Dear all, 

I have a question regarding the estimateCellCounts function in minfi and I would be very thankful for your help. 

I can read ist files in batches of each two files, so each time I call estimateCellCounts it has for two samples both red and green channel as RGset. If both samples are female, I get an error from the getSex function, even though I provided the correct sex in the format the preprocessQuantile requires when calling the function:

RGset <- read.metharray(filenames[i,], verbose=TRUE) 
RGset <- bgcorrect.illumina(RGset)  

est_wbc <- estimateCellCounts(RGset, compositeCellType = "Blood", cellTypes = c("CD8T","CD4T", "NK","Bcell","Mono","Gran"),sex = datsamples$MFGender[match(sample_names[i,],datsamples$Sentrix_Code)])
     #,referencePlatform=arraytype)

 In .getSex(CN = CN, xIndex = xIndex, yIndex = yIndex, cutoff = cutoff) :

  An inconsistency was encountered while determining sex. One possibility is that only one sex is present. We recommend further checks, for example with the plotSex function.
> print(sessionInfo())
R version 3.4.4 (2018-03-15)
Platform: both apple and linux (64-bit)
Running under: both apple and linux

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib

locale:
[1] de_DE.UTF-8/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8

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

other attached packages:
 [1] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
 [2] IlluminaHumanMethylation450kmanifest_0.4.0        
 [3] FlowSorted.Blood.450k_1.16.0                      
 [4] IlluminaHumanMethylationEPICmanifest_0.3.0        
 [5] ggplot2_2.2.1                                     
 [6] DescTools_0.99.24                                 
 [7] limma_3.34.9                                      
 [8] minfi_1.24.0                                      
 [9] bumphunter_1.20.0                                 
[10] locfit_1.5-9.1                                    
[11] iterators_1.0.9                                   
[12] foreach_1.4.4                                     
[13] Biostrings_2.46.0                                 
[14] XVector_0.18.0                                    
[15] SummarizedExperiment_1.8.1                        
[16] DelayedArray_0.4.1                                
[17] matrixStats_0.53.1                                
[18] Biobase_2.38.0                                    
[19] GenomicRanges_1.30.3                              
[20] GenomeInfoDb_1.14.0                               
[21] IRanges_2.12.0                                    
[22] S4Vectors_0.16.0                                  
[23] BiocGenerics_0.24.0                               
[24] openxlsx_4.1.0                                    

loaded via a namespace (and not attached):
 [1] nlme_3.1-137             bitops_1.0-6            
 [3] bit64_0.9-7              RColorBrewer_1.1-2      
 [5] progress_1.1.2           httr_1.3.1              
 [7] tools_3.4.4              doRNG_1.6.6             
 [9] nor1mix_1.2-3            R6_2.2.2                
[11] lazyeval_0.2.1           colorspace_1.3-2        
[13] DBI_1.0.0                withr_2.1.2             
[15] tidyselect_0.2.4         prettyunits_1.0.2       
[17] RMySQL_0.10.15           base64_2.0              
[19] bit_1.1-14               compiler_3.4.4          
[21] preprocessCore_1.40.0    expm_0.999-2            
[23] xml2_1.2.0               pkgmaker_0.27           
[25] rtracklayer_1.38.3       scales_0.5.0            
[27] mvtnorm_1.0-8            readr_1.1.1             
[29] genefilter_1.60.0        quadprog_1.5-5          
[31] stringr_1.3.1            digest_0.6.15           
[33] Rsamtools_1.30.0         foreign_0.8-70          
[35] illuminaio_0.20.0        siggenes_1.52.0         
[37] GEOquery_2.46.15         pkgconfig_2.0.1         
[39] manipulate_1.0.1         bibtex_0.4.2            
[41] rlang_0.2.1              RSQLite_2.1.1           
[43] bindr_0.1.1              mclust_5.4              
[45] BiocParallel_1.12.0      dplyr_0.7.5             
[47] zip_1.0.0                RCurl_1.95-4.10         
[49] magrittr_1.5             GenomeInfoDbData_1.0.0  
[51] Matrix_1.2-14            munsell_0.4.3           
[53] Rcpp_0.12.17             stringi_1.2.2           
[55] yaml_2.1.19              MASS_7.3-50             
[57] zlibbioc_1.24.0          plyr_1.8.4              
[59] grid_3.4.4               blob_1.1.1              
[61] lattice_0.20-35          splines_3.4.4           
[63] multtest_2.34.0          GenomicFeatures_1.30.3  
[65] annotate_1.56.2          hms_0.4.2               
[67] beanplot_1.2             pillar_1.2.3            
[69] boot_1.3-20              rngtools_1.3.1          
[71] codetools_0.2-15         biomaRt_2.34.2          
[73] XML_3.98-1.11            glue_1.2.0              
[75] data.table_1.11.4        gtable_0.2.0            
[77] openssl_1.0.1            purrr_0.2.5             
[79] tidyr_0.8.1              reshape_0.8.7           
[81] assertthat_0.2.0         xtable_1.8-2            
[83] survival_2.42-3          tibble_1.4.2            
[85] GenomicAlignments_1.14.2 AnnotationDbi_1.40.0    
[87] registry_0.5             memoise_1.1.0           
[89] bindrcpp_0.2.2 

So how can I be sure that the cell count estimation is based on the true sex? 

How can I stop the function from calling getSex? 

Many thanks for any help! 

Best, fgc