Hello,

I have run DESeq analysis on my RNAseq data to find differential expression analysis. Then I wanted to look on gene sets and I used gage package. I can output my data and visualise in pathview. I would like to ask you if I can use the output from gage and use in to make a dot plot like the one shown here https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html#go-enrichment-analysis in the goseq package.

The code to make this plot taken from the site is the following:

goResults %>% 
  top_n(10, wt=-over_represented_pvalue) %>% 
  mutate(hitsPerc=numDEInCat*100/numInCat) %>% 
  ggplot(aes(x=hitsPerc, 
             y=term, 
             colour=over_represented_pvalue, 
             size=numDEInCat)) +
  geom_point() +
  expand_limits(x=0) +
  labs(x="Hits (%)", y="GO term", colour="p value", size="Count")

Some explanations: over_rep_pval: p-value for over representation of the term in the differentially expressed genes numDEInCat: number of differentially expressed genes in this category numInCat: number of genes in this category term: detail of the term

When I use the gage package

fc.go.bp.p <- gage(res.fc, gsets = go.bp.gs)

# convert the go results to data frames
 fc.go.bp.p.up <- as.data.frame(fc.go.bp.p$greater)
 fc.go.bp.p.down <- as.data.frame(fc.go.bp.p$less)
fc.go.bp.p.down$term <-row.names(fc.go.bp.p.down)
 #Plot the top 10
 fc.go.bp.p.down  %>% 
   top_n(10, wt=-q.val) %>% 
   mutate(hitsPerc=exp1*100) %>% 
   ggplot(aes(x=hitsPerc, 
              y=term, 
              colour=q.val, 
              size=set.size)) +
   geom_point() +
   expand_limits(x=0) +
   labs(x="Hits (%)", y="GO term", colour="p value", size="Count")

I am not sure if the exp1 refers the the percentage as the term is not explained in the R documentation. Will the way I have written the plot give an analogous output as the goseq package? The output from gage is p.geomean,stat.mean,p.val,q.val,set.size and exp1.

Thanks, Maria

sessionInfo( )
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

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

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

other attached packages:
 [1] rtracklayer_1.54.0          goseq_1.46.0               
 [3] geneLenDataBase_1.30.0      BiasedUrn_1.07             
 [5] pathview_1.32.0             org.Hs.eg.db_3.13.0        
 [7] GO.db_3.13.0                AnnotationDbi_1.56.2       
 [9] gage_2.42.0                 ggbeeswarm_0.6.0           
[11] RColorBrewer_1.1-2          vsn_3.60.0                 
[13] pcaExplorer_2.18.0          UpSetR_1.4.0               
[15] pheatmap_1.0.12             magrittr_2.0.2             
[17] DEFormats_1.20.0            reshape2_1.4.4             
[19] ggpubr_0.4.0                DESeq2_1.32.0              
[21] SummarizedExperiment_1.22.0 Biobase_2.52.0             
[23] MatrixGenerics_1.4.3        matrixStats_0.61.0         
[25] GenomicRanges_1.44.0        GenomeInfoDb_1.28.4        
[27] IRanges_2.26.0              S4Vectors_0.30.0           
[29] BiocGenerics_0.38.0         forcats_0.5.1              
[31] stringr_1.4.0               dplyr_1.0.8                
[33] purrr_0.3.4                 readr_2.1.2                
[35] tidyr_1.2.0                 tibble_3.1.6               
[37] ggplot2_3.3.5               tidyverse_1.3.1            
[39] edgeR_3.34.1                limma_3.48.3               

