[1] "Anopheles gambiae" "Arabidopsis thaliana"
[3] "Bos taurus" "Caenorhabditis elegans"
[5] "Canis familiaris" "Danio rerio"
[7] "Drosophila melanogaster" "Equus caballus"
[9] "Gallus gallus" "Homo sapiens"
[11] "Mus musculus" "Pan troglodytes"
[13] "Populus trichocarpa" "Rattus norvegicus"
[15] "Saccharomyces cerevisiae" "Solanum lycopersicum"
[17] "Sus scrofa" "Zea mays" 6 WikiPathways analysis
WikiPathways is a continuously updated pathway database curated by a community of researchers and pathway enthusiasts. WikiPathways produces monthly releases of gmt files for supported organisms at data.wikipathways.org. The clusterProfiler package (Yu et al. 2012) supports enrichment analysis (either ORA or GSEA) for WikiPathways using the enrichWP() and gseWP() functions. These functions will automatically download and parse latest WikiPathways GMT file for selected organism.
Supported organisms can be listed by:
6.1 WikiPathways over-representation analysis
#
# over-representation test
#
#...@organism Homo sapiens
#...@ontology WikiPathways
#...@keytype ENTREZID
#...@gene chr [1:207] "4312" "8318" "10874" "55143" "55388" "991" "6280" "2305" ...
#...pvalues adjusted by 'BH' with cutoff <0.05
#...7 enriched terms found
'data.frame': 7 obs. of 12 variables:
$ ID : chr "WP2446" "WP2361" "WP3942" "WP179" ...
$ Description : chr "Retinoblastoma gene in cancer" "Gastric cancer network 1" "PPAR signaling" "Cell cycle" ...
$ GeneRatio : chr "11/122" "6/122" "7/122" "10/122" ...
$ BgRatio : chr "89/9032" "28/9032" "50/9032" "120/9032" ...
$ RichFactor : num 0.1236 0.2143 0.14 0.0833 0.2857 ...
$ FoldEnrichment: num 9.15 15.86 10.36 6.17 21.15 ...
$ zScore : num 9.04 9.22 7.77 6.67 8.83 ...
$ pvalue : num 2.60e-08 1.59e-06 4.26e-06 4.63e-06 2.86e-05 ...
$ p.adjust : num 8.82e-06 2.69e-04 3.93e-04 3.93e-04 1.94e-03 ...
$ qvalue : num 8.19e-06 2.50e-04 3.65e-04 3.65e-04 1.80e-03 ...
$ geneID : chr "8318/9133/7153/6241/890/983/81620/7272/1111/891/24137" "4605/7153/11065/22974/6286/6790" "4312/9415/9370/5105/2167/3158/5346" "8318/991/9133/890/983/7272/1111/891/4174/9232" ...
$ Count : int 11 6 7 10 4 7 8
#...Citation
S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang, W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize multiomics data. Nature Protocols. 2024, 19(11):3292-3320 6.2 WikiPathways gene set enrichment analysis
gseWP(geneList, organism = "Homo sapiens")#
# Gene Set Enrichment Analysis
#
#...@organism Homo sapiens
#...@setType WikiPathways
#...@keytype ENTREZID
#...@geneList Named num [1:12495] 4.57 4.51 4.42 4.14 3.88 ...
- attr(*, "names")= chr [1:12495] "4312" "8318" "10874" "55143" ...
#...nPerm
#...pvalues adjusted by 'BH' with cutoff <0.05
#...68 enriched terms found
'data.frame': 68 obs. of 11 variables:
$ ID : chr "WP2446" "WP179" "WP466" "WP2361" ...
$ Description : chr "Retinoblastoma gene in cancer" "Cell cycle" "DNA replication" "Gastric cancer network 1" ...
$ setSize : int 84 111 42 23 62 231 78 102 41 61 ...
$ enrichmentScore: num 0.731 0.663 0.792 0.837 0.665 ...
$ NES : num 2.93 2.73 2.73 2.52 2.51 ...
$ pvalue : num 1.00e-10 1.00e-10 1.00e-10 2.84e-09 2.82e-09 ...
$ p.adjust : num 2.56e-08 2.56e-08 2.56e-08 3.64e-07 3.64e-07 ...
$ qvalue : num 2.17e-08 2.17e-08 2.17e-08 3.08e-07 3.08e-07 ...
$ rank : num 1333 1234 1002 854 1111 ...
$ leading_edge : chr "tags=51%, list=11%, signal=46%" "tags=40%, list=10%, signal=36%" "tags=55%, list=8%, signal=51%" "tags=52%, list=7%, signal=49%" ...
$ core_enrichment: chr "8318/9133/7153/6241/890/983/81620/7272/1111/891/24137/993/898/4998/10733/9134/4175/4173/6502/5984/994/7298/3015"| __truncated__ "8318/991/9133/890/983/7272/1111/891/4174/9232/4171/993/990/5347/9700/898/23594/4998/9134/4175/4173/10926/6502/9"| __truncated__ "8318/55388/81620/4174/4171/990/23594/4998/4175/4173/10926/5984/5111/51053/8317/5427/23649/4176/5982/5557/5558/4172/5424" "4605/7153/11065/22974/6286/6790/1894/56992/4173/1063/9585/8607" ...
#...Citation
S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang, W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize multiomics data. Nature Protocols. 2024, 19(11):3292-3320 If your input gene ID type is not Entrez gene ID, you can use the bitr() function to convert gene ID. If you want to convert the gene IDs in output result to gene symbols, you can use the setReadable() function.
References
Yu, Guangchuang, Le-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “clusterProfiler: An r Package for Comparing Biological Themes Among Gene Clusters.” OMICS: A Journal of Integrative Biology 16 (5): 284–87. https://doi.org/10.1089/omi.2011.0118.