Hey everyone,

I'm working on developing a script to analyse my DNA methylation data using the minfi package, and keep running into this message whenever I try to run plotSex - can anyone make sense of it?

Cheers!

#code

plotSex(getSex(GRset, cutoff = -2))

#error message

Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': unable to find an inherited method for function ‘colData’ for signature ‘"DFrame"’

# please also include the results of running the following in an R session 

sessionInfo( )

R version 4.0.4 Patched (2021-02-17 r80023)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

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

other attached packages:
 [1] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0 IlluminaHumanMethylationEPICmanifest_0.3.0         
 [3] sva_3.38.0                                          BiocParallel_1.24.1                                
 [5] genefilter_1.72.1                                   mgcv_1.8-33                                        
 [7] nlme_3.1-152                                        minfiData_0.36.0                                   
 [9] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0  IlluminaHumanMethylation450kmanifest_0.4.0         
[11] minfi_1.36.0                                        bumphunter_1.32.0                                  
[13] locfit_1.5-9.4                                      iterators_1.0.13                                   
[15] foreach_1.5.1                                       Biostrings_2.58.0                                  
[17] XVector_0.30.0                                      SummarizedExperiment_1.20.0                        
[19] Biobase_2.50.0                                      MatrixGenerics_1.2.1                               
[21] matrixStats_0.58.0                                  GenomicRanges_1.42.0                               
[23] GenomeInfoDb_1.26.2                                 IRanges_2.24.1                                     
[25] S4Vectors_0.28.1                                    BiocGenerics_0.36.0                                

loaded via a namespace (and not attached):
 [1] ellipsis_0.3.1            siggenes_1.64.0           mclust_5.4.7              base64_2.0               
 [5] rstudioapi_0.13           bit64_4.0.5               AnnotationDbi_1.52.0      xml2_1.3.2               
 [9] codetools_0.2-18          splines_4.0.4             sparseMatrixStats_1.2.1   cachem_1.0.3             
[13] scrime_1.3.5              knitr_1.31                Rsamtools_2.6.0           annotate_1.68.0          
[17] dbplyr_2.1.0              HDF5Array_1.18.1          BiocManager_1.30.10       readr_1.4.0              
[21] compiler_4.0.4            httr_1.4.2                assertthat_0.2.1          Matrix_1.3-2             
[25] fastmap_1.1.0             limma_3.46.0              htmltools_0.5.1.1         prettyunits_1.1.1        
[29] tools_4.0.4               glue_1.4.2                GenomeInfoDbData_1.2.4    dplyr_1.0.4              
[33] rappdirs_0.3.3            doRNG_1.8.2               tinytex_0.29              Rcpp_1.0.6               
[37] vctrs_0.3.6               rhdf5filters_1.2.0        multtest_2.46.0           preprocessCore_1.52.1    
[41] rtracklayer_1.49.5        DelayedMatrixStats_1.12.3 xfun_0.19                 stringr_1.4.0            
[45] lifecycle_1.0.0           rngtools_1.5              XML_3.99-0.5              beanplot_1.2             
[49] edgeR_3.32.1              zlibbioc_1.36.0           MASS_7.3-53               hms_1.0.0                
[53] rhdf5_2.34.0              GEOquery_2.58.0           RColorBrewer_1.1-2        yaml_2.2.1               
[57] curl_4.3                  memoise_2.0.0             biomaRt_2.46.3            reshape_0.8.8            
[61] stringi_1.5.3             RSQLite_2.2.3             GenomicFeatures_1.42.1    rlang_0.4.10             
[65] pkgconfig_2.0.3           bitops_1.0-6              nor1mix_1.3-0             evaluate_0.14            
[69] lattice_0.20-41           purrr_0.3.4               Rhdf5lib_1.12.1           GenomicAlignments_1.26.0 
[73] bit_4.0.4                 tidyselect_1.1.0          plyr_1.8.6                magrittr_2.0.1           
[77] R6_2.5.0                  generics_0.1.0            DelayedArray_0.16.1       DBI_1.1.1                
[81] pillar_1.4.7              survival_3.2-7            RCurl_1.98-1.2            tibble_3.0.6             
[85] crayon_1.4.1              BiocFileCache_1.14.0      rmarkdown_2.6             progress_1.2.2           
[89] grid_4.0.4                data.table_1.13.6         blob_1.2.1                digest_0.6.27            
[93] xtable_1.8-4              tidyr_1.1.2               illuminaio_0.32.0         openssl_1.4.3            
[97] askpass_1.1               quadprog_1.5-8           
>

If you take a look at the help page for plotSex, you can see what the inputs are supposed to be.

Usage:

     getSex(object = NULL, cutoff = -2)
     addSex(object, sex = NULL)
     plotSex(object, id = NULL)

Arguments:

  object: An object of class '[Genomic]MethylSet'.

And further down, you can see the outputs.

Value:

     For 'getSex', a 'DataFrame' with columns 'predictedSex' (a
     character with values 'M' and 'F'), 'xMed' and 'yMed', which are
     the chip-wide medians of measurements on the two sex chromosomes.

So plotSex expects as input a [Genomic]MethylSet, yet getSex returns a DataFrame. Obviously if you provide a DataFrame to a function that expects something entirely different you should not expect things to work. At the very bottom of the help page there is an example.

Examples:

     if(require(minfiData)) {
       GMsetEx <- mapToGenome(MsetEx)
       estSex <- getSex(GMsetEx)
       GMsetEx <- addSex(GMsetEx, sex = estSex)
     }

Which shows that you have to use addSex to add the output from getSex to your GenomicMethylSet, after which you can call plotSex on the updated GenomicMethylSet.