cnetplot() error: enrichResult subset with grepl fails when color_edge="category"

[Sophia409]

Description

When using cnetplot() to visualize GO enrichment results, I encountered a reproducible issue:

• If I simply take the top 15 terms by p.adjust (Method 1), cnetplot(color_edge="category") works fine.
• If I subset terms using grepl("signal|pathway") and then take the top 15 (Method 2), cnetplot(color_edge="category") fails with:

Error in grid.Call(C_convert, x, as.integer(whatfrom), as.integer(whatto),  :
  Viewport has zero dimension(s)

This suggests that whenever the enrichResult object is subset using grepl, cnetplot(color_edge="category") cannot render properly.

---

Reproducible Code

library(clusterProfiler)
library(enrichplot)
library(DOSE)

# Built-in dataset
data(geneList, package = "DOSE")
de <- names(geneList)[1:100]

yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
yy <- setReadable(yy, 'org.Hs.eg.db', 'ENTREZID')

# Method 1: global top15
yy1 <- yy
yy1@result <- head(yy@result[order(yy@result$p.adjust), ], 15)

# Method 2: grepl subset + top15
df_sel <- yy@result[
  yy@result$p.adjust < 0.05 &
    grepl("signal|pathway", yy@result$Description, ignore.case=TRUE),
]
df_sel <- head(df_sel[order(df_sel$p.adjust), ], 15)

yy2 <- yy
yy2@result <- df_sel

# Test plots
p1 <- try(cnetplot(yy1, showCategory=15, color_edge="category"))  # works
p2 <- try(cnetplot(yy2, showCategory=15, color_edge="category"))  # error
p3 <- try(cnetplot(yy2, showCategory=15))                         # works

print(p1)  # OK
print(p2)  # Error: Viewport has zero dimension(s)
print(p3)  # OK

---

Expected Behavior

cnetplot() should be able to handle enrichResult objects subset by grepl and render edges with color_edge="category" without error.

---

Actual Behavior

• Works fine with global topN terms.
• Fails with grepl subset when color_edge="category" is specified.
• Works if color_edge is not specified.

---

Environment

R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22000)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] enrichplot_1.30.0      clusterProfiler_4.18.0 readxl_1.4.5           ggplot2_4.0.1          org.Mm.eg.db_3.22.0    openxlsx_4.2.8.1      
 [7] KEGGREST_1.50.0        GO.db_3.22.0           AnnotationDbi_1.72.0   IRanges_2.44.0         S4Vectors_0.48.0       Biobase_2.70.0        
[13] BiocGenerics_0.56.0    generics_0.1.4         dplyr_1.1.4            GOSemSim_2.36.0        DOSE_4.4.0  

---

Question

Is this a bug in how cnetplot() handles subsetted enrichResult objects?

---

[guidohooiveld]

Since you have posted this a while ago I don't know whether this still is relevant...

Anyway, in essence you selection method does work fine!
Yet, I would approach it slightly different. That is, I would select the relevant pathways/gene sets (with grepl) 'outside' the functioncnetplot(), and rather only provide the Description of the selected pathways to cnetplot(). Below some code to get you inspired.

Note that for now I am using the development (GitHub) versions of clusterProfiler and enrichplot (plus of its dependencies GOSemSim and DOSE), because some critical bugs have been fixed. When using Windows, be sure to have RTools installed (link)!
You will also need to use ggtangle version 0.1.1 (or later), which is available at CRAN. Because of a bug, do not use version 0.1.0.

install libraries

> ## make sure ggtangle version 0.1.1 (or later) is installed
> BiocManager::install(c('ggtangle'), force=TRUE)
>
> ## install dev / GitHub versions of the other packages in this order
> BiocManager::install(c('YuLab-SMU/DOSE', 'YuLab-SMU/GOSemSim',
+                        'YuLab-SMU/clusterProfiler', 'YuLab-SMU/enrichplot'), force=TRUE)

example code

> ## load required libraries
> library(clusterProfiler)
> library(enrichplot)
> 
> # Built-in dataset
> data(geneList, package = "DOSE")
> de <- names(geneList)[1:100]
> 
> ## perform GO ORA
> ## note that significance cutoff is set at p.adjust < 0.05
> yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.05)

