reese3928 / methylgsa

A Bioconductor package and shiny app for DNA methylation data length bias adjustment in gene set testing

methylGSA

methylGSA is a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing.

The Bioconductor package can be found here.
The Bioconductor package vignette can be found here.
The methylGSA paper can be found here.

Shiny app installation

The following packages are required to be installed before launching the app.
Packages from CRAN:

install.packages("DT")    
install.packages("ggplot2")       
install.packages("shinycssloaders")     

Packages from Bioconductor:
If analyzing 450K array, IlluminaHumanMethylation450kanno.ilmn12.hg19 needs to be installed.

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("IlluminaHumanMethylation450kanno.ilmn12.hg19")

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

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")

Launch the app

After installation, the app can be launched with the following code:

library(methylGSA)
methylGSA::runExample()

Step-by-step instructions

A step-by-step instructions on the workflow of the app can be found here.

Citation

Ren, X., & Kuan, P. F. (2019). methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics, 35(11), 1958-1959.

@article{ren2019methylgsa,
title={methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing},
author={Ren, Xu and Kuan, Pei Fen},
journal={Bioinformatics},
volume={35},
number={11},
pages={1958--1959},
year={2019},
publisher={Oxford University Press}
}

methylgsa's Issues

getAnnot.R no default group

Dear,

Currently trying to use methylgometh for EPIC data, for GO, KEGG and Reactome.
When running Reactome, I receive an ERROR:

Error in getAnnot("EPIC") : argument "group" is missing, with no default.

Based on the manual, group should have a default value "all". However, when I manually look in the defined function, I cannot find a declaration of group.

Can you fix this?

Thanks in advance.

Ellen.

cpg.pval - which p-values to use?

When running the analyses, e.g. with the methylglm function, the cpg.pval vector should contain raw p-values from calling significant DMPs or the adjusted p-values?

BTW, can the results table contain the SYMBOLs of the genes that represent the given GO or KEGG ID?

'runExample' is not an exported object from 'namespace:methylGSA'

Hi there,

After following your guidelines on how to launch the Shiny app, I am getting the following error

'runExample' is not an exported object from 'namespace:methylGSA'

> sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
[1] shinydashboard_0.7.1  methylGSA_1.2.3       shinycssloaders_1.0.0
[4] DT_0.18               shiny_1.6.0          

