unable to visualize enriched GO terms as a directed acyclic graph with all three ONTOLOGies

[dryezl]

First, I wonder if it is possible to visualize across three ontologies.

The three GO aspects are is a disjoint, meaning that no is a relation exists between terms from the different ontology aspects. However, other relationships such as part of and occurs in can operate between terms from different GO aspects.
https://geneontology.org/docs/ontology-documentation/

If it's possible, how can I visualize them in one plot?

I tried the following code, but got errors.

# code for reproduce 

library(clusterProfiler)
data(geneList, package = "DOSE")
de <- names(geneList)[1:100]
if (file.exists("tmp/yy_all.RDS")) {
    yy <- readRDS("tmp/yy_all.RDS")
} else {
    yy <- enrichGO(de, 'org.Hs.eg.db', ont="ALL", pvalueCutoff=0.01
, pool = T # I have tried this arugment T and F
)
    saveRDS(yy, "tmp/yy_all.RDS")
}
goplot(yy)

Output

> goplot(yy)
GOALL.sqlite is not found, download it online...
Error in `httr2::req_perform()`:
! HTTP 404 Not Found.
Run `rlang::last_trace()` to see where the error occurred.

I tried to debug(goplot), and got infinite error messages. Therefore I shut down the R from outside.

Error: INTEGER() can only be applied to a 'integer', not a 'unknown type #29'
In addition: Warning message:
type 29 is unimplemented in 'type2char' 

session.info

> xfun::session_info()
R version 4.5.0 (2025-04-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045), RStudio 2023.12.1.402

Locale:
  LC_COLLATE=Japanese_Japan.utf8  LC_CTYPE=Japanese_Japan.utf8    LC_MONETARY=Japanese_Japan.utf8
  LC_NUMERIC=C                    LC_TIME=Japanese_Japan.utf8    

Package version:
  AnnotationDbi_1.70.0    ape_5.8-1               aplot_0.2.5             askpass_1.2.1          
  backports_1.5.0         BH_1.87.0.1             Biobase_2.68.0          BiocGenerics_0.54.0    
  BiocParallel_1.42.0     Biostrings_2.76.0       bit_4.6.0               bit64_4.6.0-1          
  blob_1.2.4              boot_1.3.31             broom_1.0.8             cachem_1.1.0           
  callr_3.7.6             class_7.3.23            cli_3.6.5               clipr_0.8.0            
  clusterProfiler_4.16.0  codetools_0.2-20        colorspace_2.1-1        compiler_4.5.0         
  consort_1.2.2           cowplot_1.1.3           cpp11_0.5.2             crayon_1.5.3           
  curl_6.2.2              data.table_1.17.0       DBI_1.2.3               Deriv_4.1.6            
  desc_1.4.3              digest_0.6.37           doBy_4.6.26             DOSE_4.2.0             
  dplyr_1.1.4             e1071_1.7.16            enrichplot_1.28.0       fansi_1.0.6            
  farver_2.1.2            fastmap_1.2.0           fastmatch_1.1-6         fgsea_1.34.0           
  forcats_1.0.0           formatR_1.14            fs_1.6.6                futile.logger_1.4.3    
  futile.options_1.0.1    gdata_3.0.1             generics_0.1.3          GenomeInfoDb_1.44.0    
  GenomeInfoDbData_1.2.14 ggforce_0.4.2           ggfun_0.1.8             ggnewscale_0.5.1       
  ggplot2_3.5.2           ggplotify_0.1.2         ggrepel_0.9.6           ggtangle_0.0.6         
  ggtree_3.16.0           glue_1.8.0              gmodels_2.19.1          GO.db_3.21.0           
  GOSemSim_2.34.0         graphics_4.5.0          grDevices_4.5.0         grid_4.5.0             
  gridGraphics_0.5-1      gson_0.1.0              gtable_0.3.6            gtools_3.9.5           
  haven_2.5.4             hms_1.1.3               httr_1.4.7              httr2_1.1.2            
  igraph_2.1.4            IRanges_2.42.0          isoband_0.2.7           jsonlite_2.0.0         
  KEGGREST_1.48.0         labeling_0.4.3          labelled_2.14.0         lambda.r_1.2.4         
  lattice_0.22-7          lazyeval_0.2.2          lifecycle_1.0.4         magrittr_2.0.3         
  MASS_7.3.65             Matrix_1.7-3            memoise_2.0.1           methods_4.5.0          
  mgcv_1.9.3              microbenchmark_1.5.0    mime_0.13               minqa_1.2.8            
  mitools_2.4             modelr_0.1.11           nlme_3.1-168            numDeriv_2016.8.1.1    
  openssl_2.3.2           org.Hs.eg.db_3.21.0     parallel_4.5.0          patchwork_1.3.0        
  pillar_1.10.2           pkgbuild_1.4.7          pkgconfig_2.0.3         pkgload_1.4.0          
  plogr_0.2.0             plyr_1.8.9              png_0.1-8               polyclip_1.10.7        
  prettyunits_1.2.0       processx_3.8.6          progress_1.2.3          proxy_0.4.27           
  ps_1.9.1                purrr_1.0.4             qvalue_2.40.0           R.methodsS3_1.8.2      
  R.oo_1.27.0             R.utils_2.13.0          R6_2.6.1                rappdirs_0.3.3         
  RColorBrewer_1.1-3      Rcpp_1.0.14             RcppArmadillo_14.4.2.1  RcppEigen_0.3.4.0.2    
  readr_2.1.5             reshape2_1.4.4          rlang_1.1.6             rprojroot_2.0.4        
  RSQLite_2.3.9           rstudioapi_0.17.1       S4Vectors_0.46.0        scales_1.4.0           
  scatterpie_0.2.4        snow_0.4.4              splines_4.5.0           stats_4.5.0            
  stats4_4.5.0            stringi_1.8.7           stringr_1.5.1           survey_4.4.2           
  survival_3.8.3          sys_3.4.3               systemfonts_1.2.2       t3f2_0.0.0.9000        
  tableone_0.13.2         tibble_3.2.1            tidyr_1.3.1             tidyselect_1.2.1       
  tidytree_0.4.6          tools_4.5.0             treeio_1.32.0           tweenr_2.0.3           
  tzdb_0.5.0              UCSC.utils_1.4.0        utf8_1.2.4              utils_4.5.0            
  vctrs_0.6.5             viridisLite_0.4.2       vroom_1.6.5             withr_3.0.2            
  xfun_0.52               XVector_0.48.0          yulab.utils_0.2.0       zoo_1.8.14            

