To install and load methylGSA

Depending on the DNA methylation array type, other packages may be needed before running the analysis.

If analyzing 450K array, IlluminaHumanMethylation450kanno.ilmn12.hg19 needs to be loaded.

If analyzing EPIC array, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 needs to be loaded.

If analyzing user-supplied mapping between CpG ID and gene name, neither IlluminaHumanMethylation450kanno.ilmn12.hg19 nor IlluminaHumanMethylationEPICanno.ilm10b4.hg19 needs to be loaded.


The methylGSA package contains functions to carry out gene set analysis adjusting for the number of CpGs of each gene. It has been shown by Geeleher et al [1] that gene set analysis is extremely biased for DNA methylation data. This package contains three main functions to adjust for the bias in gene set analysis.

Supported gene sets and gene ID types

Supported array types

Description of methylglm

methylglm is an extention of GOglm [9]. GOglm adjusts length bias for RNA-Seq data by incorporating gene length as a covariate in logistic regression model. methylglm adjusts length bias in DNA methylation by the number of CpGs instead of gene length. For each gene set, we fit a logistic regression model:

\[logit (\pi_{i}) = \beta_{0} + \beta_{1}x_{i} + \beta_{2}c_{i}\] For each gene \(i\), \(\pi_{i}\) = Pr(gene \(i\) is in gene set), \(x_{i}\) represents negative logarithmic transform of its minimum p-value in differential methylation analysis, and \(c_{i}\) is logarithmic transform of its number of CpGs.

methylglm requires only a simple named vector. This vector contains p-values of each CpG. Names should be their corresponding CpG IDs.


Here is what the input vector looks like:

cg00050873 cg00212031 cg00214611 cg01707559 cg02004872 cg02011394 cg02050847 
    0.2876     0.7883     0.4090     0.8830     0.9405     0.0456     0.5281 
cg02233190 cg02494853 cg02839557 cg02842889 cg03052502 cg03244189 cg03443143 
    0.8924     0.5514     0.4566     0.9568     0.4533     0.6776     0.5726 
cg03683899 cg03706273 cg03750315 cg04016144 cg04042030 cg04448376 
    0.1029     0.8998     0.2461     0.0421     0.3279     0.9545 

Please note that the p-values here in cpg.pval is just for illustration purposes. They are used to illustrate how to use the functions in methylGSA. The actual p-values in differential methylation analysis may be quite different from the p-values in cpg.pval in terms of the magnitude.

Then call methylglm:

         ID                             Description Size    pvalue padj
04080 04080 Neuroactive ligand-receptor interaction  272 0.4811308    1
04060 04060  Cytokine-cytokine receptor interaction  265 0.6961531    1
04010 04010                  MAPK signaling pathway  268 1.0000000    1
04144 04144                             Endocytosis  201 1.0000000    1
04510 04510                          Focal adhesion  200 1.0000000    1
04740 04740                  Olfactory transduction  388 1.0000000    1
04810 04810        Regulation of actin cytoskeleton  213 1.0000000    1
05200 05200                      Pathways in cancer  326 1.0000000    1

Result is a data frame ranked by p-values of gene sets.

Description of methylRRA

Robust rank aggregation [2] is a parameter free model that aggregates several ranked gene lists into a single gene list. The aggregation assumes random order of input lists and assign each gene a p-value based on order statistics. We apply this order statistics idea to adjust for number of CpGs.

For gene \(i\), let \(P_{1}, P_{2}, ... P_{n}\) be the p-values of individual CpGs in differential methylation analysis. Under the null hypothesis, \(P_{1}, P_{2}, ... P_{n} ~ \overset{i.i.d}{\sim} Unif[0, 1]\). Let \(P_{(1)}, P_{(2)}, ... P_{(n)}\) be the order statistics. Define: \[\rho = \text{min}\{\text{Pr}(P_{(1)}<P_{(1)\text{obs}}), \text{Pr}(P_{(2)}<P_{(2)\text{obs}})..., \text{Pr}(P_{(n)}<P_{(n)\text{obs}}) \} \]

methylRRA supports two approaches to adjust for number of CpGs, ORA and GSEAPreranked [3]. In ORA approach, for gene \(i\), conversion from \(\rho\) score into p-value is done by Bonferroni correction [2]. We get a p-value for each gene and these p-values are then corrected for multiple testing use Benjamini & Hochberg procedure [10]. By default, genes satisfy FDR<0.05 are considered DE genes. If there are no DE genes under FDR 0.05, users are able to use sig.cut option to specify a higher FDR cut-off or topDE option to declare top genes to be differentially expressed. We then apply ORA based on these DE genes.

