Hello, I am following the CytofWorkFlow, and reading fcs files using the read.flowSet function from the flowCore package. It seems to me that I am getting the same values when I use transformation="linearize" and tranformation = NULL

Is it possible that the values are transformed also when using transformation = NULL?

When using the CyTOF workflow (https://www.bioconductor.org/packages/release/workflows/vignettes/cytofWorkflow/inst/doc/cytofWorkflow.html) is it recommended to transform the data using "linearize" (later it is transformed using arcsinh transformation).

Thank you

 sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Ubuntu 18.04 LTS

Matrix products: default
BLAS/LAPACK: /gpfs_apps/ubu18-86_64/src/SysApps/anaconda/anaconda-py3.8/lib/libopenblasp-r0.3.7.so

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

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

other attached packages:
[1] flowCore_2.0.1

loaded via a namespace (and not attached):
  [1] ggbeeswarm_0.6.0            TH.data_1.0-10              Rtsne_0.15                  colorspace_1.4-1           
  [5] rjson_0.2.20                ellipsis_0.3.1              rio_0.5.16                  ggridges_0.5.2             
  [9] circlize_0.4.10             cytolib_2.0.3               XVector_0.28.0              GenomicRanges_1.40.0       
 [13] GlobalOptions_0.1.2         base64enc_0.1-3             BiocNeighbors_1.6.0         clue_0.3-57                
 [17] rstudioapi_0.11             hexbin_1.28.1               CytoML_2.0.5                ggrepel_0.8.2              
 [21] fansi_0.4.1                 mvtnorm_1.1-1               xml2_1.3.2                  codetools_0.2-16           
 [25] splines_4.0.2               scater_1.16.2               jsonlite_1.7.1              cluster_2.1.0              
 [29] png_0.1-7                   graph_1.66.0                compiler_4.0.2              drc_3.0-1                  
 [33] assertthat_0.2.1            Matrix_1.2-18               cli_2.1.0                   BiocSingular_1.4.0         
 [37] tools_4.0.2                 ncdfFlow_2.34.0             rsvd_1.0.3                  igraph_1.2.6               
 [41] gtable_0.3.0                glue_1.4.2                  GenomeInfoDbData_1.2.3      flowWorkspace_4.0.6        
 [45] reshape2_1.4.4              dplyr_1.0.2                 ggcyto_1.16.0               Rcpp_1.0.5                 
 [49] carData_3.0-4               Biobase_2.48.0              cellranger_1.1.0            vctrs_0.3.4                
 [53] DelayedMatrixStats_1.10.1   stringr_1.4.0               openxlsx_4.2.2              irlba_2.3.3                
 [57] lifecycle_0.2.0             gtools_3.8.2                XML_3.99-0.5                zlibbioc_1.34.0            
 [61] MASS_7.3-53                 zoo_1.8-8                   scales_1.1.1                RProtoBufLib_2.0.0         
 [65] hms_0.5.3                   parallel_4.0.2              SummarizedExperiment_1.18.2 RBGL_1.64.0                
 [69] sandwich_3.0-0              RColorBrewer_1.1-2          SingleCellExperiment_1.10.1 ComplexHeatmap_2.4.3       
 [73] yaml_2.2.1                  curl_4.3                    gridExtra_2.3               ggplot2_3.3.2              
 [77] latticeExtra_0.6-29         stringi_1.5.3               S4Vectors_0.26.1            plotrix_3.7-8              
 [81] BiocGenerics_0.34.0         zip_2.1.1                   BiocParallel_1.22.0         shape_1.4.5                
 [85] GenomeInfoDb_1.24.2         rlang_0.4.8                 pkgconfig_2.0.3             matrixStats_0.57.0         
 [89] bitops_1.0-6                lattice_0.20-41             purrr_0.3.4                 cowplot_1.1.0              
 [93] tidyselect_1.1.0            plyr_1.8.6                  magrittr_1.5                R6_2.4.1                   
 [97] IRanges_2.22.2              generics_0.0.2              nnls_1.4                    multcomp_1.4-14            
[101] DelayedArray_0.14.1         pillar_1.4.6                haven_2.3.1                 foreign_0.8-80             
[105] survival_3.2-7              abind_1.4-5                 RCurl_1.98-1.2              FlowSOM_1.20.0             
[109] tibble_3.0.4                tsne_0.1-3                  crayon_1.3.4                car_3.0-10                 
[113] viridis_0.5.1               jpeg_0.1-8.1                GetoptLong_1.0.3            grid_4.0.2                 
[117] readxl_1.3.1                CATALYST_1.12.2             data.table_1.13.0           Rgraphviz_2.32.0           
[121] ConsensusClusterPlus_1.52.0 forcats_0.5.0               digest_0.6.25               RcppParallel_5.0.2         
[125] stats4_4.0.2                munsell_0.5.0               viridisLite_0.3.0           beeswarm_0.2.3             
[129] vipor_0.4.5