loaded via a namespace (and not attached):
  [1] utf8_1.2.1                                         
  [2] tidyselect_1.1.1                                   
  [3] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
  [4] RSQLite_2.2.7                                      
  [5] AnnotationDbi_1.46.1                               
  [6] htmlwidgets_1.5.3                                  
  [7] grid_3.6.3                                         
  [8] BiocParallel_1.18.1                                
  [9] munsell_0.5.0                                      
 [10] codetools_0.2-18                                   
 [11] preprocessCore_1.46.0                              
 [12] statmod_1.4.36                                     
 [13] colorspace_2.0-1                                   
 [14] GOSemSim_2.10.0                                    
 [15] Biobase_2.44.0                                     
 [16] stats4_3.6.3                                       
 [17] DOSE_3.10.2                                        
 [18] urltools_1.7.3                                     
 [19] GenomeInfoDbData_1.2.1                             
 [20] polyclip_1.10-0                                    
 [21] bit64_4.0.5                                        
 [22] farver_2.1.0                                       
 [23] rhdf5_2.28.1                                       
 [24] vctrs_0.3.8                                        
 [25] generics_0.1.0                                     
 [26] R6_2.5.0                                           
 [27] GenomeInfoDb_1.20.0                                
 [28] illuminaio_0.26.0                                  
 [29] graphlayouts_0.7.1                                 
 [30] locfit_1.5-9.4                                     
 [31] bitops_1.0-7                                       
 [32] cachem_1.0.5                                       
 [33] reshape_0.8.8                                      
 [34] fgsea_1.10.1                                       
 [35] gridGraphics_0.5-1                                 
 [36] DelayedArray_0.10.0                                
 [37] assertthat_0.2.1                                   
 [38] promises_1.2.0.1                                   
 [39] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0 
 [40] scales_1.1.1                                       
 [41] ggraph_2.0.5                                       
 [42] enrichplot_1.4.0                                   
 [43] gtable_0.3.0                                       
 [44] methylumi_2.30.0                                   
 [45] tidygraph_1.2.0                                    
 [46] rlang_0.4.11                                       
 [47] genefilter_1.66.0                                  
 [48] splines_3.6.3                                      
 [49] rtracklayer_1.44.4                                 
 [50] GEOquery_2.52.0                                    
 [51] europepmc_0.4                                      
 [52] BiocManager_1.30.15                                
 [53] reshape2_1.4.4                                     
 [54] GenomicFeatures_1.36.4                             
 [55] httpuv_1.6.1                                       
 [56] qvalue_2.16.0                                      
 [57] clusterProfiler_3.12.0                             
 [58] tools_3.6.3                                        
 [59] ggplotify_0.0.7                                    
 [60] nor1mix_1.3-0                                      
 [61] ggplot2_3.3.3                                      
 [62] ellipsis_0.3.2                                     
 [63] RColorBrewer_1.1-2                                 
 [64] BiocGenerics_0.30.0                                
 [65] siggenes_1.58.0                                    
 [66] ggridges_0.5.3                                     
 [67] Rcpp_1.0.6                                         
 [68] plyr_1.8.6                                         
 [69] progress_1.2.2                                     
 [70] zlibbioc_1.30.0                                    
 [71] purrr_0.3.4                                        
 [72] RCurl_1.98-1.3                                     
 [73] BiasedUrn_1.07                                     
 [74] prettyunits_1.1.1                                  
 [75] openssl_1.4.4                                      
 [76] IlluminaHumanMethylationEPICmanifest_0.3.0         
 [77] viridis_0.6.1                                      
 [78] cowplot_1.1.1                                      
 [79] bumphunter_1.26.0                                  
 [80] S4Vectors_0.22.1                                   
 [81] SummarizedExperiment_1.14.1                        
 [82] ggrepel_0.9.1                                      
 [83] magrittr_2.0.1                                     
 [84] data.table_1.14.0                                  
 [85] DO.db_2.9                                          
 [86] triebeard_0.3.0                                    
 [87] reactome.db_1.68.0                                 
 [88] packrat_0.6.0                                      
 [89] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
 [90] matrixStats_0.58.0                                 
 [91] missMethyl_1.18.0                                  
 [92] hms_1.1.0                                          
 [93] mime_0.10                                          
 [94] xtable_1.8-4                                       
 [95] XML_3.99-0.3                                       
 [96] RobustRankAggreg_1.1                               
 [97] mclust_5.4.7                                       
 [98] IRanges_2.18.3                                     
 [99] gridExtra_2.3                                      
[100] compiler_3.6.3                                     
[101] biomaRt_2.40.5                                     
[102] minfi_1.30.0                                       
[103] tibble_3.1.2                                       
[104] crayon_1.4.1                                       
[105] htmltools_0.5.1.1                                  
[106] later_1.2.0                                        
[107] tidyr_1.1.3                                        
[108] DBI_1.1.1                                          
[109] tweenr_1.0.2                                       
[110] MASS_7.3-54                                        
[111] Matrix_1.3-3                                       
[112] readr_1.4.0                                        
[113] cli_2.5.0                                          
[114] quadprog_1.5-8                                     
[115] parallel_3.6.3                                     
[116] igraph_1.2.6                                       
[117] GenomicRanges_1.36.1                               
[118] pkgconfig_2.0.3                                    
[119] rvcheck_0.1.8                                      
[120] GenomicAlignments_1.20.1                           
[121] IlluminaHumanMethylation450kmanifest_0.4.0         
[122] xml2_1.3.2                                         
[123] foreach_1.5.1                                      
[124] annotate_1.62.0                                    
[125] rngtools_1.5                                       
[126] multtest_2.40.0                                    
[127] beanplot_1.2                                       
[128] XVector_0.24.0                                     
[129] ruv_0.9.7.1                                        
[130] doRNG_1.8.2                                        
[131] scrime_1.3.5                                       
[132] stringr_1.4.0                                      
[133] digest_0.6.27                                      
[134] Biostrings_2.52.0                                  
[135] base64_2.0                                         
[136] fastmatch_1.1-0                                    
[137] DelayedMatrixStats_1.6.1                           
[138] Rsamtools_2.0.3                                    
[139] lifecycle_1.0.0                                    
[140] nlme_3.1-152                                       
[141] jsonlite_1.7.2                                     
[142] Rhdf5lib_1.6.3                                     
[143] viridisLite_0.4.0                                  
[144] askpass_1.1                                        
[145] limma_3.40.6                                       
[146] fansi_0.5.0                                        
[147] pillar_1.6.1                                       
[148] lattice_0.20-44                                    
[149] fastmap_1.1.0                                      
[150] httr_1.4.2                                         
[151] survival_3.2-11                                    
[152] GO.db_3.8.2                                        
[153] glue_1.4.2                                         
[154] UpSetR_1.4.0                                       
[155] iterators_1.0.13                                   
[156] bit_4.0.4                                          
[157] ggforce_0.3.3                                      
[158] stringi_1.6.2                                      
[159] HDF5Array_1.12.3                                   
[160] blob_1.2.1                                         
[161] org.Hs.eg.db_3.8.2                                 
[162] memoise_2.0.0                                      
[163] dplyr_1.0.6

I can launch a Shiny app example runExample("01_hello") but it does not work with methylGSA.
Probably, I am missing something and I hope that someone can help me with this issue.

Support for EPICv2

is there a way we can work with illumina EPICv2 array with full functionalities?

glm.fit: algorithm did not converge

Hello,

Thank you for the package! Very useful and interesting!!

Here I have a question:
I ran the following command:

methylGSA_go <-
methylglm(cpg.pval = cpg.pval,
array.type = "EPIC",
FullAnnot = NULL,
group = "all",
GS.list = NULL,
GS.idtype = "ENTREZID",
GS.type = "GO",
minsize = 1,
maxsize = 500,
parallel = FALSE,
BPPARAM = bpparam())

And got: "glm.fit: algorithm did not converge"

Would you in this case advise not to use these results? Or suggest to change the setting...?

Thanks!

subscript out of bounds

Hi,

I'm using your tool to perform a gene-set enrichment for some EWAS datasets. I want to test 7 curated gene-sets using "methylRRA" and the "GSEA" method, but it give back an error only when I use a dataset performed with "450K" chip.

ra1 = methylRRA(cpg.pval = obj, method = "GSEA", minsize = 30, maxsize = 2000, array.type="450K", GS.list=mylist, GS.idtype="ENTREZID")
7 gene sets are being tested...
Error in .subset2(x, i, exact = exact) : subscript out of bounds

I've tried with ORA method with the same dataset and it works, also, I have successfully run the "GSEA" method with another dataset performed with the "EPIC" chip.

I don't know how to solve this error, could you help me please?

Thanks!

Judit

Could not find function "getAnnot"

Hi,

I'm currently trying to use the getAnnot function but it tells me the function is impossible to find :

library(methylGSA)
a=getAnnot("EPIC")
Error in getAnnot("EPIC") : could not find function "getAnnot"

packageVersion('methylGSA')
[1] ‘1.8.0’

Would you know where the problem comes from ?

Thanks in advance.

Bastien