enrichkegg函数报错

[PanSX-Dr]

于老师，我最近在用clusterprofiler进行kegg分析，运行enrichkegg函数的时候代码如下：kk <- enrichKEGG(gene = gene,
organism = 'hsa',
pvalueCutoff = 0.05)，但是提示报错信息如下：Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
Error in file(con, "r") :
cannot open the connection to 'https://rest.kegg.jp/link/hsa/pathway'
此外: Warning message:
In file(con, "r") :
URL 'https://rest.kegg.jp/link/hsa/pathway': status was 'SSL connect error'。尝试过很多问题，请问这个该怎么结局。

[huerqiang]

你这个是网络问题，尝试换别的网络，并且安装最新版本的clusterProfiler后再试试吧，

[PanSX-Dr]

您好老师，我的clusterprofiler应该是最新版本的。一直不行，然后尝试了在本地运行enrichkegg，代码如下
kk <- enrichKEGG(gene = gene,
organism = 'hsa',
pvalueCutoff = 0.05,
use_internal_data = T)
然后能够运行出来结果，但是富集的结果和您的教程里不一样，我腹肌的结果如下

在discription那一列只显示的kegg的编号，但是在教程里面应该是具体的某个信号通路的描述。而且我本地运行的还多出来了category和subcategory两列。请问这是什么原因呢？
sessionInfo()
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8
[2] LC_CTYPE=Chinese (Simplified)_China.utf8
[3] LC_MONETARY=Chinese (Simplified)_China.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.utf8

