I am using Minfi to process data from Illumina EPIC methylation beadChips and I have found a discrepancy between the number of CpGs that GenomeStudio reports with a detection P value <0.01 and the number that I get reading in the .idat files using Minfi in R.

For example, in a recent batch I had one sample with only 801,850 CpGs with detection P values <0.01 (92.5%) according to GenomeStudio, but when I read in the data from the idat files and used the detectionP() function in Minfi, I got a count of 842,051 CpGs with detection P <0.01 (97.1%). 

Is there an explanation for the discrepancy? This example has a pretty extreme difference, but the number of good CpGs I get when I read from the idat files directly is consistently higher than what comes out of GenomeStudio.

Example code for getting the count of good detection P values from Minfi and sessionInfo is below.

RGSet <- read.metharray.exp(targets = targets)
detP <- detectionP(RGSet)
dim(detP)
# [1] 866836    137
sum(detP[,"200861170017_R06C01"]<0.01)
# [1] 842051

​

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Fedora 23 (Server Edition)

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

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

other attached packages:
 [1] Gviz_1.17.4                minfi_1.20.2               bumphunter_1.14.0         
 [4] locfit_1.5-9.1             iterators_1.0.8            foreach_1.4.3             
 [7] Biostrings_2.42.1          XVector_0.13.7             SummarizedExperiment_1.4.0
[10] GenomicRanges_1.26.4       GenomeInfoDb_1.10.3        IRanges_2.8.2             
[13] S4Vectors_0.12.2           Biobase_2.34.0             BiocGenerics_0.19.2       

loaded via a namespace (and not attached):
 [1] nlme_3.1-131                  bitops_1.0-6                 
 [3] matrixStats_0.52.1            RColorBrewer_1.1-2           
 [5] httr_1.2.1                    tools_3.3.0                  
 [7] backports_1.0.5               doRNG_1.6                    
 [9] nor1mix_1.2-2                 R6_2.2.0                     
[11] rpart_4.1-10                  Hmisc_4.0-2                  
[13] DBI_0.6-1                     lazyeval_0.2.0               
[15] colorspace_1.3-2              nnet_7.3-12                  
[17] gridExtra_2.2.1               base64_2.0                   
[19] preprocessCore_1.36.0         htmlTable_1.9                
[21] pkgmaker_0.22                 rtracklayer_1.34.2           
[23] scales_0.4.1                  checkmate_1.8.2              
[25] genefilter_1.56.0             quadprog_1.5-5               
[27] stringr_1.2.0                 digest_0.6.12                
[29] Rsamtools_1.26.1              foreign_0.8-67               
[31] illuminaio_0.16.0             siggenes_1.48.0              
[33] GEOquery_2.40.0               base64enc_0.1-3              
[35] dichromat_2.0-0               htmltools_0.3.5              
[37] BSgenome_1.41.2               ensembldb_1.5.9              
[39] limma_3.30.13                 htmlwidgets_0.8              
[41] RSQLite_1.1-2                 BiocInstaller_1.24.0         
[43] shiny_1.0.1                   mclust_5.2.3                 
[45] BiocParallel_1.8.1            acepack_1.4.1                
[47] VariantAnnotation_1.19.7      RCurl_1.95-4.8               
[49] magrittr_1.5                  Formula_1.2-1                
[51] Matrix_1.2-8                  Rcpp_0.12.10                 
[53] munsell_0.4.3                 stringi_1.1.3                
[55] MASS_7.3-45                   zlibbioc_1.20.0              
[57] plyr_1.8.4                    AnnotationHub_2.5.4          
[59] lattice_0.20-35               splines_3.3.0                
[61] multtest_2.30.0               GenomicFeatures_1.26.4       
[63] annotate_1.52.1               knitr_1.15.1                 
[65] beanplot_1.2                  rngtools_1.2.4               
[67] codetools_0.2-15              biomaRt_2.30.0               
[69] XML_3.98-1.6                  biovizBase_1.22.0            
[71] latticeExtra_0.6-28           data.table_1.10.4            
[73] httpuv_1.3.3                  gtable_0.2.0                 
[75] openssl_0.9.6                 reshape_0.8.6                
[77] assertthat_0.1                ggplot2_2.2.1                
[79] mime_0.5                      xtable_1.8-2                 
[81] survival_2.41-3               tibble_1.2                   
[83] GenomicAlignments_1.10.1      AnnotationDbi_1.36.2         
[85] registry_0.3                  memoise_1.0.0                
[87] cluster_2.0.6                 interactiveDisplayBase_1.12.0
> packageVersion("minfi")
[1] '1.20.2'

The obvious answer is that they use different algorithms to determine which probes are actually measuring something, and apparently BeadStudio is a bit more conservative. However it is probably not possible to say much more than that because BeadStudio's code isn't (AFAIK) available for you to peruse.

It's easy to know what minfi is doing, because it's Open Source, so you can just look at the code, or read the help page or the paper(s) describing how minfi works (or probably all of those things). So you can know exactly how minfi determines if a given probe is detecting anything above background.

It's a bit harder to know what BeadStudio is doing. Even if they have a whitepaper or a description somewhere, it is often impossible to know exactly what they are doing under the hood, because a general description (even if quite detailed) is not likely to be as descriptive as the underlying code would be.