Text label size on emmaplot

[fmarinn]

I am running a emmaplot on a on gseGO object. How can I change the size of text labels for each of the categories? I coudn't see any built-in options.
Thanks for any help

[guidohooiveld]

I agree that this is not (yet) documented! However, you can use the approach explained in this post for the cnetplot.
#302 (comment)

Key thing to know is that since the release of October 2024 the code for the generation of networks (cnetplot and emapplot) has been completely rewritten (to allow better customization of the ggplot2-objects). Relevant code has been transferred to the ggtangle package, so you will need to load that as well.

Then you first generate an emapplot without any labels, and then a 2nd layer is added to the ggplot2-object using the function geom_cnet_label. That's all...

See code below for an example applied to an emapplot.

> ## load libraries
> library(clusterProfiler)
> library(enrichplot)
> library(ggtangle)
> library(org.Hs.eg.db)
> 
> ##load sample data
> data(geneList, package="DOSE")
> 
> ## run GSEA
> res <- gseGO(geneList = geneList,
+              OrgDb         = org.Hs.eg.db,
+              ont           = "CC",
+              minGSSize     = 100,
+              maxGSSize     = 500,
+              pvalueCutoff  = 0.10,
+              eps           = 0)
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).

preparing geneSet collections...
GSEA analysis...
leading edge analysis...
done...
> 
> res <- pairwise_termsim(res)
> res <- setReadable(res, "org.Hs.eg.db")
> 
> res
#
# Gene Set Enrichment Analysis
#
#...@organism    Homo sapiens 
#...@setType     CC 
#...@keytype     ENTREZID 
#...@geneList    Named num [1:12495] 4.57 4.51 4.42 4.14 3.88 ...
 - attr(*, "names")= chr [1:12495] "4312" "8318" "10874" "55143" ...
#...nPerm        
#...pvalues adjusted by 'BH' with cutoff <0.1 
#...52 enriched terms found
'data.frame':   52 obs. of  11 variables:
 $ ID             : chr  "GO:0098687" "GO:0000775" "GO:0000779" "GO:0000793" ...
 $ Description    : chr  "chromosomal region" "chromosome, centromeric region" "condensed chromosome, centromeric region" "condensed chromosome" ...
 $ setSize        : int  319 199 148 219 338 450 450 138 181 355 ...
 $ enrichmentScore: num  0.538 0.594 0.621 0.553 0.47 ...
 $ NES            : num  2.52 2.66 2.67 2.49 2.2 ...
 $ pvalue         : num  7.61e-23 3.64e-20 7.08e-18 1.13e-17 5.26e-17 ...
 $ p.adjust       : num  1.18e-20 2.82e-18 3.66e-16 4.39e-16 1.63e-15 ...
 $ qvalue         : num  7.61e-21 1.82e-18 2.36e-16 2.83e-16 1.05e-15 ...
 $ rank           : num  1804 798 798 919 1274 ...
 $ leading_edge   : chr  "tags=28%, list=14%, signal=24%" "tags=22%, list=6%, signal=21%" "tags=24%, list=6%, signal=23%" "tags=21%, list=7%, signal=20%" ...
 $ core_enrichment: chr  "CDCA8/CDC20/CENPE/NDC80/TOP2A/HJURP/SKA1/NEK2/CENPM/RAD51AP1/CENPN/CDK1/ERCC6L/MAD2L1/KIF18A/CDT1/BIRC5/EZH2/TT"| __truncated__ "CDCA8/CDC20/CENPE/NDC80/TOP2A/HJURP/SKA1/NEK2/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/CDT1/BIRC5/EZH2/TTK/NCAPG/AURKB/"| __truncated__ "CDC20/CENPE/NDC80/HJURP/SKA1/NEK2/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/CDT1/BIRC5/TTK/NCAPG/AURKB/AURKA/CCNB1/KIF2C"| __truncated__ "CDC20/CENPE/NDC80/TOP2A/NCAPH/HJURP/SKA1/NEK2/CENPM/CENPN/ERCC6L/MAD2L1/KIF18A/CDT1/BIRC5/TTK/NCAPG/AURKB/CHEK1"| __truncated__ ...
#...Citation
S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang, W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize multiomics data. Nature Protocols. 2024, 19(11):3292-3320 

