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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