time zone: Asia/Shanghai
tzcode source: internal

attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base

other attached packages:
[1] clusterProfiler_4.10.0

loaded via a namespace (and not attached):
[1] IRanges_2.36.0 nnet_7.3-19
[3] goftest_1.2-3 Biostrings_2.70.2
[5] vctrs_0.6.5 spatstat.random_3.2-2
[7] digest_0.6.34 png_0.1-8
[9] shape_1.4.6 ggrepel_0.9.5
[11] deldir_2.0-2 parallelly_1.36.0
[13] MASS_7.3-60.0.1 reshape2_1.4.4
[15] httpuv_1.6.14 foreach_1.5.2
[17] BiocGenerics_0.48.1 qvalue_2.34.0
[19] withr_3.0.0 ggfun_0.1.4
[21] ellipsis_0.3.2 survival_3.5-7
[23] memoise_2.0.1 gson_0.1.0
[25] tidyHeatmap_1.8.1 tidytree_0.4.6
[27] zoo_1.8-12 GlobalOptions_0.1.2
[29] DNAcopy_1.76.0 pbapply_1.7-2
[31] KEGGREST_1.42.0 promises_1.2.1
[33] httr_1.4.7 globals_0.16.2
[35] fitdistrplus_1.1-11 rstudioapi_0.15.0
[37] pan_1.9 miniUI_0.1.1.1
[39] generics_0.1.3 DOSE_3.28.2
[41] curl_5.2.0 S4Vectors_0.40.2
[43] zlibbioc_1.48.0 ggraph_2.1.0
[45] polyclip_1.10-6 GenomeInfoDbData_1.2.11
[47] SparseArray_1.2.3 interactiveDisplayBase_1.40.0
[49] xtable_1.8-4 stringr_1.5.1
[51] doParallel_1.0.17 S4Arrays_1.2.0
[53] BiocFileCache_2.10.1 hms_1.1.3
[55] glmnet_4.1-8 GenomicRanges_1.54.1
[57] irlba_2.3.5.1 colorspace_2.1-0
[59] filelock_1.0.3 ROCR_1.0-11
[61] reticulate_1.35.0 spatstat.data_3.0-4
[63] magrittr_2.0.3 lmtest_0.9-40
[65] readr_2.1.5 later_1.3.2
[67] viridis_0.6.5 ggtree_3.10.0
[69] lattice_0.22-5 spatstat.geom_3.2-8
[71] future.apply_1.11.1 scattermore_1.2
[73] XML_3.99-0.16.1 shadowtext_0.1.3
[75] cowplot_1.1.3 matrixStats_1.2.0
[77] RcppAnnoy_0.0.22 pillar_1.9.0
[79] nlme_3.1-164 iterators_1.0.14
[81] compiler_4.3.2 RSpectra_0.16-1
[83] stringi_1.8.3 UCSCXenaTools_1.4.8
[85] jomo_2.7-6 tensor_1.5
[87] minqa_1.2.6 SummarizedExperiment_1.32.0
[89] dendextend_1.17.1 lubridate_1.9.3
[91] KEGG.db_3.2.3 plyr_1.8.9
[93] crayon_1.5.2 abind_1.4-5
[95] gridGraphics_0.5-1 locfit_1.5-9.8
[97] sp_2.1-3 graphlayouts_1.1.0
[99] bit_4.0.5 dplyr_1.1.4
[101] fastmatch_1.1-4 codetools_0.2-19
[103] GetoptLong_1.0.5 plotly_4.10.4
[105] mime_0.12 splines_4.3.2
[107] circlize_0.4.15 Rcpp_1.0.12
[109] fastDummies_1.7.3 dbplyr_2.4.0
[111] HDO.db_0.99.1 blob_1.2.4
[113] utf8_1.2.4 clue_0.3-65
[115] BiocVersion_3.18.1 lme4_1.1-35.1
[117] fs_1.6.3 listenv_0.9.1
[119] ggplotify_0.1.2 tibble_3.2.1
[121] maftools_2.18.0 Matrix_1.6-5
[123] statmod_1.5.0 tzdb_0.4.0
[125] tweenr_2.0.2 pkgconfig_2.0.3
[127] tools_4.3.2 cachem_1.0.8
[129] RSQLite_2.3.5 viridisLite_0.4.2
[131] DBI_1.2.1 fastmap_1.1.1
[133] scales_1.3.0 grid_4.3.2
[135] ica_1.0-3 Seurat_5.0.1
[137] broom_1.0.5 AnnotationHub_3.10.0
[139] patchwork_1.2.0 BiocManager_1.30.22
[141] dotCall64_1.1-1 RANN_2.6.1
[143] rpart_4.1.23 farver_2.1.1
[145] tidygraph_1.3.1 scatterpie_0.2.1
[147] yaml_2.3.8 MatrixGenerics_1.14.0
[149] cli_3.6.2 purrr_1.0.2
[151] stats4_4.3.2 GEOquery_2.70.0
[153] leiden_0.4.3.1 lifecycle_1.0.4
[155] uwot_0.1.16 Biobase_2.62.0
[157] backports_1.4.1 BiocParallel_1.36.0
[159] timechange_0.3.0 gtable_0.3.4
[161] rjson_0.2.21 ggridges_0.5.6
[163] progressr_0.14.0 parallel_4.3.2
[165] ape_5.7-1 limma_3.58.1
[167] jsonlite_1.8.8 RcppHNSW_0.5.0
[169] mitml_0.4-5 bitops_1.0-7
[171] ggplot2_3.4.4 bit64_4.0.5
[173] Rtsne_0.17 yulab.utils_0.1.4
[175] spatstat.utils_3.0-4 SeuratObject_5.0.1
[177] mice_3.16.0 GOSemSim_2.28.1
[179] lazyeval_0.2.2 shiny_1.8.0
[181] htmltools_0.5.7 enrichplot_1.22.0
[183] GO.db_3.18.0 sctransform_0.4.1
[185] rappdirs_0.3.3 glue_1.7.0
[187] spam_2.10-0 XVector_0.42.0
[189] RCurl_1.98-1.14 treeio_1.26.0
[191] gridExtra_2.3 boot_1.3-28.1
[193] igraph_2.0.1.1 R6_2.5.1
[195] tidyr_1.3.1 DESeq2_1.42.0
[197] SingleCellExperiment_1.24.0 cluster_2.1.6
[199] aplot_0.2.2 GenomeInfoDb_1.38.5
[201] nloptr_2.0.3 DelayedArray_0.28.0
[203] tidyselect_1.2.0 ggforce_0.4.1
[205] xml2_1.3.6 AnnotationDbi_1.64.1
[207] future_1.33.1 munsell_0.5.0
[209] KernSmooth_2.23-22 data.table_1.15.0
[211] htmlwidgets_1.6.4 fgsea_1.28.0
[213] ComplexHeatmap_2.18.0 RColorBrewer_1.1-3
[215] rlang_1.1.3 spatstat.sparse_3.0-3
[217] spatstat.explore_3.2-6 fansi_1.0.6