In GSEAPreranked approach, for gene \(i\), we also convert \(\rho\) score into p-value by Bonferroni correction. p-values are converted into z-scores. We then apply Preranked version of Gene Set Enrichment Analysis (GSEAPreranked) on the gene list ranked by the z-scores.


To apply ORA approach, we use argument method = "ORA" in methylRRA

To apply GSEAPreranked approach, we use argument method = "GSEA" in methylRRA

Description of methylgometh

methylgometh calls gometh or gsameth function in missMethyl package [4] to adjust number of CpGs in gene set testing. gometh modifies goseq method [11] by fitting a probability weighting function and resampling from Wallenius non-central hypergeometric distribution.

methylgometh requires two inputs, cpg.pval and sig.cut. sig.cut specifies the cut-off point to declare a CpG as differentially methylated. By default, sig.cut is 0.001. Similar to methylRRA, if no CpG is significant, users are able to specify a higher cut-off or use topDE option to declare top CpGs to be differentially methylated.

Other options

methylGSA provides many other options for users to customize the analysis.


Here an example of user supplied gene sets. The gene ID type is gene symbol

 [1] "ABCA11P"   "ACOT1"     "ACSM2A"    "ADAMTS4"   "ADH4"      "AGTR2"    
 [7] "AMAC1"     "AMY1B"     "AMY2A"     "ANKRD13C"  "ANKRD20A1" "ANKRD20A3"
[13] "ANKRD20A4" "ANXA2P3"   "ANXA8"     "ANXA8L1"   "ARGFXP2"   "ARL17B"   
[19] "ATF5"      "ATP5F1"    "BAGE2"     "BCL8"      "BET3L"     "C12orf24" 
[25] "C15orf62"  "C16orf93"  "C17orf86"  "C19orf70"  "C1orf182"  "C22orf29" 

 [1] "KRTAP5-5"     "KRTAP6-1"     "KRTAP9-8"     "KTI12"        "LASS1"       
 [6] "LCMT2"        "LGALS9B"      "LOC100093631" "LOC100101115" "LOC100101266"
[11] "LOC100130264" "LOC100130932" "LOC100131193" "LOC100131726" "LOC100132247"
[16] "LOC100132832" "LOC100133920" "LOC100286938" "LOC144438"    "LOC145820"   
[21] "LOC148413"    "LOC202781"    "LOC286367"    "LOC339047"    "LOC388499"   
[26] "LOC392196"    "LOC399753"    "LOC440895"    "LOC441294"    "LOC441455"   

 [1] "SNORA17"     "SNORA23"     "SNORA25"     "SNORA2B"     "SNORA31"    
 [6] "SNORA36C"    "SNORA64"     "SNORD11"     "SNORD113-5"  "SNORD113-7" 
[11] "SNORD114-1"  "SNORD114-16" "SNORD114-17" "SNORD114-18" "SNORD114-21"
[16] "SNORD114-27" "SNORD114-28" "SNORD114-5"  "SNORD114-6"  "SNORD114-8" 
[21] "SNORD114-9"  "SNORD116-16" "SNORD116-26" "SNORD116-29" "SNORD12"    
[26] "SNORD125"    "SNORD126"    "SNORD16"     "SNORD38B"    "SNORD50A"   

This is an example of running methylglm with parallel

methylglm and methylRRA support user supplied CpG ID to gene mapping. The mapping is expected to be a matrix, or a data frame or a list. For a matrix or data frame, 1st column should be CpG ID and 2nd column should be gene name. For a list, entry names should be gene names and elements correpond to CpG IDs. This is an example of user supplied CpG to gene mapping:

         CpG   Gene
1 cg00050873  TSPY4
2 cg00212031 TTTY14
3 cg00214611 TMSB4Y
4 cg01707559  TBL1Y
5 cg02004872 TMSB4Y
6 cg02011394  TSPY4

To use user supplied mapping in methylglm or methylRRA, first preprocess the mapping by prepareAnnot function

Test the gene sets using “ORA” in methylRRA, use FullAnnot argument to provide the preprocessed CpG ID to gene mapping

       ID Count Size pvalue padj
GS1   GS1     0  157      1    1
GS2   GS2     0  257      1    1
GS3   GS3     0  181      1    1
GS4   GS4     0  274      1    1
GS5   GS5     0  285      1    1
GS6   GS6     0  108      1    1
GS7   GS7     0  202      1    1
GS8   GS8     0  273      1    1
GS9   GS9     0  206      1    1
GS10 GS10     0  187      1    1

Here is another example. Test Reactome pathways using methylglm


Following bar plot implemented in enrichplot [12], we also provide bar plot to visualize the gene set analysis results. The input of barplot function can be any result returned by methylglm, methylRRA, or methylgometh. Various options are provided for users to customize the plot.


