Amplification of signal from the kinetochores
## R-HSA-141444  Amplification  of signal from unattached  kinetochores via a MAD2  inhibitory signal
## R-HSA-69618                                                             Mitotic Spindle Checkpoint
## R-HSA-68877                                                                   Mitotic Prometaphase
##               GeneRatio   BgRatio       pvalue     p.adjust       qvalue
## R-HSA-69620      37/322 293/10619 1.099781e-13 7.907422e-11 6.992289e-11
## R-HSA-2500257    23/322 126/10619 3.018476e-12 1.085142e-09 9.595577e-10
## R-HSA-141424     20/322  96/10619 6.421326e-12 1.154233e-09 1.020653e-09
## R-HSA-141444     20/322  96/10619 6.421326e-12 1.154233e-09 1.020653e-09
## R-HSA-69618      21/322 112/10619 1.602868e-11 2.304924e-09 2.038173e-09
## R-HSA-68877      26/322 200/10619 3.180145e-10 3.810874e-08 3.369838e-08
##                                                                                                                                                                                                                                 geneID
## R-HSA-69620   CDC45/CDCA8/MCM10/CDC20/CENPE/CCNB2/NDC80/UBE2C/SKA1/CENPM/CENPN/CCNA2/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/CHEK1/CCNB1/MCM5/MCM2/KIF2C/CDC25A/CDC6/PLK1/BUB1B/GTSE1/EXO1/ZWINT/CENPU/SPC25/CENPI/CCNE1/ORC6/ORC1/TAOK1
## R-HSA-2500257                                                                                CDCA8/CDC20/CENPE/CCNB2/NDC80/SKA1/CENPM/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/CCNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## R-HSA-141424                                                                                                  CDCA8/CDC20/CENPE/NDC80/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## R-HSA-141444                                                                                                  CDCA8/CDC20/CENPE/NDC80/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## R-HSA-69618                                                                                             CDCA8/CDC20/CENPE/NDC80/UBE2C/SKA1/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/BIRC5/AURKB/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
## R-HSA-68877                                                                 CDCA8/CDC20/CENPE/CCNB2/NDC80/NCAPH/SKA1/NEK2/CENPM/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/BIRC5/NCAPG/AURKB/CCNB1/KIF2C/PLK1/BUB1B/ZWINT/CENPU/SPC25/CENPI/TAOK1
##               Count
## R-HSA-69620      37
## R-HSA-2500257    23
## R-HSA-141424     20
## R-HSA-141444     20
## R-HSA-69618      21
## R-HSA-68877      26

For calculation/parameter details, please refer to the vignette of DOSE(Yu et al. 2015)..

Pathway analysis of NGS data

Pathway analysis using NGS data (eg, RNA-Seq and ChIP-Seq) can be performed by linking coding and non-coding regions to coding genes via ChIPseeker package, which can annotates genomic regions to their nearest genes, host genes, and flanking genes respectivly. In addtion, it provides a function, seq2gene, that simultaneously considering host genes, promoter region and flanking gene from intergenic region that may under control via cis-regulation. This function maps genomic regions to genes in a many-to-many manner and facilitate functional analysis. For more details, please refer to ChIPseeker(Yu, Wang, and He 2015).

Visualize enrichment result

We implement barplot, dotplot enrichment map and category-gene-network for visualization. It is very common to visualize the enrichment result in bar or pie chart. We believe the pie chart is misleading and only provide bar chart.

Enrichment map can be viusalized by enrichMap:

In order to consider the potentially biological complexities in which a gene may belong to multiple annotation categories, we developed cnetplot function to extract the complex association between genes and diseases.

Comparing enriched reactome pathways among gene clusters with clusterProfiler

We have developed an R package clusterProfiler(Yu et al. 2012) for comparing biological themes among gene clusters. ReactomePA works fine with clusterProfiler and can compare biological themes at reactome pathway perspective.