[guidohooiveld]

I can reproduce the error you observe. It is caused because an attempt is made to download a file that doesn't exist on that web location.

@GuangchuangYu : could you please have a look at this? Also whether in this case a file has to be downloaded at all, or that all info rather should be extracted from the GO.db hosted at Bioconductor (here).

Having said this, since I never tried myself I don't know whether all 3 GO categories (and their interactions) can be jointly visualized through goplot(). It's wait to hear from Guangchuang.

> library(clusterProfiler)
> 
> data(geneList, package = "DOSE")
> de <- names(geneList)[1:100]
> 
> yy <- enrichGO(de, 'org.Hs.eg.db', ont="ALL",
+                pvalueCutoff=0.01, pool = TRUE)

> goplot(yy)
GOALL.sqlite is not found, download it online...
Error in `httr2::req_perform()`:
! HTTP 404 Not Found.
Run `rlang::last_trace()` to see where the error occurred.
> traceback()
12: stop(fallback)
11: signal_abort(cnd)
10: cnd_signal(cnd)
9: handle_resp(req, resp, error_call = error_call)
8: httr2::req_perform(req, path = destfile)
7: yulab.utils:::mydownload(url, gzdbfile)
6: load_onto(ont)
5: get_onto_data(ont, output = "list", "ancestor")
4: getAncestors(x@ontology)
3: goplot.enrichResult(x, showCategory = showCategory, color = color, 
       layout = layout, geom = geom, ...)
2: goplot(yy)
1: goplot(yy)
> 

get_onto_data() is called here: https://github.com/YuLab-SMU/GOSemSim/blob/507194f48737a687d34a566e136aa0cf340038e5/R/utilities.R#L59
...
and is defined here: https://github.com/YuLab-SMU/GOSemSim/blob/507194f48737a687d34a566e136aa0cf340038e5/R/OntDb.R#L74
...
whereas it is downloaded here: https://github.com/YuLab-SMU/GOSemSim/blob/507194f48737a687d34a566e136aa0cf340038e5/R/OntDb.R#L122

[GuangchuangYu]

To be clarify, plot all three ontologies is not supported.

[GuangchuangYu]

see my commit: YuLab-SMU/enrichplot@ee94d8e.

[guidohooiveld]

Thanks for your quick response!

One remark, I think you made a typo in the commit: BF should rather be BP.

throw error in goplot() if ontology is not one of the 'MF', 'CC' or 'BF'

[dryezl]

see my commit: YuLab-SMU/enrichplot@ee94d8e.

@GuangchuangYu Thanks for your clarification. Could you please give the reason why it is not supported? Or is it going to be future feature?

[guidohooiveld]

AFAIK most, if not all, GO visualization tools do it like this (thus separate, and not combined visualization). Likely because of practical and theoretical reasons. Recall that the GO was originally constructed as three independent categories,

One approach that combines info across categories is discussed here (https://doi.org/10.1186/s12859-017-1959-3), but it doesn't detail on how to visualize data. I also found this an interesting read (https://doi.org/10.1038/s41598-020-73326-3).

[dryezl]

Thank you for both of your kindness and patience. I am going to close this issue.