[BioC] Analysing grouped datasets in repitools

Andrew Beggs [guest] guest at bioconductor.org
Mon Mar 4 16:48:30 CET 2013


I have some Affymetrix tiling arrays from a meDIP (i.e. consisting of a _me and _in files for each sample) experiment I have successfully managed to import into Aroma/repitools.

I would like to analyse them as a between group comparison, i.e. I would like to compare differential methylation between the two groups and come up with a top list of differentially methylated probes, with levels of significance. Normally I would do this with limma, but I can't see how this would fit into the data structures produced as part of repitools.

Could anyone possibly give me some pointers?



 -- output of sessionInfo(): 

R version 2.15.2 (2012-10-26)
Platform: x86_64-pc-linux-gnu (64-bit)

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

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

other attached packages:
 [1] TxDb.Hsapiens.UCSC.hg19.knownGene_2.8.0
 [2] BiocInstaller_1.8.3                    
 [3] biomaRt_2.14.0                         
 [4] preprocessCore_1.20.0                  
 [5] limma_3.14.4                           
 [6] GenomicFeatures_1.10.2                 
 [7] AnnotationDbi_1.20.5                   
 [8] Biobase_2.18.0                         
 [9] gsmoothr_0.1.5                         
[10] Repitools_1.4.0                        
[11] GenomicRanges_1.10.7                   
[12] BiocGenerics_0.4.0                     
[13] aroma.affymetrix_2.8.0                 
[14] affxparser_1.30.2                      
[15] aroma.apd_0.2.3                        
[16] R.huge_0.4.1                           
[17] aroma.light_1.28.0                     
[18] aroma.core_2.8.0                       
[19] matrixStats_0.6.2                      
[20] R.rsp_0.8.2                            
[21] R.devices_2.1.3                        
[22] R.cache_0.6.5                          
[23] R.filesets_2.0.0                       
[24] R.utils_1.19.5                         
[25] R.oo_1.11.7                            
[26] affy_1.36.1                            
[27] IRanges_1.16.6                         
[28] R.methodsS3_1.4.2                      

loaded via a namespace (and not attached):
 [1] affyio_1.26.0      Biostrings_2.26.3  bitops_1.0-5       BSgenome_1.26.1   
 [5] DBI_0.2-5          digest_0.6.3       edgeR_3.0.8        parallel_2.15.2   
 [9] PSCBS_0.30.0       RCurl_1.95-3       Rsamtools_1.10.2   RSQLite_0.11.2    
[13] rtracklayer_1.18.2 stats4_2.15.2      tools_2.15.2       XML_3.95-0.1      
[17] zlibbioc_1.4.0    

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