How to get methylation data in count data format from "Infinium MethylationEPIC BeadChips (Illumina)" idat files
0
0
Entering edit mode
Jojo • 0
@edf159b6
Last seen 2.7 years ago
Germany

I have to analyse BeadChip Methylation Data. Since I don't have replicates in my experiment, I'm thinking of using the package 'DSS' for the analysis. This package takes data in count format for each CG position: chromosome number, genomic coordinate, total number of reads, and number of reads showing methylation, like:

chr     pos     N       X
chr18   3014904 26      2
chr18   3031032 33      12
chr18   3031044 33      13
chr18   3031065 48      24

I could read the Illumina .idat files using the library 'illuminaio', which gives this result.

> library(illuminaio)
> idat <- readIDAT("205715840012_R01C01_Grn.idat")

> names(idat)
 [1] "fileSize"      "versionNumber" "nFields"       "fields"        "nSNPsRead"     "Quants"        "MidBlock"     
 [8] "RedGreen"      "Barcode"       "ChipType"      "RunInfo"       "Unknowns"     

> idat$Quants[1:5,]
        Mean  SD NBeads
1600101 8827 870     20
1600111 2972 355     16
1600115 2550 484     16
1600123 1266 221     12
1600131  180  94     19

Now, I do not know how to covert this information to the above 'count data' information with chr, pos, N, X. Any help would be appreciated.

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_India.1252  LC_CTYPE=English_India.1252    LC_MONETARY=English_India.1252 LC_NUMERIC=C                  
[5] LC_TIME=English_India.1252    

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

other attached packages:
 [1] IlluminaDataTestFiles_1.30.0 illuminaio_0.34.0            DSS_2.40.0                   bsseq_1.28.0                
 [5] SummarizedExperiment_1.22.0  MatrixGenerics_1.4.3         matrixStats_0.61.0           GenomicRanges_1.44.0        
 [9] GenomeInfoDb_1.28.4          IRanges_2.26.0               S4Vectors_0.30.2             BiocParallel_1.26.2         
[13] Biobase_2.52.0               BiocGenerics_0.38.0         

loaded via a namespace (and not attached):
 [1] base64_2.0                Rcpp_1.0.8                locfit_1.5-9.4            lattice_0.20-44           Rsamtools_2.8.0          
 [6] Biostrings_2.60.2         gtools_3.9.2              digest_0.6.29             R6_2.5.1                  evaluate_0.14            
[11] sparseMatrixStats_1.4.2   zlibbioc_1.38.0           rlang_1.0.1               rstudioapi_0.13           data.table_1.14.2        
[16] jquerylib_0.1.4           R.utils_2.11.0            R.oo_1.24.0               Matrix_1.4-0              rmarkdown_2.11           
[21] splines_4.1.0             stringr_1.4.0             RCurl_1.98-1.5            munsell_0.5.0             DelayedArray_0.18.0      
[26] HDF5Array_1.20.0          compiler_4.1.0            rtracklayer_1.52.1        xfun_0.29                 askpass_1.1              
[31] htmltools_0.5.2           openssl_1.4.6             GenomeInfoDbData_1.2.6    XML_3.99-0.8              permute_0.9-7            
[36] crayon_1.4.2              GenomicAlignments_1.28.0  bitops_1.0-7              rhdf5filters_1.4.0        R.methodsS3_1.8.1        
[41] grid_4.1.0                jsonlite_1.7.3            lifecycle_1.0.1           magrittr_2.0.2            scales_1.1.1             
[46] stringi_1.7.6             cli_3.1.1                 XVector_0.32.0            limma_3.48.3              bslib_0.3.1              
[51] DelayedMatrixStats_1.14.3 Rhdf5lib_1.14.2           rjson_0.2.21              restfulr_0.0.13           tools_4.1.0              
[56] BSgenome_1.60.0           fastmap_1.1.0             yaml_2.2.2                colorspace_2.0-2          rhdf5_2.36.0             
[61] BiocManager_1.30.16       knitr_1.37                sass_0.4.0                BiocIO_1.2.0
methylationArrayAnalysis illuminaio beadarray DSS bsseqData • 1.5k views
ADD COMMENT
1
Entering edit mode

I don't think DSS is designed for array data but rather to analyse BS-seq data

ADD REPLY
0
Entering edit mode

Yes, I read that, but I thought it might be possible to convert the information to BS-seq format somehow.

ADD REPLY
1
Entering edit mode

Since methylation arrays rely on fluorescence signals data and not sequencing data I think it is a non-sense. There are plenty of packages specifically designed for methylation array analysis : minfi, ChAMPare among the most popular

ADD REPLY
0
Entering edit mode

I have been using ChAMP for such analyses, but as far as I know, ChAMP doesn't give a way for a 'no-replicate' situation. Therefore, I tried to move to DSS. But I understand your point. Thanks!

ADD REPLY

Login before adding your answer.

Traffic: 280 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6