Why `FoldEnrichment`,`RichFactor`and `zScore` are not in the ReactomePA::enrichPathway result
1
0
Entering edit mode
Yifeii.Ge • 0
@73df120c
Last seen 18 hours ago
Singapore

I found that FoldEnrichment,RichFactorand zScore are not in the ReactomePA::enrichPathway result. However, these fields are in the clusterProfiler::enrichKEGG and clusterProfiler::enrichGO. Considering that all of these three functions use DOSE::enricher_internal to perform enrichment analysis and there is an update for adding the above mentioned fields, why ReactomePA::enrichPathway result dose not have these three columns?


library(ReactomePA)
data(geneList, package="DOSE")
de <- names(geneList)[abs(geneList) > 1.5]
x <- enrichPathway(gene=de, pvalueCutoff = 0.05, readable=TRUE)
colnames(x@result)
> colnames(x@result)
[1] "ID"          "Description" "GeneRatio"   "BgRatio"     "pvalue"      "p.adjust"   
[7] "qvalue"      "geneID"      "Count"

library(clusterProfiler)
gene <- names(geneList)[abs(geneList) > 2]
kk <- enrichKEGG(gene         = gene,
                 organism     = 'hsa',
                 pvalueCutoff = 0.05)
colnames(kk@result)
> colnames(kk@result)
 [1] "category"       "subcategory"    "ID"             "Description"    "GeneRatio"     
 [6] "BgRatio"        "RichFactor"     "FoldEnrichment" "zScore"         "pvalue"        
[11] "p.adjust"       "qvalue"         "geneID"         "Count"  

> sessionInfo()
R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8  LC_CTYPE=Chinese (Simplified)_China.utf8   
[3] LC_MONETARY=Chinese (Simplified)_China.utf8 LC_NUMERIC=C                               
[5] LC_TIME=Chinese (Simplified)_China.utf8    

time zone: Asia/Shanghai
tzcode source: internal

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

other attached packages:
[1] clusterProfiler_4.12.6 org.Hs.eg.db_3.19.1    AnnotationDbi_1.66.0   IRanges_2.38.1        
[5] S4Vectors_0.42.1       Biobase_2.64.0         BiocGenerics_0.50.0    ReactomePA_1.48.0     

loaded via a namespace (and not attached):
  [1] DBI_1.2.3               gson_0.1.0              shadowtext_0.1.4       
  [4] gridExtra_2.3           httr2_1.0.5             remotes_2.5.0          
  [7] rlang_1.1.4             magrittr_2.0.3          DOSE_3.30.5            
 [10] compiler_4.4.1          RSQLite_2.3.7           reactome.db_1.88.0     
 [13] png_0.1-8               vctrs_0.6.5             reshape2_1.4.4         
 [16] stringr_1.5.1           pkgconfig_2.0.3         crayon_1.5.3           
 [19] fastmap_1.2.0           XVector_0.44.0          ggraph_2.2.1           
 [22] utf8_1.2.4              enrichplot_1.24.4       graph_1.82.0           
 [25] UCSC.utils_1.0.0        purrr_1.0.2             bit_4.5.0              
 [28] zlibbioc_1.50.0         cachem_1.1.0            graphite_1.50.0        
 [31] aplot_0.2.3             GenomeInfoDb_1.40.1     jsonlite_1.8.8         
 [34] blob_1.2.4              BiocParallel_1.38.0     tweenr_2.0.3           
 [37] parallel_4.4.1          R6_2.5.1                stringi_1.8.4          
 [40] RColorBrewer_1.1-3      GOSemSim_2.30.2         Rcpp_1.0.13            
 [43] R.utils_2.12.3          Matrix_1.7-0            splines_4.4.1          
 [46] igraph_2.0.3            tidyselect_1.2.1        qvalue_2.36.0          
 [49] rstudioapi_0.17.1       viridis_0.6.5           codetools_0.2-20       
 [52] lattice_0.22-6          tibble_3.2.1            plyr_1.8.9             
 [55] treeio_1.28.0           withr_3.0.2             KEGGREST_1.44.1        
 [58] gridGraphics_0.5-1      scatterpie_0.2.4        polyclip_1.10-7        
 [61] Biostrings_2.72.1       BiocManager_1.30.25     pillar_1.9.0           
 [64] ggtree_3.12.0           ggfun_0.1.7             generics_0.1.3         
 [67] ggplot2_3.5.1           munsell_0.5.1           scales_1.3.0           
 [70] tidytree_0.4.6          glue_1.7.0              lazyeval_0.2.2         
 [73] tools_4.4.1             data.table_1.16.0       fgsea_1.30.0           
 [76] fs_1.6.4                graphlayouts_1.1.1      fastmatch_1.1-4        
 [79] tidygraph_1.3.1         cowplot_1.1.3           grid_4.4.1             
 [82] tidyr_1.3.1             ape_5.8                 colorspace_2.1-1       
 [85] nlme_3.1-165            GenomeInfoDbData_1.2.12 patchwork_1.3.0        
 [88] ggforce_0.4.2           cli_3.6.3               rappdirs_0.3.3         
 [91] fansi_1.0.6             viridisLite_0.4.2       dplyr_1.1.4            
 [94] gtable_0.3.6            R.methodsS3_1.8.2       yulab.utils_0.1.7      
 [97] digest_0.6.36           ggrepel_0.9.5           ggplotify_0.1.2        
[100] farver_2.1.2            memoise_2.0.1           R.oo_1.26.0            
[103] lifecycle_1.0.4         httr_1.4.7              GO.db_3.19.1           
[106] bit64_4.0.5             MASS_7.3-61
DOSE ReactomePA • 495 views
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1
Entering edit mode

Good question! It indeed would be nice if the output of all ORA-functions is aligned.

I assume Guangchuang Yu, the maintainer of ReactomePA, will chime in soon.

> library(ReactomePA)
> 
> gene <- c("11171", "8243", "112464", "2194",
+ "9318", "79026", "1654", "65003",
+ "6240", "3476", "6238", "3836",
+ "4176", "1017", "249")
> 
> res.reactome = enrichPathway(gene, pvalueCutoff=0.05)
> as.data.frame(res.reactome)[1,]
                     ID                               Description GeneRatio
R-HSA-68962 R-HSA-68962 Activation of the pre-replicative complex      2/12
             BgRatio       pvalue   p.adjust     qvalue    geneID Count
R-HSA-68962 33/11091 0.0005561733 0.02431985 0.01565458 4176/1017     2
> 
> 
> library(clusterProfiler)
> 
> res.kegg <- enrichKEGG(gene, pvalueCutoff=0.05)
> 
> as.data.frame(res.kegg)[1,]
                   category           subcategory       ID Description
hsa04110 Cellular Processes Cell growth and death hsa04110  Cell cycle
         GeneRatio  BgRatio RichFactor FoldEnrichment   zScore      pvalue
hsa04110      3/12 158/8850 0.01898734       14.00316 6.076849 0.001091161
           p.adjust     qvalue         geneID Count
hsa04110 0.04473758 0.03560629 8243/4176/1017     3
> 
> 
>
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1
Entering edit mode
Guangchuang Yu ★ 1.2k
@guangchuang-yu-5419
Last seen 7 days ago
China/Guangzhou/Southern Medical Univer…

Re-install ReactomePA should ensure it works.

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0
Entering edit mode

It works after re-installation. Thanks! : )

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