Cut down genes displayed on x axis from enrichPlot's heatplot() function
Entering edit mode
saw44 • 0
Last seen 8 months ago

I've created a heatplot using enrichPlot, from GSEA results obtained by clusterProfiler. However, it's plotted a lot of genes on the x axis and it's far too busy (pictured). Is there a way to cut down the amount of genes plotted to make the graph more readable? Thanks for your time!

GSEA_results <- GSEA(ranked_gene_list, 
                         TERM2GENE = m_t2g)

         showCategory = 10,
         foldChange = ranked_gene_list)


sessionInfo( )
R version 4.0.1 (2020-06-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

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

other attached packages:
 [1] DOSE_3.14.0            enrichplot_1.8.1       pathview_1.28.1       
 [4] forcats_0.5.0          stringr_1.4.0          dplyr_1.0.2           
 [7] purrr_0.3.4            readr_1.4.0            tidyr_1.1.2           
[10] tibble_3.0.3           ggplot2_3.3.2          tidyverse_1.3.0       
[13] clusterProfiler_3.16.1 limma_3.44.3           biomaRt_2.44.4        

loaded via a namespace (and not attached):
  [1] snow_0.4-3                  readxl_1.3.1                backports_1.1.10           
  [4] fastmatch_1.1-0             BiocFileCache_1.12.1        plyr_1.8.6                 
  [7] igraph_1.2.6                splines_4.0.1               BiocParallel_1.22.0        
 [10] GenomeInfoDb_1.24.2         urltools_1.7.3              digest_0.6.25              
 [13] htmltools_0.5.0             GOSemSim_2.14.2             viridis_0.5.1              
 [16] GO.db_3.11.4                fansi_0.4.1                 magrittr_1.5               
 [19] memoise_1.1.0               Biostrings_2.56.0           annotate_1.66.0            
 [22] graphlayouts_0.7.1          modelr_0.1.8                matrixStats_0.57.0         
 [25] askpass_1.1                 prettyunits_1.1.1           colorspace_1.4-1           
 [28] blob_1.2.1                  rvest_0.3.6                 rappdirs_0.3.1             
 [31] ggrepel_0.8.2               haven_2.3.1                 xfun_0.18                  
 [34] crayon_1.3.4                RCurl_1.98-1.2              jsonlite_1.7.1             
 [37]         graph_1.66.0                scatterpie_0.1.5           
 [40] genefilter_1.70.0           survival_3.1-12             glue_1.4.2                 
 [43] polyclip_1.10-0             gtable_0.3.0                zlibbioc_1.34.0            
 [46] XVector_0.28.0              DelayedArray_0.14.1         Rgraphviz_2.32.0           
 [49] BiocGenerics_0.34.0         scales_1.1.1                DBI_1.1.0                  
 [52] Rcpp_1.0.5                  viridisLite_0.3.0           xtable_1.8-4               
 [55] progress_1.2.2              gridGraphics_0.5-0          bit_4.0.4                  
 [58] europepmc_0.4               stats4_4.0.1                DT_0.16                    
 [61] htmlwidgets_1.5.2           httr_1.4.2                  fgsea_1.14.0               
 [64] RColorBrewer_1.1-2          ellipsis_0.3.1              pkgconfig_2.0.3            
 [67] XML_3.99-0.5                farver_2.0.3                dbplyr_1.4.4               
 [70] locfit_1.5-9.4              ggplotify_0.0.5             tidyselect_1.1.0           
 [73] labeling_0.4.2              rlang_0.4.7                 reshape2_1.4.4             
 [76] AnnotationDbi_1.50.3        munsell_0.5.0               cellranger_1.1.0           
 [79] tools_4.0.1                 downloader_0.4              cli_2.1.0                  
 [82] generics_0.0.2              RSQLite_2.2.1               broom_0.7.2                
 [85] ggridges_0.5.2             bit64_4.0.5                
 [88] fs_1.5.0                    tidygraph_1.2.0             KEGGREST_1.28.0            
 [91] ggraph_2.0.3                KEGGgraph_1.48.0            DO.db_2.9                  
 [94] xml2_1.3.2                  compiler_4.0.1              rstudioapi_0.11            
 [97] png_0.1-7                   curl_4.3                    reprex_0.3.0               
[100] tweenr_1.0.1                geneplotter_1.66.0          stringi_1.5.3              
[103] lattice_0.20-41             Matrix_1.2-18               vctrs_0.3.4                
[106] pillar_1.4.6                lifecycle_0.2.0             BiocManager_1.30.10        
[109] triebeard_0.3.0             data.table_1.13.0           cowplot_1.1.0              
[112] bitops_1.0-6                GenomicRanges_1.40.0        qvalue_2.20.0              
[115] R6_2.5.0                    gridExtra_2.3               IRanges_2.22.2             
[118] MASS_7.3-51.6               assertthat_0.2.1            SummarizedExperiment_1.18.2
[121] openssl_1.4.3               DESeq2_1.28.1               withr_2.3.0                
[124] S4Vectors_0.26.1            GenomeInfoDbData_1.2.3      parallel_4.0.1             
[127] hms_0.5.3                   grid_4.0.1                  rvcheck_0.1.8              
[130] ggforce_0.3.2               Biobase_2.48.0              lubridate_1.7.9            
[133] tinytex_0.26
clusterProfiler GeneSetEnrichment enrichplot • 290 views
Entering edit mode

from what I've seen here and here, the enrichment analysis is performed on the list of genes of interest that you'd like to display, so that you can then show only those in the heatmap. I hope that helps!


Login before adding your answer.

Traffic: 276 users visited in the last hour
Help About
Access RSS

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6