> 
> 
> ## generate default emapplot
> p1 <- emapplot(res, showCategory=6)
> print(p1)
> 

> ## in order to change the size of the labels, 
> ## make plot without lables (using argument node_label
> p2 <- emapplot(res, showCategory=6, node_label="none")
> print(p2)
> 

> p3 <- p2 + geom_cnet_label(node_label = "all",  size = 6, fontface = "bold")
> print(p3)
>

> ## another example
> p4 <- p2 + geom_cnet_label(node_label = "all",  size = 5, color = "red", fontface = "italic")
> print(p4)
>

> packageVersion("clusterProfiler")
[1] ‘4.14.4’
> packageVersion("enrichplot")
[1] ‘1.26.6’
> packageVersion("ggtangle")
[1] ‘0.0.6’
> 
> sessionInfo()
R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19042)

Matrix products: default


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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] org.Hs.eg.db_3.20.0    AnnotationDbi_1.68.0   IRanges_2.40.1        
[4] S4Vectors_0.44.0       Biobase_2.66.0         BiocGenerics_0.52.0   
[7] ggtangle_0.0.6         enrichplot_1.26.6      clusterProfiler_4.14.4

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1        dplyr_1.1.4             farver_2.1.2           
 [4] blob_1.2.4              R.utils_2.12.3          Biostrings_2.74.1      
 [7] lazyeval_0.2.2          fastmap_1.2.0           digest_0.6.37          
[10] lifecycle_1.0.4         KEGGREST_1.46.0         tidytree_0.4.6         
[13] RSQLite_2.3.9           magrittr_2.0.3          compiler_4.4.2         
[16] rlang_1.1.5             tools_4.4.2             igraph_2.1.4           
[19] data.table_1.16.4       labeling_0.4.3          bit_4.5.0.1            
[22] gson_0.1.0              plyr_1.8.9              RColorBrewer_1.1-3     
[25] aplot_0.2.4             BiocParallel_1.40.0     withr_3.0.2            
[28] purrr_1.0.2             R.oo_1.27.0             grid_4.4.2             
[31] GOSemSim_2.32.0         colorspace_2.1-1        GO.db_3.20.0           
[34] ggplot2_3.5.1           scales_1.3.0            cli_3.6.3              
[37] crayon_1.5.3            treeio_1.30.0           generics_0.1.3         
[40] ggtree_3.14.0           httr_1.4.7              reshape2_1.4.4         
[43] DBI_1.2.3               qvalue_2.38.0           ape_5.8-1              
[46] cachem_1.1.0            DOSE_4.0.0              stringr_1.5.1          
[49] zlibbioc_1.52.0         splines_4.4.2           parallel_4.4.2         
[52] ggplotify_0.1.2         XVector_0.46.0          yulab.utils_0.2.0      
[55] vctrs_0.6.5             Matrix_1.7-2            jsonlite_1.8.9         
[58] gridGraphics_0.5-1      patchwork_1.3.0         bit64_4.6.0-1          
[61] ggrepel_0.9.6           tidyr_1.3.1             snow_0.4-4             
[64] glue_1.8.0              codetools_0.2-20        cowplot_1.1.3          
[67] stringi_1.8.4           gtable_0.3.6            GenomeInfoDb_1.42.3    
[70] UCSC.utils_1.2.0        munsell_0.5.1           tibble_3.2.1           
[73] pillar_1.10.1           fgsea_1.32.2            GenomeInfoDbData_1.2.13
[76] R6_2.5.1                lattice_0.22-6          R.methodsS3_1.8.2      
[79] png_0.1-8               memoise_2.0.1           ggfun_0.1.8            
[82] Rcpp_1.0.14             fastmatch_1.1-6         nlme_3.1-167           
[85] fs_1.6.5                pkgconfig_2.0.3        
> 

[GuangchuangYu]

Thanks @guidohooiveld .

Actually, I wasn't aware of it.