minfi MethylationEPIC errors
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@ceciliebucher-12672
Last seen 5.2 years ago

I am new to analyzing array data and I am currently trying to analyze some 850k array data using minfi. I have loaded the IlluminaHumanMethylationEPICmanifest and IlluminaHumanMethylationEPICanno.ilm10b2.hg19. I am following this "recipe": A cross-package Bioconductor workflow for analysing methylation array data. The read.metharray.exp() function works (and everything else down to the point of normalisation) and i manage to assign the right annotation. However, many downstream functions like qcReport(), preprocessQuantile(), preprocessNoob(), preprocessFunnorm() give me an error:

[preprocessQuantile] Mapping to genome.
Error in `assayDataElement<-`(`*tmp*`, "Meth", validate = FALSE, value = c(471,  :
  unused argument (validate = FALSE)
In addition: Warning message:
display list redraw incomplete 

I cannot seem to figure out what the problem is, i have also tried to use the 450k annotation as well but it still does not work. 

illuminahumanmethylationepicanno.ilmn10b.hg19 illuminahumanmethylationepicmanifest minfi limma 850k • 1.6k views
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> sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 8 (jessie)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8      
 [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0  
stringr_1.2.0   
DMRcate_1.10.8 
 [4] DMRcatedata_1.10.1  
DSS_2.14.0
bsseq_1.10.0
 [7] Gviz_1.18.2
missMethyl_1.8.0
RColorBrewer_1.1-2
[10] limma_3.30.13 
genefilter_1.48.1                                   
annotate_1.44.0                                    
[13] XML_3.98-1.5                                        
AnnotationDbi_1.36.2                                
shinyMethyl_1.10.0                                 
[16] IlluminaHumanMethylation450kmanifest_0.4.0          
shiny_1.0.0                                         
FlowSorted.Blood.450k_1.12.0                       
[19] mclust_5.2.2                                        
zlibbioc_1.12.0                                     
matrixStats_0.51.0                                 
[22] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0 IlluminaHumanMethylationEPICmanifest_0.3.0
BiocInstaller_1.24.0                               
[25] minfi_1.20.2
bumphunter_1.14.0
locfit_1.5-9.1            
[28] iterators_1.0.8
foreach_1.4.3
Biostrings_2.42.1                                  
[31] XVector_0.14.0
SummarizedExperiment_1.4.0
GenomicRanges_1.26.3                               
[34] GenomeInfoDb_1.10.3
IRanges_2.8.1
S4Vectors_0.12.1                                   
[37] Biobase_2.26.0
BiocGenerics_0.20.0                                

loaded via a namespace (and not attached):
 [1] colorspace_1.3-2              siggenes_1.48.0               biovizBase_1.22.0             htmlTable_1.9                 base64enc_0.1-3               dichromat_2.0-0              
 [7] base64_2.0                    interactiveDisplayBase_1.12.0 R.methodsS3_1.7.1             codetools_0.2-15              methylumi_2.20.0              knitr_1.15.1                 
[13] Formula_1.2-1                 Rsamtools_1.26.1              cluster_2.0.5                 GO.db_3.4.0                   R.oo_1.21.0                   httr_1.2.1                   
[19] backports_1.0.5               assertthat_0.1                Matrix_1.2-7.1                lazyeval_0.2.0                acepack_1.4.1                 htmltools_0.3.5              
[25] tools_3.3.3                   gtable_0.2.0                  doRNG_1.6                     Rcpp_0.12.9                   multtest_2.30.0               preprocessCore_1.36.0        
[31] nlme_3.1-128                  rtracklayer_1.34.2            mime_0.5                      ensembldb_1.6.2               gtools_3.5.0                  rngtools_1.2.4               
[37] statmod_1.4.29                beanplot_1.2                  org.Hs.eg.db_3.4.0            AnnotationHub_2.6.4           MASS_7.3-45                   scales_0.4.1                 
[43] BSgenome_1.42.0               VariantAnnotation_1.20.2      GEOquery_2.40.0               yaml_2.1.14                   memoise_1.0.0                 gridExtra_2.2.1              
[49] ggplot2_2.2.1                 pkgmaker_0.22                 biomaRt_2.30.0                rpart_4.1-10                  reshape_0.8.6                 latticeExtra_0.6-28          
[55] stringi_1.1.2                 RSQLite_1.1-2                 permute_0.9-4                 checkmate_1.8.2               GenomicFeatures_1.26.3        BiocParallel_1.8.1           
[61] bitops_1.0-6                  nor1mix_1.2-2                 lattice_0.20-34               ruv_0.9.6                     GenomicAlignments_1.10.0      htmlwidgets_0.8              
[67] plyr_1.8.4                    magrittr_1.5                  R6_2.2.0                      Hmisc_4.0-2                   DBI_0.6                       foreign_0.8-67               
[73] survival_2.40-1               RCurl_1.95-4.8                nnet_7.3-12                   tibble_1.2                    data.table_1.10.4             digest_0.6.12                
[79] xtable_1.8-2                  httpuv_1.3.3                  R.utils_2.5.0                 illuminaio_0.16.0             openssl_0.9.6                 munsell_0.4.3                
[85] registry_0.3                  BiasedUrn_1.07                quadprog_1.5-5 
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It would also be helpful if you would show the code you used up to that point.

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