Here is an example of using barplot to visualize the result of methylglm

Session info

R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.10-bioc/R/lib/
LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

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

other attached packages:
 [1] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
 [2] minfi_1.32.0                                      
 [3] bumphunter_1.28.0                                 
 [4] locfit_1.5-9.1                                    
 [5] iterators_1.0.12                                  
 [6] foreach_1.4.8                                     
 [7] Biostrings_2.54.0                                 
 [8] XVector_0.26.0                                    
 [9] SummarizedExperiment_1.16.1                       
[10] DelayedArray_0.12.2                               
[11] BiocParallel_1.20.1                               
[12] matrixStats_0.55.0                                
[13] Biobase_2.46.0                                    
[14] GenomicRanges_1.38.0                              
[15] GenomeInfoDb_1.22.0                               
[16] IRanges_2.20.2                                    
[17] S4Vectors_0.24.3                                  
[18] BiocGenerics_0.32.0                               
[19] methylGSA_1.4.9                                   

loaded via a namespace (and not attached):
  [1] tidyselect_1.0.0                                   
  [2] RSQLite_2.2.0                                      
  [3] AnnotationDbi_1.48.0                               
  [4] grid_3.6.2                                         
  [5] munsell_0.5.0                                      
  [6] codetools_0.2-16                                   
  [7] preprocessCore_1.48.0                              
  [8] statmod_1.4.34                                     
  [9] colorspace_1.4-1                                   
 [10] GOSemSim_2.12.0                                    
 [11] knitr_1.28                                         
 [12] DOSE_3.12.0                                        
 [13] labeling_0.3                                       
 [14] urltools_1.7.3                                     
 [15] GenomeInfoDbData_1.2.2                             
 [16] polyclip_1.10-0                                    
 [17] bit64_0.9-7                                        
 [18] farver_2.0.3                                       
 [19] rhdf5_2.30.1                                       
 [20] vctrs_0.2.3                                        
 [21] xfun_0.12                                          
 [22] BiocFileCache_1.10.2                               
 [23] R6_2.4.1                                           
 [24] illuminaio_0.28.0                                  
 [25] graphlayouts_0.5.0                                 
 [26] bitops_1.0-6                                       
 [27] reshape_0.8.8                                      
 [28] fgsea_1.12.0                                       
 [29] gridGraphics_0.4-1                                 
 [30] assertthat_0.2.1                                   
 [31] promises_1.1.0                                     
 [32] scales_1.1.0                                       
 [33] ggraph_2.0.1                                       
 [34] enrichplot_1.6.1                                   
 [35] gtable_0.3.0                                       
 [36] methylumi_2.32.0                                   
 [37] tidygraph_1.1.2                                    
 [38] rlang_0.4.4                                        
 [39] genefilter_1.68.0                                  
 [40] splines_3.6.2                                      
 [41] rtracklayer_1.46.0                                 
 [42] lazyeval_0.2.2                                     
 [43] GEOquery_2.54.1                                    
 [44] europepmc_0.3                                      
 [45] BiocManager_1.30.10                                
 [46] yaml_2.2.1                                         
 [47] reshape2_1.4.3                                     
 [48] GenomicFeatures_1.38.2                             
 [49] httpuv_1.5.2                                       
 [50] qvalue_2.18.0                                      
 [51] clusterProfiler_3.14.3                             
 [52] tools_3.6.2                                        
 [53] ggplotify_0.0.4                                    
 [54] nor1mix_1.3-0                                      
 [55] ggplot2_3.2.1                                      
 [56] RColorBrewer_1.1-2                                 
 [57] siggenes_1.60.0                                    
 [58] ggridges_0.5.2                                     
 [59] Rcpp_1.0.3                                         
 [60] plyr_1.8.5                                         
 [61] progress_1.2.2                                     
 [62] zlibbioc_1.32.0                                    
 [63] purrr_0.3.3                                        
 [64] RCurl_1.98-1.1                                     
 [65] BiasedUrn_1.07                                     
 [66] prettyunits_1.1.1                                  
 [67] openssl_1.4.1                                      
 [68] IlluminaHumanMethylationEPICmanifest_0.3.0         
 [69] viridis_0.5.1                                      
 [70] cowplot_1.0.0                                      
 [71] ggrepel_0.8.1                                      
 [72] magrittr_1.5                                       
 [73] data.table_1.12.8                                  
 [74] DO.db_2.9                                          
 [75] triebeard_0.3.0                                    
 [76] reactome.db_1.70.0                                 
 [77] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
 [78] missMethyl_1.20.4                                  
 [79] hms_0.5.3                                          
 [80] mime_0.9                                           
 [81] evaluate_0.14                                      
 [82] xtable_1.8-4                                       
 [83] XML_3.99-0.3                                       
 [84] RobustRankAggreg_1.1                               
 [85] mclust_5.4.5                                       
 [86] gridExtra_2.3                                      
 [87] compiler_3.6.2                                     
 [88] biomaRt_2.42.0                                     
 [89] tibble_2.1.3                                       
 [90] crayon_1.3.4                                       
 [91] htmltools_0.4.0                                    
 [92] later_1.0.0                                        
 [93] tidyr_1.0.2                                        
 [94] DBI_1.1.0                                          
 [95] tweenr_1.0.1                                       
 [96] dbplyr_1.4.2                                       
 [97] MASS_7.3-51.5                                      
 [98] rappdirs_0.3.1                                     
 [99] Matrix_1.2-18                                      
[100] readr_1.3.1                                        
[101] quadprog_1.5-8                                     
[102] igraph_1.2.4.2                                     
[103] pkgconfig_2.0.3                                    
[104] rvcheck_0.1.7                                      
[105] GenomicAlignments_1.22.1                           
[106] IlluminaHumanMethylation450kmanifest_0.4.0         
[107] xml2_1.2.2                                         
[108] annotate_1.64.0                                    
[109] rngtools_1.5                                       
[110] multtest_2.42.0                                    
[111] beanplot_1.2                                       
[112] ruv_0.9.7.1                                        
[113] doRNG_1.8.2                                        
[114] scrime_1.3.5                                       
[115] stringr_1.4.0                                      
[116] digest_0.6.25                                      
[117] rmarkdown_2.1                                      
[118] base64_2.0                                         
[119] fastmatch_1.1-0                                    
[120] DelayedMatrixStats_1.8.0                           
[121] curl_4.3                                           
[122] shiny_1.4.0                                        
[123] Rsamtools_2.2.3                                    
[124] lifecycle_0.1.0                                    
[125] nlme_3.1-144                                       
[126] jsonlite_1.6.1                                     
[127] Rhdf5lib_1.8.0                                     
[128] viridisLite_0.3.0                                  
[129] askpass_1.1                                        
[130] limma_3.42.2                                       
[131] pillar_1.4.3                                       
[132] lattice_0.20-40                                    
[133] fastmap_1.0.1                                      
[134] httr_1.4.1                                         
[135] survival_3.1-8                                     
[136] GO.db_3.10.0                                       
[137] glue_1.3.1                                         
[138] bit_1.1-15.2                                       
[139] ggforce_0.3.1                                      
[140] stringi_1.4.6                                      
[141] HDF5Array_1.14.2                                   
[142] blob_1.2.1                                         
[144] memoise_1.1.0                                      
[145] dplyr_0.8.4                                        


