Introduction.

The goal of goSorensen is to implement the equivalence test introduced in Flores, P., Salicrú, M., Sánchez-Pla, A. and Ocaña, J.(2022) “An equivalence test between features lists, based on the Sorensen - Dice index and the joint frequencies of GO node enrichment”, BMC Bioinformatics, 2022 23:207.

Given two gene lists, L₁ and L₂, (the data) and a given set of n Gene Ontology (GO) terms (the frame of reference for biological significance in these lists), the test is devoted to answer the following question (quite informally stated for the moment): The dissimilarity between the biological information in both lists, is it negligible? To measure the dissimilarity we use the Sorensen-Dice index:

$$ \hat d_S = \hat d(L_1,L_2) = \frac{2n_{11}}{2n_{11} + n_{10} + n_{01}} $$

where n₁₁ corresponds to the number of GO terms (among the n GO terms under consideration) which are enriched in both gene lists, n₁₀ corresponds to the GO terms enriched in L₁ but not in L₂ and n₀₁ the reverse, those enriched in L₂ but not in L₁. For notation completeness, n₀₀ would correspond to those GO terms not enriched in both lists; it is not considered by the Sorensen-Dice index but would be necessary in some computations. Obviously, n = n₁₁ + n₁₀ + n₀₁ + n₀₀.

More precisely, the above problem can be restated as follows: Given a negligibility threshold d₀ for the Sorensen-Dice values, to decide negligibility corresponds to rejecting the null hypothesis H₀ : d_S ≥ d₀ in favour of the alternative H₁ : d_S < d₀, where d_S stands for the “true” value of the Sorensen-Dice dissimilarity (L₁ and L₂ are samples, and the own process of declaring enrichment of a GO term is random, so d̂_S = d̂(L₁, L₂) is an estimate of d_S). Then, a bit more precise statement of the problem is “The dissimilarity between the biological information in two gene lists, is it negligible up to a degree d₀?” Where this information is expressed by means of the Sorensen-Dice dissimilarity measured on the degree of coincidence and non-coincidence in GO terms enrichment among a given set of GO terms.

For the moment, the reference set of GO terms can be only all those GO terms in a given level of one GO ontology, either BP, CC or MF.

if (!requireNamespace("goSorensen", quietly = TRUE)) {
    BiocManager::install("goSorensen")
}
library(goSorensen)

data("allOncoGeneLists")

length(allOncoGeneLists)

## [1] 7

sapply(allOncoGeneLists, length)

##         atlas      cangenes           cis miscellaneous        sanger 
##           991           189           613           187           450 
##    Vogelstein       waldman 
##           419           426

# First 20 gene identifiers of gene lists Vogelstein and sanger:
allOncoGeneLists[["Vogelstein"]][1:20]

##  [1] "10006"  "25"     "27"     "23305"  "91"     "4299"   "3899"   "27125" 
##  [9] "207"    "238"    "139285" "324"    "367"    "23092"  "23365"  "8289"  
## [17] "57492"  "196528" "405"    "79058"

allOncoGeneLists[["sanger"]][1:20]

##  [1] "25"     "27"     "2181"   "57082"  "10962"  "51517"  "27125"  "10142" 
##  [9] "207"    "208"    "217"    "238"    "57714"  "324"    "23365"  "399"   
## [17] "8289"   "405"    "79058"  "171023"

if (!requireNamespace("org.Hs.eg.db", quietly = TRUE)) {
    BiocManager::install("org.Hs.eg.db")
}
library(org.Hs.eg.db)

library(org.Hs.eg.db)

humanEntrezIDs <- keys(org.Hs.eg.db, keytype = "ENTREZID")

Session information.

All software and respective versions used to produce this document are listed below.

