Hi,

I am relatively new in R (hope is in the good section)

I am trying to run the function "estimatecellscount" from the minfi package on my RGset data. I have been able to import the targets and the RGset. The code that I am trying to execute is as follows:

est<-estimateCellCounts(rgset_pf)

"rgset_pf" is because it was after applying pfilter for the quality control

rgset_pf <- pfilter(rgset,pnthresh = 0.01)
`

Here's the error that is produced:

> est<-estimateCellCounts(rgset_pf)
[estimateCellCounts] Combining user data with reference (flow sorted) data.

[estimateCellCounts] Processing user and reference data together.

[preprocessQuantile] Mapping to genome.
Error: useNames = NA is defunct. Instead, specify either useNames = TRUE or useNames = FALSE.
In addition: Warning message:
In DataFrame(sampleNames = c(colnames(rgSet), colnames(referenceRGset)),  :
  'stringsAsFactors' is ignored

Any suggestions on how to fix this?

Best regards

:) Clara

> sessionInfo() 
R version 4.2.3 (2023-03-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.3

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
 [2] systemfonts_1.0.5                                 
 [3] mclust_6.0.1                                      
 [4] Matrix_1.6-5                                      
 [5] IlluminaHumanMethylation450kmanifest_0.4.0        
 [6] FlowSorted.Blood.450k_1.36.0                      
 [7] minfi_1.44.0                                      
 [8] bumphunter_1.40.0                                 
 [9] locfit_1.5-9.9                                    
[10] iterators_1.0.14                                  
[11] foreach_1.5.2                                     
[12] Biostrings_2.66.0                                 
[13] XVector_0.38.0                                    
[14] SummarizedExperiment_1.28.0                       
[15] Biobase_2.58.0                                    
[16] MatrixGenerics_1.10.0                             
[17] matrixStats_1.2.0                                 
[18] GenomicRanges_1.50.2                              
[19] GenomeInfoDb_1.34.9                               
[20] IRanges_2.32.0                                    
[21] S4Vectors_0.36.2                                  
[22] BiocGenerics_0.44.0                               

loaded via a namespace (and not attached):
  [1] rjson_0.2.21              siggenes_1.72.0           base64_2.0.1             
  [4] rstudioapi_0.15.0         bit64_4.0.5               AnnotationDbi_1.60.2     
  [7] fansi_1.0.6               xml2_1.3.6                codetools_0.2-19         
 [10] splines_4.2.3             sparseMatrixStats_1.10.0  cachem_1.0.8             
 [13] scrime_1.3.5              Rsamtools_2.14.0          annotate_1.76.0          
 [16] dbplyr_2.5.0              png_0.1-8                 HDF5Array_1.26.0         
 [19] BiocManager_1.30.22       readr_2.1.5               compiler_4.2.3           
 [22] httr_1.4.7                fastmap_1.1.1             limma_3.54.2             
 [25] cli_3.6.2                 prettyunits_1.2.0         tools_4.2.3              
 [28] glue_1.7.0                GenomeInfoDbData_1.2.9    dplyr_1.1.4              
 [31] rappdirs_0.3.3            doRNG_1.8.6               Rcpp_1.0.12              
 [34] vctrs_0.6.5               rhdf5filters_1.10.1       multtest_2.54.0          
 [37] preprocessCore_1.60.2     nlme_3.1-164              rtracklayer_1.58.0       
 [40] DelayedMatrixStats_1.20.0 stringr_1.5.1             lifecycle_1.0.4          
 [43] restfulr_0.0.15           rngtools_1.5.2            XML_3.99-0.16.1          
 [46] beanplot_1.3.1            zlibbioc_1.44.0           MASS_7.3-60.0.1          
 [49] hms_1.1.3                 rhdf5_2.42.1              GEOquery_2.66.0          
 [52] RColorBrewer_1.1-3        yaml_2.3.8                curl_5.2.1               
 [55] memoise_2.0.1             biomaRt_2.54.1            reshape_0.8.9            
 [58] stringi_1.8.3             RSQLite_2.3.5             genefilter_1.80.3        
 [61] BiocIO_1.8.0              GenomicFeatures_1.50.4    filelock_1.0.3           
 [64] BiocParallel_1.32.6       rlang_1.1.3               pkgconfig_2.0.3          
 [67] bitops_1.0-7              nor1mix_1.3-2             lattice_0.22-6           
 [70] purrr_1.0.2               Rhdf5lib_1.20.0           GenomicAlignments_1.34.1 
 [73] bit_4.0.5                 tidyselect_1.2.1          plyr_1.8.9               
 [76] magrittr_2.0.3            R6_2.5.1                  generics_0.1.3           
 [79] DelayedArray_0.24.0       DBI_1.2.2                 pillar_1.9.0             
 [82] survival_3.5-8            KEGGREST_1.38.0           RCurl_1.98-1.14          
 [85] tibble_3.2.1              crayon_1.5.2              utf8_1.2.4               
 [88] BiocFileCache_2.6.1       tzdb_0.4.0                progress_1.2.3           
 [91] grid_4.2.3                data.table_1.15.2         blob_1.2.4               
 [94] digest_0.6.35             xtable_1.8-4              tidyr_1.3.1              
 [97] illuminaio_0.40.0         openssl_2.1.1             askpass_1.2.0            
[100] quadprog_1.5-8

Don't filter first. You are likely removing the CpGs that are used to estimate the cell counts.