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YuLab-SMU
Why does enrichKEGG() output include Category / Subcategory, but in my case gseKEGG() does not?
Hi all！
First, thank you for your excellent work on this package!
I observed a discrepancy when using KEGG analysis or I missed somewhere:
• enrichKEGG(gene = gene, organism = "hsa", minGSSize = 10, pvalueCutoff = 0.05)returns results with Category and Subcategory columns (i.e. pathway-level classification).
• gseKEGG(geneList = geneList, organism = "hsa", minGSSize = 10, pvalueCutoff = 0.05, verbose = FALSE) returns results without those classification columns.
Example output from gseKEGG@result:
ID Description setSize enrichmentScore NES pvalue
hsa00190 hsa00190 Oxidative phosphorylation 96 0.6717222 3.671845 1.000000e-10
hsa03010 hsa03010 Ribosome 71 0.6773091 3.432822 1.000000e-10
hsa04714 hsa04714 Thermogenesis 153 0.5008260 2.991333 1.000000e-10
hsa05208 hsa05208 Chemical carcinogenesis - reactive oxygen species 148 0.4989005 2.926987 1.000000e-10
Could you clarify: 1. Is this difference by design (i.e. gseKEGG() currently does not integrate pathway classification)? 2. If so, what is the recommended way to attach Category / Subcategory information to the gseKEGG() results? 3. Would there be plans in future versions to include classification metadata directly in gseKEGG() outputs for consistency?
Thank you for your time and guidance. 🙏
sessionInfo()
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.0.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Shanghai
tzcode source: internal
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] pbmcapply_1.5.1 future.apply_1.20.0 future_1.67.0 vroom_1.6.5 data.table_1.17.8
[6] viridis_0.6.5 viridisLite_0.4.2 tidyr_1.3.1 patchwork_1.3.1 dplyr_1.1.4
[11] Seurat_5.3.0 SeuratObject_5.1.0 sp_2.2-0 leo.basic_0.0.1 leo.sc_0.0.2
[16] ggrepel_0.9.6 plotly_4.11.0 ggplot2_4.0.0.9000 qs_0.27.3 aPEAR_1.0
[21] DOSE_4.2.0 org.Hs.eg.db_3.21.0 AnnotationDbi_1.70.0 IRanges_2.42.0 S4Vectors_0.46.0
[26] Biobase_2.68.0 BiocGenerics_0.54.0 generics_0.1.4 clusterProfiler_4.16.0 devtools_2.4.5
[31] usethis_3.1.0
loaded via a namespace (and not attached):
[1] igraph_2.1.4 graph_1.86.0 ica_1.0-3 maps_3.4.3
[5] tidyselect_1.2.1 bit_4.6.0 doParallel_1.0.17 clue_0.3-66
[9] lattice_0.22-7 rjson_0.2.23 DoubletFinder_2.0.6 blob_1.2.4
[13] stringr_1.5.1 urlchecker_1.0.1 S4Arrays_1.8.1 dichromat_2.0-0.1
[17] png_0.1-8 cli_3.6.5 ggplotify_0.1.2 goftest_1.2-3
[21] purrr_1.1.0 BiocNeighbors_2.2.0 uwot_0.2.3 curl_6.4.0
[25] mime_0.13 evaluate_1.0.4 tidytree_0.4.6 coin_1.4-3
[29] ComplexHeatmap_2.24.1 stringi_1.8.7 desc_1.4.3 lubridate_1.9.4
[33] httpuv_1.6.16 magrittr_2.0.3 rappdirs_0.3.3 splines_4.5.1
[37] mclust_6.1.1 nortest_1.0-4 prodlim_2025.04.28 RApiSerialize_0.1.4
[41] ggraph_2.2.1 sctransform_0.4.2 ggbeeswarm_0.7.2 sessioninfo_1.2.3
[45] DBI_1.2.3 Nebulosa_1.0.1 reactome.db_1.92.0 withr_3.0.2
[49] class_7.3-23 rprojroot_2.1.0 enrichplot_1.28.4 lmtest_0.9-40
[53] ggnewscale_0.5.2 brio_1.1.5 tidygraph_1.3.1 BiocManager_1.30.26
[57] htmlwidgets_1.6.4 fs_1.6.6 SingleCellExperiment_1.30.1 labeling_0.4.3
[61] SparseArray_1.8.1 MatrixGenerics_1.21.0 reticulate_1.43.0 zoo_1.8-14