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [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] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] org.Hs.eg.db_3.19.1  AnnotationDbi_1.67.0 IRanges_2.39.2      
## [4] S4Vectors_0.43.2     Biobase_2.65.1       BiocGenerics_0.51.0 
## [7] goSorensen_1.7.1     BiocStyle_2.33.1     rmarkdown_2.28      
## 
## loaded via a namespace (and not attached):
##   [1] DBI_1.2.3               gson_0.1.0              shadowtext_0.1.4       
##   [4] gridExtra_2.3           rlang_1.1.4             magrittr_2.0.3         
##   [7] DOSE_3.99.1             compiler_4.4.1          RSQLite_2.3.7          
##  [10] png_0.1-8               vctrs_0.6.5             reshape2_1.4.4         
##  [13] stringr_1.5.1           pkgconfig_2.0.3         crayon_1.5.3           
##  [16] fastmap_1.2.0           XVector_0.45.0          ggraph_2.2.1           
##  [19] utf8_1.2.4              enrichplot_1.25.2       UCSC.utils_1.1.0       
##  [22] purrr_1.0.2             bit_4.0.5               xfun_0.47              
##  [25] zlibbioc_1.51.1         cachem_1.1.0            aplot_0.2.3            
##  [28] GenomeInfoDb_1.41.1     jsonlite_1.8.8          blob_1.2.4             
##  [31] BiocParallel_1.39.0     tweenr_2.0.3            parallel_4.4.1         
##  [34] R6_2.5.1                RColorBrewer_1.1-3      bslib_0.8.0            
##  [37] stringi_1.8.4           jquerylib_0.1.4         GOSemSim_2.31.2        
##  [40] Rcpp_1.0.13             knitr_1.48              goProfiles_1.67.0      
##  [43] R.utils_2.12.3          Matrix_1.7-0            splines_4.4.1          
##  [46] igraph_2.0.3            tidyselect_1.2.1        viridis_0.6.5          
##  [49] qvalue_2.37.0           yaml_2.3.10             codetools_0.2-20       
##  [52] lattice_0.22-6          tibble_3.2.1            plyr_1.8.9             
##  [55] treeio_1.29.1           withr_3.0.1             KEGGREST_1.45.1        
##  [58] evaluate_0.24.0         gridGraphics_0.5-1      CompQuadForm_1.4.3     
##  [61] scatterpie_0.2.4        polyclip_1.10-7         Biostrings_2.73.1      
##  [64] ggtree_3.13.1           pillar_1.9.0            BiocManager_1.30.25    
##  [67] clusterProfiler_4.13.3  ggfun_0.1.6             generics_0.1.3         
##  [70] ggplot2_3.5.1           tidytree_0.4.6          munsell_0.5.1          
##  [73] scales_1.3.0            glue_1.7.0              lazyeval_0.2.2         
##  [76] maketools_1.3.0         tools_4.4.1             sys_3.4.2              
##  [79] data.table_1.16.0       fgsea_1.31.0            graphlayouts_1.1.1     
##  [82] buildtools_1.0.0        fs_1.6.4                fastmatch_1.1-4        
##  [85] tidygraph_1.3.1         cowplot_1.1.3           grid_4.4.1             
##  [88] ape_5.8                 tidyr_1.3.1             colorspace_2.1-1       
##  [91] nlme_3.1-166            patchwork_1.2.0         GenomeInfoDbData_1.2.12
##  [94] ggforce_0.4.2           cli_3.6.3               fansi_1.0.6            
##  [97] viridisLite_0.4.2       dplyr_1.1.4             gtable_0.3.5           
## [100] R.methodsS3_1.8.2       yulab.utils_0.1.7       sass_0.4.9             
## [103] digest_0.6.37           ggplotify_0.1.2         ggrepel_0.9.5          
## [106] farver_2.1.2            memoise_2.0.1           htmltools_0.5.8.1      
## [109] R.oo_1.26.0             lifecycle_1.0.4         httr_1.4.7             
## [112] GO.db_3.19.1            bit64_4.0.5             MASS_7.3-61

Bibliography.

Flores, P., Salicrú, M., Sánchez-Pla, A. et al. An equivalence test between features lists, based on the Sorensen–Dice index and the joint frequencies of GO term enrichment. BMC Bioinformatics 23, 207 (2022). https://doi.org/10.1186/s12859-022-04739-2