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Description
I have a dataframe (df) attached.
There is a column of Entrez IDs and a column of groups (groupA or groupB)
I can run enrichKEGG on the entire dataset successfully, but compareCluster does not work
In code not shown here, I can run enrichKEGG just on groupA Entrez IDs and that works as well
It seems that others have had a similar issue but I cannot adapt it to my issue. Any suggestions would be greatly appreciated.
library(clusterProfiler)
library(tidyverse)
R.utils::setOption("clusterProfiler.download.method","auto")
df <- read.csv('df.csv')
#run enrichKEGG on all entrez IDs
kegg_all <- enrichKEGG(gene = df$entrez, organism = 'mmu', pvalueCutoff = 0.05)
dotplot(kegg_all)
#run compareCluster with enrichKEGG between the 2 groups
kegg_compare <- compareCluster(entrez~group, data=df, fun=enrichKEGG, pvalueCutoff = 1, qvalueCutoff=1)
Warning message:
In compareCluster(entrez ~ group, data = df, fun = enrichKEGG, pvalueCutoff = 1, :
No enrichment found in any of gene cluster, please check your input...
> sessionInfo()
R version 4.5.0 (2025-04-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] data.table_1.17.0 org.Mm.eg.db_3.21.0 AnnotationDbi_1.70.0 IRanges_2.42.0 S4Vectors_0.46.0
[6] Biobase_2.68.0 BiocGenerics_0.54.0 generics_0.1.3 lubridate_1.9.4 forcats_1.0.0
[11] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
[16] tibble_3.2.1 ggplot2_3.5.2 tidyverse_2.0.0 clusterProfiler_4.16.0 xlsx_0.6.5
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 farver_2.1.2 blob_1.2.4 R.utils_2.13.0
[5] Biostrings_2.76.0 lazyeval_0.2.2 fastmap_1.2.0 digest_0.6.37
[9] timechange_0.3.0 lifecycle_1.0.4 KEGGREST_1.48.0 tidytree_0.4.6
[13] RSQLite_2.3.11 magrittr_2.0.3 compiler_4.5.0 rlang_1.1.6
[17] tools_4.5.0 utf8_1.2.5 igraph_2.1.4 ggtangle_0.0.6
[21] labeling_0.4.3 bit_4.6.0 gson_0.1.0 plyr_1.8.9
[25] RColorBrewer_1.1-3 aplot_0.2.5 BiocParallel_1.42.0 withr_3.0.2
[29] R.oo_1.27.1 grid_4.5.0 GOSemSim_2.34.0 enrichplot_1.28.2
[33] GO.db_3.21.0 scales_1.4.0 cli_3.6.5 crayon_1.5.3
[37] treeio_1.32.0 rstudioapi_0.17.1 ggtree_3.16.0 tzdb_0.5.0
[41] httr_1.4.7 reshape2_1.4.4 DBI_1.2.3 qvalue_2.40.0
[45] ape_5.8-1 cachem_1.1.0 DOSE_4.2.0 splines_4.5.0
[49] parallel_4.5.0 ggplotify_0.1.2 BiocManager_1.30.25 XVector_0.48.0
[53] yulab.utils_0.2.0 vctrs_0.6.5 Matrix_1.7-3 jsonlite_2.0.0
[57] hms_1.1.3 gridGraphics_0.5-1 patchwork_1.3.0 bit64_4.6.0-1
[61] ggrepel_0.9.6 glue_1.8.0 codetools_0.2-20 cowplot_1.1.3
[65] stringi_1.8.7 rJava_1.0-11 gtable_0.3.6 GenomeInfoDb_1.44.0
[69] UCSC.utils_1.4.0 pillar_1.10.2 xlsxjars_0.6.1 fgsea_1.34.0
[73] GenomeInfoDbData_1.2.14 R6_2.6.1 lattice_0.22-6 R.methodsS3_1.8.2
[77] png_0.1-8 memoise_2.0.1 ggfun_0.1.8 Rcpp_1.0.14
[81] fastmatch_1.1-6 nlme_3.1-168 fs_1.6.6 pkgconfig_2.0.3
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