loaded via a namespace (and not attached):
  [1] utf8_1.2.2               shinydashboard_0.7.2    
  [3] tidyselect_1.1.2         heatmaply_1.3.0         
  [5] RSQLite_2.2.10           htmlwidgets_1.5.4       
  [7] grid_4.1.2               TSP_1.1-11              
  [9] BiocParallel_1.26.2      munsell_0.5.0           
 [11] preprocessCore_1.54.0    codetools_0.2-18        
 [13] DT_0.21                  withr_2.4.3             
 [15] colorspace_2.0-2         Category_2.60.0         
 [17] filelock_1.0.2           knitr_1.37              
 [19] rstudioapi_0.13          ggsignif_0.6.3          
 [21] NMF_0.23.0               labeling_0.4.2          
 [23] KEGGgraph_1.54.0         GenomeInfoDbData_1.2.7  
 [25] topGO_2.46.0             farver_2.1.0            
 [27] bit64_4.0.5              vctrs_0.3.8             
 [29] generics_0.1.2           xfun_0.29               
 [31] BiocFileCache_2.2.1      R6_2.5.1                
 [33] doParallel_1.0.17        seriation_1.3.2         
 [35] locfit_1.5-9.4           bitops_1.0-7            
 [37] cachem_1.0.6             shinyAce_0.4.1          
 [39] DelayedArray_0.18.0      assertthat_0.2.1        
 [41] BiocIO_1.4.0             promises_1.2.0.1        
 [43] scales_1.1.1             beeswarm_0.4.0          
 [45] gtable_0.3.0             affy_1.70.0             
 [47] rlang_1.0.1              genefilter_1.74.0       
 [49] splines_4.1.2            rstatix_0.7.0           
 [51] lazyeval_0.2.2           shinyBS_0.61            
 [53] broom_0.7.12             checkmate_2.0.0         
 [55] yaml_2.3.5               BiocManager_1.30.16     
 [57] abind_1.4-5              modelr_0.1.8            
 [59] GenomicFeatures_1.46.5   threejs_0.3.3           
 [61] crosstalk_1.2.0          backports_1.4.1         
 [63] httpuv_1.6.5             RBGL_1.68.0             
 [65] tools_4.1.2              gridBase_0.4-7          
 [67] affyio_1.62.0            ellipsis_0.3.2          
 [69] Rcpp_1.0.8               plyr_1.8.6              
 [71] base64enc_0.1-3          progress_1.2.2          
 [73] zlibbioc_1.38.0          RCurl_1.98-1.6          
 [75] prettyunits_1.1.1        viridis_0.6.2           
 [77] haven_2.4.3              ggrepel_0.9.1           
 [79] cluster_2.1.2            fs_1.5.2                
 [81] data.table_1.14.2        SparseM_1.81            
 [83] reprex_2.0.1             hms_1.1.1               
 [85] mime_0.12                evaluate_0.15           
 [87] xtable_1.8-4             XML_3.99-0.8            
 [89] readxl_1.3.1             gridExtra_2.3           
 [91] compiler_4.1.2           biomaRt_2.50.3          
 [93] crayon_1.5.0             htmltools_0.5.2         
 [95] GOstats_2.60.0           mgcv_1.8-39             
 [97] later_1.3.0              tzdb_0.2.0              
 [99] geneplotter_1.72.0       lubridate_1.8.0         
[101] DBI_1.1.2                dbplyr_2.1.1            
[103] MASS_7.3-55              rappdirs_0.3.3          
[105] Matrix_1.4-0             car_3.0-12              
[107] cli_3.2.0                glmpca_0.2.0            
[109] igraph_1.2.11            pkgconfig_2.0.3         
[111] GenomicAlignments_1.30.0 registry_0.5-1          
[113] plotly_4.10.0            xml2_1.3.3              
[115] foreach_1.5.2            annotate_1.72.0         
[117] vipor_0.4.5              rngtools_1.5.2          
[119] pkgmaker_0.32.2          webshot_0.5.2           
[121] XVector_0.32.0           AnnotationForge_1.36.0  
[123] rvest_1.0.2              digest_0.6.29           
[125] graph_1.70.0             Biostrings_2.60.2       
[127] rmarkdown_2.11           cellranger_1.1.0        
[129] dendextend_1.15.2        GSEABase_1.56.0         
[131] restfulr_0.0.13          curl_4.3.2              
[133] Rsamtools_2.10.0         shiny_1.7.1             
[135] rjson_0.2.21             nlme_3.1-155            
[137] lifecycle_1.0.1          jsonlite_1.7.3          
[139] carData_3.0-5            viridisLite_0.4.0       
[141] fansi_1.0.2              pillar_1.7.0            
[143] lattice_0.20-45          KEGGREST_1.34.0         
[145] fastmap_1.1.0            httr_1.4.2              
[147] survival_3.2-13          glue_1.6.1              
[149] png_0.1-7                iterators_1.0.14        
[151] bit_4.0.4                Rgraphviz_2.36.0        
[153] stringi_1.7.6            blob_1.2.2              
[155] memoise_2.0.1