Getting started

Starting with a list of genes of interest (eg. coming from a differential expression analysis), apply any method for the identification of eneriched GO terms (see GOStats or GSEA).

rrvgo does not care about genes, but GO terms. The input is a vector of enriched GO terms, along with (recommended, but not mandatory) a vector of scores. If scores are not provided, rrvgo takes the GO term (set) size as a score, thus favoring broader terms.

Calculating the similarity matrix and reducing GO terms

First step is to get the similarity matrix between terms. The function calculateSimMatrix takes a list of GO terms for which the semantic simlarity is to be calculated, an OrgDb object for an organism, the ontology of interest and the method to calculate the similarity scores.

library(rrvgo)
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID,
                                orgdb="org.Hs.eg.db",
                                ont="BP",
                                method="Rel")

The semdata parameter (see ?calculateSimMatrix) is not mandatory as it is calculated on demand. If the function needs to run several times with the same organism, it’s advisable to save the GOSemSim::godata(orgdb, ont=ont) object, in order to reuse it between calls and speedup the calculation of the similarity matrix.

From the similarity matrix one can group terms based on similarity. rrvgo provides the reduceSimMatrix function for that. It takes as arguments i) the similarity matrix, ii) an optional named vector of scores associated to each GO term, iii) a similarity threshold used for grouping terms, and iv) an orgdb object.

scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix,
                                scores,
                                threshold=0.7,
                                orgdb="org.Hs.eg.db")

reduceSimMatrix groups terms which are at least within a similarity below threshold, and selects as the group representative the term with the higher score within the group. In case the vector of scores is not available, reduceSimMatrix can either use the uniqueness of a term (default), or the GO term size. In the case of size, rrvgo will fetch the GO term size from the OrgDb object and use it as the score, thus favoring broader terms. Please note that scores are interpreted in the direction that higher are better, therefore if you use p-values as scores, minus log-transform them before.

NOTE:rrvgo uses the similarity between pairs of terms to compute a distance matrix, defined as (1-simMatrix). The terms are then hierarchically clustered using complete linkage, and the tree is cut at the desired threshold, picking the term with the highest score as the representative of each group.
Therefore, higher thresholds lead to fewer groups, and the threshold should be read as the minimum similarity between group representatives.

Plotting and interpretation

rrvgo provides several methods for plotting and interpreting the results.

heatmapPlot(simMatrix,
            reducedTerms,
            annotateParent=TRUE,
            annotationLabel="parentTerm",
            fontsize=6)

scatterPlot(simMatrix, reducedTerms)

Treemap plot

Treemaps are space-filling visualization of hierarchical structures. The terms are grouped (colored) based on their parent, and the space used by the term is proportional to the score. Treemaps can help with the interpretation of the summarized results and also comparing differents sets of GO terms.

treemapPlot(reducedTerms)

treemap

The function internally uses treemap, and further parameters can be passed to this function.

wordcloudPlot(reducedTerms, min.freq=1, colors="black")

Shiny app

To make the software more accessible to a non-technical audience, rrvgo packages a shiny app which can be accessed calling the shiny_rrvgo() function from the R console.

rrvgo::shiny_rrvgo()

shiny_app

The app offers interactive access to the plots and tables calculated by rrvgo.

Similarity methods

All similarity measures available are those implemented in the GOSemSim package, namely the Resnik, Lin, Relevance, Jiang and Wang methods. See the Semantic Similarity Measurement Based on GO section from the GOSeSim documentation for more details.

Organisms

Bioconductor current provides OrgDb objects for 20 species provided by the following packages:

If the organism is not supported in Bioconductor, you can still build your own OrgDb object usign the AnnotationForge package and rendering the necessary data for semantic similarity using the GOSemSim package with:

my_new_fancy_orgdb_object <- 'org.Zz.eg.db'
hsGO <- GOSemSim::godata(my_new_fancy_orgdb_object, ont="MF")

Gene Ontologies

One of Biologiocal Process (BP), Molecular Function (MF) or Cellular Compartment (CC).

Reporting problems or bugs

If you run into problems using rrvgo, the Bioconductor Support site is a good first place to ask for help. If you think there is a bug or an unreported feature, you can report it using the rrvgo github site.

Session info

The following package and versions were used in the production of this vignette.

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
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##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] rrvgo_1.19.0     knitr_1.48       BiocStyle_2.35.0
## 
## loaded via a namespace (and not attached):
##   [1] tidyselect_1.2.1        gridBase_0.4-7          farver_2.1.2           
##   [4] dplyr_1.1.4             blob_1.2.4              R.utils_2.12.3         
##   [7] Biostrings_2.75.0       fastmap_1.2.0           treemap_2.4-4          
##  [10] promises_1.3.0          digest_0.6.37           mime_0.12              
##  [13] lifecycle_1.0.4         NLP_0.3-0               KEGGREST_1.47.0        
##  [16] RSQLite_2.3.7           magrittr_2.0.3          compiler_4.4.1         
##  [19] rlang_1.1.4             sass_0.4.9              tools_4.4.1            
##  [22] wordcloud_2.6           igraph_2.1.1            utf8_1.2.4             
##  [25] yaml_2.3.10             data.table_1.16.2       labeling_0.4.3         
##  [28] askpass_1.2.1           bit_4.5.0               reticulate_1.39.0      
##  [31] xml2_1.3.6              RColorBrewer_1.1-3      withr_3.0.2            
##  [34] BiocGenerics_0.53.0     sys_3.4.3               R.oo_1.26.0            
##  [37] grid_4.4.1              stats4_4.4.1            fansi_1.0.6            
##  [40] GOSemSim_2.33.0         xtable_1.8-4            tm_0.7-14              
##  [43] colorspace_2.1-1        GO.db_3.20.0            ggplot2_3.5.1          
##  [46] scales_1.3.0            cli_3.6.3               rmarkdown_2.28         
##  [49] crayon_1.5.3            generics_0.1.3          umap_0.2.10.0          
##  [52] RSpectra_0.16-2         httr_1.4.7              DBI_1.2.3              
##  [55] cachem_1.1.0            zlibbioc_1.52.0         parallel_4.4.1         
##  [58] AnnotationDbi_1.69.0    BiocManager_1.30.25     XVector_0.46.0         
##  [61] yulab.utils_0.1.7       vctrs_0.6.5             Matrix_1.7-1           
##  [64] jsonlite_1.8.9          slam_0.1-54             IRanges_2.41.0         
##  [67] S4Vectors_0.44.0        bit64_4.5.2             ggrepel_0.9.6          
##  [70] maketools_1.3.1         jquerylib_0.1.4         glue_1.8.0             
##  [73] codetools_0.2-20        gtable_0.3.6            later_1.3.2            
##  [76] GenomeInfoDb_1.43.0     UCSC.utils_1.2.0        munsell_0.5.1          
##  [79] tibble_3.2.1            pillar_1.9.0            htmltools_0.5.8.1      
##  [82] openssl_2.2.2           GenomeInfoDbData_1.2.13 R6_2.5.1               
##  [85] lattice_0.22-6          evaluate_1.0.1          shiny_1.9.1            
##  [88] Biobase_2.67.0          highr_0.11              R.methodsS3_1.8.2      
##  [91] png_0.1-8               pheatmap_1.0.12         memoise_2.0.1          
##  [94] httpuv_1.6.15           bslib_0.8.0             Rcpp_1.0.13            
##  [97] org.Hs.eg.db_3.20.0     xfun_0.48               fs_1.6.5               
## [100] buildtools_1.0.0        pkgconfig_2.0.3