[1] Geeleher, Paul, Lori Hartnett, Laurance J Egan, Aaron Golden, Raja Affendi Raja Ali, and Cathal Seoighe. 2013. Gene-Set Analysis Is Severely Biased When Applied to Genome-Wide Methylation Data. Bioinformatics 29 (15). Oxford University Press: 1851–7.

[2] Kolde, Raivo, Sven Laur, Priit Adler, and Jaak Vilo. 2012. Robust Rank Aggregation for Gene List Integration and Meta-Analysis. Bioinformatics 28 (4). Oxford University Press: 573–80.

[3] Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, et al. 2005. Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proceedings of the National Academy of Sciences 102 (43). National Acad Sciences: 15545–50.

[4] Phipson, Belinda, Jovana Maksimovic, and Alicia Oshlack. 2015. MissMethyl: An R Package for Analyzing Data from Illumina’s Humanmethylation450 Platform. Bioinformatics 32 (2). Oxford University Press: 286–88.

[5] Carlson M (2018). Genome wide annotation for Human. R package version 3.6.0.

[6] Ligtenberg W (2018). reactome.db: A set of annotation maps for reactome. R package version 1.64.0.

[7] Hansen, KD. (2016). IlluminaHumanMethylation450kanno.ilmn12.hg19: Annotation for Illumina’s 450k Methylation Arrays. R Package, Version 0.6.0 1.

[8] Hansen, KD. (2017). IlluminaHumanMethylationEPICanno.ilm10b4.hg19: Annotation for Illumina’s Epic Methylation Arrays. R Package, Version 0.6.0 1.

[9] Mi, Gu, Yanming Di, Sarah Emerson, Jason S Cumbie, and Jeff H Chang. 2012. Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression. PloS One 7 (10). Public Library of Science: e46128.

[10] Benjamini, Yoav, and Yosef Hochberg. 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological). JSTOR, 289–300.

[11] Young, Matthew D, Matthew J Wakefield, Gordon K Smyth, and Alicia Oshlack. 2012. Goseq: Gene Ontology Testing for Rna-Seq Datasets. R Bioconductor.

[12] Yu G (2018). enrichplot: Visualization of Functional Enrichment Result. R package version 1.0.2,