Entering edit mode
I found that FoldEnrichment
,RichFactor
and zScore
are not in the ReactomePA::enrichPathway
result. However, these fields are in the clusterProfiler::enrichKEGG
and clusterProfiler::enrichGO
. Considering that all of these three functions use DOSE::enricher_internal
to perform enrichment analysis and there is an update for adding the above mentioned fields, why ReactomePA::enrichPathway
result dose not have these three columns?
library(ReactomePA)
data(geneList, package="DOSE")
de <- names(geneList)[abs(geneList) > 1.5]
x <- enrichPathway(gene=de, pvalueCutoff = 0.05, readable=TRUE)
colnames(x@result)
> colnames(x@result)
[1] "ID" "Description" "GeneRatio" "BgRatio" "pvalue" "p.adjust"
[7] "qvalue" "geneID" "Count"
library(clusterProfiler)
gene <- names(geneList)[abs(geneList) > 2]
kk <- enrichKEGG(gene = gene,
organism = 'hsa',
pvalueCutoff = 0.05)
colnames(kk@result)
> colnames(kk@result)
[1] "category" "subcategory" "ID" "Description" "GeneRatio"
[6] "BgRatio" "RichFactor" "FoldEnrichment" "zScore" "pvalue"
[11] "p.adjust" "qvalue" "geneID" "Count"
> sessionInfo()
R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8 LC_CTYPE=Chinese (Simplified)_China.utf8
[3] LC_MONETARY=Chinese (Simplified)_China.utf8 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.utf8
time zone: Asia/Shanghai
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] clusterProfiler_4.12.6 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0 IRanges_2.38.1
[5] S4Vectors_0.42.1 Biobase_2.64.0 BiocGenerics_0.50.0 ReactomePA_1.48.0
loaded via a namespace (and not attached):
[1] DBI_1.2.3 gson_0.1.0 shadowtext_0.1.4
[4] gridExtra_2.3 httr2_1.0.5 remotes_2.5.0
[7] rlang_1.1.4 magrittr_2.0.3 DOSE_3.30.5
[10] compiler_4.4.1 RSQLite_2.3.7 reactome.db_1.88.0
[13] png_0.1-8 vctrs_0.6.5 reshape2_1.4.4
[16] stringr_1.5.1 pkgconfig_2.0.3 crayon_1.5.3
[19] fastmap_1.2.0 XVector_0.44.0 ggraph_2.2.1
[22] utf8_1.2.4 enrichplot_1.24.4 graph_1.82.0
[25] UCSC.utils_1.0.0 purrr_1.0.2 bit_4.5.0
[28] zlibbioc_1.50.0 cachem_1.1.0 graphite_1.50.0
[31] aplot_0.2.3 GenomeInfoDb_1.40.1 jsonlite_1.8.8
[34] blob_1.2.4 BiocParallel_1.38.0 tweenr_2.0.3
[37] parallel_4.4.1 R6_2.5.1 stringi_1.8.4
[40] RColorBrewer_1.1-3 GOSemSim_2.30.2 Rcpp_1.0.13
[43] R.utils_2.12.3 Matrix_1.7-0 splines_4.4.1
[46] igraph_2.0.3 tidyselect_1.2.1 qvalue_2.36.0
[49] rstudioapi_0.17.1 viridis_0.6.5 codetools_0.2-20
[52] lattice_0.22-6 tibble_3.2.1 plyr_1.8.9
[55] treeio_1.28.0 withr_3.0.2 KEGGREST_1.44.1
[58] gridGraphics_0.5-1 scatterpie_0.2.4 polyclip_1.10-7
[61] Biostrings_2.72.1 BiocManager_1.30.25 pillar_1.9.0
[64] ggtree_3.12.0 ggfun_0.1.7 generics_0.1.3
[67] ggplot2_3.5.1 munsell_0.5.1 scales_1.3.0
[70] tidytree_0.4.6 glue_1.7.0 lazyeval_0.2.2
[73] tools_4.4.1 data.table_1.16.0 fgsea_1.30.0
[76] fs_1.6.4 graphlayouts_1.1.1 fastmatch_1.1-4
[79] tidygraph_1.3.1 cowplot_1.1.3 grid_4.4.1
[82] tidyr_1.3.1 ape_5.8 colorspace_2.1-1
[85] nlme_3.1-165 GenomeInfoDbData_1.2.12 patchwork_1.3.0
[88] ggforce_0.4.2 cli_3.6.3 rappdirs_0.3.3
[91] fansi_1.0.6 viridisLite_0.4.2 dplyr_1.1.4
[94] gtable_0.3.6 R.methodsS3_1.8.2 yulab.utils_0.1.7
[97] digest_0.6.36 ggrepel_0.9.5 ggplotify_0.1.2
[100] farver_2.1.2 memoise_2.0.1 R.oo_1.26.0
[103] lifecycle_1.0.4 httr_1.4.7 GO.db_3.19.1
[106] bit64_4.0.5 MASS_7.3-61
Good question! It indeed would be nice if the output of all ORA-functions is aligned.
I assume Guangchuang Yu, the maintainer of
ReactomePA
, will chime in soon.