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 37/322 293/10654 9.962958e-14 7.173330e-11 6.355319e-11
## R-HSA-2500257 23/322 126/10654 2.824834e-12 1.016940e-09 9.009735e-10
## R-HSA-141424 20/322 96/10654 6.054136e-12 1.089744e-09 9.654753e-10
## R-HSA-141444 20/322 96/10654 6.054136e-12 1.089744e-09 9.654753e-10
## R-HSA-69618 21/322 112/10654 1.508315e-11 2.171973e-09 1.924292e-09
## R-HSA-68877 26/322 200/10654 2.966075e-10 3.559290e-08 3.153406e-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.