> yy <- pairwise_termsim(yy)
> yy <- setReadable(yy, 'org.Hs.eg.db', 'ENTREZID')
> 
> 
> ## select 'ID' of relevant pathways
> ## relevant pathways are selected using 'grepl'.
> my.pathways <- yy@result[
+                          yy@result$p.adjust < 0.05 &
+                          grepl("signal|pathway", yy@result$Description, ignore.case=TRUE),
+                         ]$ID
> 
> ## note 12 pathways have been selected,
> ## thus 12 pathways contain 'signal' OR 'pathway', AND have p.adjust < 0.05.
> length(my.pathways)
[1] 12
> my.pathways
 [1] "GO:0000075" "GO:0007094" "GO:0071173" "GO:0071174" "GO:0031577"
 [6] "GO:0007093" "GO:0070098" "GO:0031570" "GO:0007095" "GO:0033313"
[11] "GO:0070303" "GO:0070302"
> 
> 
> 
> ## select 'Description' of the top 10 pathways ordered on p.adjust
> my.pathways.ordered <- 
+     head( yy@result[ my.pathways[ order( yy@result[my.pathways,"p.adjust"] ) ], ], 10)$Description
> 
> ## check
> length(my.pathways.ordered)
[1] 10
> my.pathways.ordered
 [1] "cell cycle checkpoint signaling"              
 [2] "mitotic spindle assembly checkpoint signaling"
 [3] "spindle assembly checkpoint signaling"        
 [4] "mitotic spindle checkpoint signaling"         
 [5] "spindle checkpoint signaling"                 
 [6] "mitotic cell cycle checkpoint signaling"      
 [7] "chemokine-mediated signaling pathway"         
 [8] "DNA integrity checkpoint signaling"           
 [9] "mitotic G2 DNA damage checkpoint signaling"   
[10] "meiotic cell cycle checkpoint signaling"      
> 
> ## What if top 20 is selected, out of only 12?
> ## Then those 12 pathways will be returned.
> my.pathways.ordered <- 
+     head( yy@result[ my.pathways[ order( yy@result[my.pathways,"p.adjust"] ) ], ], 20)$Description
>
> ## check
> length(my.pathways.ordered)
[1] 12
> my.pathways.ordered
 [1] "cell cycle checkpoint signaling"                                         
 [2] "mitotic spindle assembly checkpoint signaling"                           
 [3] "spindle assembly checkpoint signaling"                                   
 [4] "mitotic spindle checkpoint signaling"                                    
 [5] "spindle checkpoint signaling"                                            
 [6] "mitotic cell cycle checkpoint signaling"                                 
 [7] "chemokine-mediated signaling pathway"                                    
 [8] "DNA integrity checkpoint signaling"                                      
 [9] "mitotic G2 DNA damage checkpoint signaling"                              
[10] "meiotic cell cycle checkpoint signaling"                                 
[11] "negative regulation of stress-activated protein kinase signaling cascade"
[12] "regulation of stress-activated protein kinase signaling cascade"         
> 
>
> 
> ## make cnetplot of these 12 pathways
> 
> ## First only plot network without any lables.
> ## Also reduce size of the nodes representing gene sets
> p1 <- enrichplot::cnetplot(yy, layout=igraph::layout_in_circle,
+                            showCategory = my.pathways.ordered, size_category = 0.6,
+                            node_label="none", color_edge="category", foldChange = geneList)
> 
> ## add gene labels
> p2 <- p1 + geom_cnet_label(node_label = "item",  size = 2.5, color = "black")
> 
> ## add gene set lables
> p3 <- p2 + geom_cnet_label(node_label = "category",  size = 3,
+                            color = "blue", fontface = "bold.italic")
> 
> print(p3)

> sessionInfo()
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Europe/Amsterdam
tzcode source: internal

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

other attached packages:
[1] ggpubr_0.6.2           ggplot2_4.0.1          enrichplot_1.31.3.007 
[4] clusterProfiler_4.19.3

loaded via a namespace (and not attached):
  [1] DBI_1.2.3               gson_0.1.0              rlang_1.1.7            
  [4] magrittr_2.0.4          DOSE_4.5.1              compiler_4.5.1         
  [7] RSQLite_2.4.5           png_0.1-8               systemfonts_1.3.1      
 [10] vctrs_0.6.5             reshape2_1.4.5          stringr_1.6.0          
 [13] pkgconfig_2.0.3         crayon_1.5.3            fastmap_1.2.0          
 [16] backports_1.5.0         XVector_0.50.0          labeling_0.4.3         
 [19] purrr_1.2.1             bit_4.6.0               cachem_1.1.0           
 [22] aplot_0.2.9             jsonlite_2.0.0          blob_1.3.0             
 [25] tidydr_0.0.6            tweenr_2.0.3            broom_1.0.11           
 [28] parallel_4.5.1          cluster_2.1.8.1         R6_2.6.1               
 [31] stringi_1.8.7           RColorBrewer_1.1-3      car_3.1-3              
 [34] enrichit_0.0.9          GOSemSim_2.37.2         Rcpp_1.1.1             
 [37] Seqinfo_1.0.0           ggtangle_0.1.1          IRanges_2.44.0         
 [40] splines_4.5.1           igraph_2.2.1            tidyselect_1.2.1       
 [43] qvalue_2.42.0           dichromat_2.0-0.1       abind_1.4-8            
 [46] lattice_0.22-7          tibble_3.3.1            plyr_1.8.9             
 [49] Biobase_2.70.0          treeio_1.34.0           withr_3.0.2            
 [52] KEGGREST_1.50.0         S7_0.2.1                gridGraphics_0.5-1     
 [55] scatterpie_0.2.6        polyclip_1.10-7         Biostrings_2.78.0      
 [58] pillar_1.11.1           ggtree_4.0.4            carData_3.0-5          
 [61] stats4_4.5.1            ggfun_0.2.0             generics_0.1.4         
 [64] S4Vectors_0.48.0        scales_1.4.0            tidytree_0.4.7         
 [67] glue_1.8.0              gdtools_0.4.4           lazyeval_0.2.2         
 [70] tools_4.5.1             ggnewscale_0.5.2        ggsignif_0.6.4         
 [73] ggiraph_0.9.2           fs_1.6.6                cowplot_1.2.0          
 [76] grid_4.5.1              tidyr_1.3.2             ape_5.8-1              
 [79] AnnotationDbi_1.72.0    nlme_3.1-168            patchwork_1.3.2        
 [82] ggforce_0.5.0           Formula_1.2-5           cli_3.6.5              
 [85] rappdirs_0.3.3          fontBitstreamVera_0.1.1 dplyr_1.1.4            
 [88] gtable_0.3.6            rstatix_0.7.3           yulab.utils_0.2.3      
 [91] digest_0.6.39           fontquiver_0.2.1        BiocGenerics_0.56.0    
 [94] ggrepel_0.9.6           ggplotify_0.1.3         org.Hs.eg.db_3.22.0    
 [97] htmlwidgets_1.6.4       farver_2.1.2            memoise_2.0.1          
[100] htmltools_0.5.9         lifecycle_1.0.5         httr_1.4.7             
[103] GO.db_3.22.0            fontLiberation_0.1.0    bit64_4.6.0-1          
[106] MASS_7.3-65            
>