[65] XVector_0.48.0 knitr_1.50 UCSC.utils_1.4.0 timechange_0.3.0
[69] foreach_1.5.2 grid_4.5.1 timeDate_4041.110 ggtree_3.16.3
[73] R.oo_1.27.1 bayesbio_1.0.0 RSpectra_0.16-2 irlba_2.3.5.1
[77] tester_0.2.0 ggrastr_1.0.2 fastDummies_1.7.5 gridGraphics_0.5-1
[81] ellipsis_0.3.2 lazyeval_0.2.2 yaml_2.3.10 survival_3.8-3
[85] scattermore_1.2 ROGUE_1.0 crayon_1.5.3 RcppAnnoy_0.0.22
[89] RColorBrewer_1.1-3 progressr_0.15.1 tweenr_2.0.3 mapproj_1.2.12
[93] later_1.4.2 ggridges_0.5.6 codetools_0.2-20 GlobalOptions_0.1.2
[97] profvis_0.4.0 KEGGREST_1.48.1 Rtsne_0.17 ggpie_0.2.5
[101] shape_1.4.6.1 ReactomePA_1.52.0 limma_3.64.3 pkgconfig_2.0.3
[105] spatstat.univar_3.1-4 GenomicRanges_1.60.0 aplot_0.2.8 spatstat.sparse_3.1-0
[109] ape_5.8-1 xtable_1.8-4 highr_0.11 plyr_1.8.9
[113] httr_1.4.7 tools_4.5.1 globals_0.18.0 hardhat_1.4.1
[117] pkgbuild_1.4.8 beeswarm_0.4.0 nlme_3.1-168 crosstalk_1.2.1
[121] MCL_1.0 digest_0.6.37 numDeriv_2016.8-1.1 Matrix_1.7-3
[125] tzdb_0.5.0 furrr_0.3.1 farver_2.1.2 reshape2_1.4.4
[129] Augur_1.0.3 ks_1.15.1 yulab.utils_0.2.0 SnowballC_0.7.1
[133] rpart_4.1.24 glue_1.8.0 cachem_1.1.0 polyclip_1.10-7
[137] Biostrings_2.76.0 ggalluvial_0.12.5 mvtnorm_1.3-3 rsample_1.3.1
[141] presto_1.0.0 parallelly_1.45.1 pkgload_1.4.0 statmod_1.5.0
[145] RcppHNSW_0.6.0 ScaledMatrix_1.16.0 pbapply_1.7-4 miloR_2.4.1
[149] fields_16.3.1 SummarizedExperiment_1.38.1 spam_2.11-1 gson_0.1.0
[153] utf8_1.2.6 gower_1.0.2 gtools_3.9.5 graphlayouts_1.2.2
[157] lsa_0.73.3 gridExtra_2.3 shiny_1.11.1 lava_1.8.1
[161] GenomeInfoDbData_1.2.14 R.utils_2.13.0 pals_1.10 arules_1.7-11
[165] memoise_2.0.1 scales_1.4.0 R.methodsS3_1.8.2 RANN_2.6.2
[169] stringfish_0.17.0 spatstat.data_3.1-6 rstudioapi_0.17.1 cluster_2.1.8.1
[173] spatstat.utils_3.1-5 fitdistrplus_1.2-4 cowplot_1.2.0 colorspace_2.1-1
[177] rlang_1.1.6 GenomeInfoDb_1.44.1 sparseMatrixStats_1.19.0 ipred_0.9-15
[181] dotCall64_1.2 ggforce_0.5.0 circlize_0.4.16 ggtangle_0.0.7
[185] xfun_0.52 pacman_0.5.1 TH.data_1.1-3 remotes_2.5.0
[189] recipes_1.3.1 iterators_1.0.14 matrixStats_1.5.0 modeltools_0.2-24
[193] abind_1.4-8 randomForest_4.7-1.2 GOSemSim_2.34.0 tibble_3.3.0
[197] libcoin_1.0-10 treeio_1.32.0 ggsci_3.2.0 ps_1.9.1
[201] promises_1.3.3 RSQLite_2.4.2 qvalue_2.40.0 sandwich_3.1-1
[205] fgsea_1.34.2 DelayedArray_0.34.1 GO.db_3.21.0 compiler_4.5.1
[209] beachmat_2.24.0 graphite_1.54.0 listenv_0.9.1 Rcpp_1.1.0
[213] parsnip_1.3.2 edgeR_4.6.3 BiocSingular_1.24.0 tensor_1.5.1
[217] MASS_7.3-65 BiocParallel_1.42.1 spatstat.random_3.4-1 R6_2.6.1
[221] fastmap_1.2.0 multcomp_1.4-28 fastmatch_1.1-6 vipor_0.4.7
[225] ROCR_1.0-11 rsvd_1.0.5 nnet_7.3-20 gtable_0.3.6
[229] KernSmooth_2.23-26 miniUI_0.1.2 deldir_2.0-4 htmltools_0.5.8.1
[233] yardstick_1.3.2 RcppParallel_5.1.10 bit64_4.6.0-1 spatstat.explore_3.5-2
[237] lifecycle_1.0.4 S7_0.2.0 processx_3.8.6 callr_3.7.6
[241] vctrs_0.6.5 testthat_3.2.3 spatstat.geom_3.5-0 ggfun_0.2.0
[245] pracma_2.4.4 pillar_1.11.0 locfit_1.5-9.12 jsonlite_2.0.0
[249] expm_1.0-0 GetoptLong_1.0.5
My bad — I just found the append_kegg_category() function.
Meanwhile, a quick extended question: are there standardized “category” / “subcategory” (or hierarchical) labels for other commonly used gene sets (e.g. Reactome, MSigDB, GO)? Many thx!