guidohooiveld
commented
2 months ago Please show the lines of code you used as well as some of the input ids.
Also, you may first want to update to the the latest versions of R and Bioconductor (your's is > 1 year old). I am suggesting this because under the hood clusterProfiler downloads files from the internet (e.g. KEGG, WiKiPathways), and access/locations to some of the relevant files has been changed on the server side. This has been fixed but possibly not in your version of clusterProfiler.
See e.g. https://github.com/YuLab-SMU/clusterProfiler/issues/559
LoetC
Hi,
When running the "enrichWP" function with Entrez gene IDs as input I get the following error:
No gene can be mapped.... --> Expected input gene ID: --> return NULL...
while in other 'enrich' functions (enrichKEGG, enrichGO, ...) it works. Also, when running get_wp_organisms() function to identify the organisms, it only returns 3 organisms ("Caenorhabditis elegans", "Bos taurus", "Arabidopsis thaliana" ) which may explain why they cannot be mapped using my organism ("Homo sapiens").
I have tried reinstalling clusterProfiler and running in a new session without any other packages to overcome conflicting functions, but no success so far. Any ideas on how to resolve?
Session info: R version 4.3.1 (2023-06-16) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Sonoma 14.4.1
Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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: Europe/Amsterdam tzcode source: internal
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] clusterProfiler_4.10.1
loaded via a namespace (and not attached): [1] DBI_1.2.3 bitops_1.0-8 gson_0.1.0 shadowtext_0.1.4 gridExtra_2.3
[6] rlang_1.1.4 magrittr_2.0.3 DOSE_3.28.2 compiler_4.3.1 RSQLite_2.3.7
[11] png_0.1-8 vctrs_0.6.5 reshape2_1.4.4 stringr_1.5.1 pkgconfig_2.0.3
[16] crayon_1.5.3 fastmap_1.2.0 XVector_0.42.0 ggraph_2.2.1 utf8_1.2.4
[21] HDO.db_0.99.1 enrichplot_1.22.0 purrr_1.0.2 bit_4.0.5 zlibbioc_1.48.2
[26] cachem_1.1.0 aplot_0.2.3 GenomeInfoDb_1.38.8 jsonlite_1.8.8 blob_1.2.4
[31] BiocParallel_1.36.0 tweenr_2.0.3 parallel_4.3.1 R6_2.5.1 stringi_1.8.4
[36] RColorBrewer_1.1-3 GOSemSim_2.28.1 Rcpp_1.0.13 IRanges_2.36.0 Matrix_1.6-5
[41] splines_4.3.1 igraph_2.0.3 tidyselect_1.2.1 qvalue_2.34.0 rstudioapi_0.16.0
[46] viridis_0.6.5 codetools_0.2-20 lattice_0.22-6 tibble_3.2.1 plyr_1.8.9
[51] Biobase_2.62.0 treeio_1.29.1 withr_3.0.1 KEGGREST_1.42.0 gridGraphics_0.5-1
[56] scatterpie_0.2.4 polyclip_1.10-7 Biostrings_2.70.3 pillar_1.9.0 ggtree_3.10.1
[61] stats4_4.3.1 ggfun_0.1.6 generics_0.1.3 RCurl_1.98-1.16 S4Vectors_0.40.2
[66] ggplot2_3.5.1 munsell_0.5.1 scales_1.3.0 tidytree_0.4.6 glue_1.7.0
[71] lazyeval_0.2.2 tools_4.3.1 data.table_1.16.0 fgsea_1.28.0 fs_1.6.4
[76] graphlayouts_1.1.1 fastmatch_1.1-4 tidygraph_1.3.1 cowplot_1.1.3 grid_4.3.1
[81] tidyr_1.3.1 ape_5.8 AnnotationDbi_1.64.1 colorspace_2.1-1 nlme_3.1-166
[86] GenomeInfoDbData_1.2.11 patchwork_1.3.0 ggforce_0.4.2 cli_3.6.3 fansi_1.0.6
[91] viridisLite_0.4.2 dplyr_1.1.4 gtable_0.3.5 yulab.utils_0.1.7 digest_0.6.37
[96] BiocGenerics_0.48.1 ggrepel_0.9.6 ggplotify_0.1.2 farver_2.1.2 memoise_2.0.1
[101] lifecycle_1.0.4 httr_1.4.7 GO.db_3.18.0 bit64_4.0.5 MASS_7.3-60