Hi Bioconductor Community -
I run multiple experiments on a flow cytometer at the same time in multiple 96-well plates. I gate the data in FlowJo and then want to further analyze each experiment individually in R. My problem is that I am unable to grab just the subset of samples from a particular experiment when generating a gating set using the CytoML::flowjo_to_gatingset() function.
For example, if I have a flow cytometry run with four A1 Well_001.fcs samples (keyword = $FIL), and I only am interested in analyzing one of those samples, the documentation for the function suggests I should be able to add a parameter such as the '$TOT' keyword to generate unique sample identifier to help subset, but I can't get this to work.
Here is my process. I first load the FlowJo workspace into R:
>library(CytoML)
>
>wsfile <- flowjo_workspace.wsp # path flowjo workspace file
>
>ws <- open_flowjo_xml(wsfile)
>ws
Groups in Workspace
Name Num.Samples
1 All Samples 264
2 Panel 10 Plate 1 72
3 Panel 10 Plate 2 72
4 Panel 10 Plate 3 72
5 Panel 10 Plate 4 48
>fj_ws_get_samples(ws)
sampleID name count pop.counts
1 8 A1 Well_001.fcs 1024 27
2 83 A1 Well_001.fcs 11096 27
3 155 A1 Well_001.fcs 5040 27
4 224 A1 Well_001.fcs 181896 27
5 9 A2 Well_002.fcs 22984 27
6 84 A2 Well_002.fcs 11120 27
7 156 A2 Well_002.fcs 9072 27
8 225 A2 Well_002.fcs 144856 27
9 10 A3 Well_003.fcs 22856 27
10 85 A3 Well_003.fcs 17040 27
11 157 A3 Well_003.fcs 14872 27
12 226 A3 Well_003.fcs 153888 27
13 11 A4 Well_004.fcs 44040 27
The samples in each group have the same $FIL names: A1 Well_001.fcs, A2 Well_002, A3 Well_003.fcs, etc, with group 5 having fewer samples. I omitted many rows from the fj_ws_get_samples(ws) printout.
I then only want to create a gating set from sampleIDs 4 and 13 from the above output (they belong to different workspace groups). I think I should store their $TOT values in a vector to pass to the function, but the gating set (gs) contains 8 samples, not the expected 2 samples: "A1 Well_001.fcs_181896" and "A3 Well_003.fcs_17040".
>total <- c("181896", "17040")
>
>gs <- flowjo_to_gatingset(ws,
> name = "All Samples",
> subset = `$TOT` %in% total,
> keywords = "$TOT")
>
>sampleNames(gs)
[1] "A1 Well_001.fcs_1024" "A1 Well_001.fcs_11096" "A1 Well_001.fcs_5040" "A1 Well_001.fcs_181896"
[5] "A3 Well_003.fcs_22856" "A3 Well_003.fcs_17040" "A3 Well_003.fcs_14872" "A3 Well_003.fcs_153888"
I have also tried something more explicit with no luck:
>total <- c("A1 Well_001.fcs_181896", "A3 Well_003.fcs_17040")
>
>gs <- flowjo_to_gatingset(ws,
> name = "All Samples",
> subset = paste(`$FIL`, `$TOT`, sep="_") %in% total,
> keywords = c("$FIL","$TOT"))
>
>sampleNames(gs)
[1] "A1 Well_001.fcs_1024" "A1 Well_001.fcs_11096" "A1 Well_001.fcs_5040" "A1 Well_001.fcs_181896"
[5] "A3 Well_003.fcs_22856" "A3 Well_003.fcs_17040" "A3 Well_003.fcs_14872" "A3 Well_003.fcs_153888"
The gating set contains all of the "A1 Well_001.fcs" and "A3 Well_003.fcs" files, and I can't figure out how to just subset the two files of interest. I've also played around with the additional.keys, additional.sampleID, and greedy_match parameters with no luck.
Any help would be much appreciated!
>sessionInfo( )
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] plyr_1.8.7 forcats_0.5.1 ggpubr_0.4.0 kableExtra_1.3.4
[5] ggprism_1.0.3 gridExtra_2.3 ggcyto_1.22.0 flowWorkspace_4.6.0
[9] ncdfFlow_2.40.0 BH_1.78.0-0 RcppArmadillo_0.11.0.0.0 flowCore_2.6.0
[13] assertr_2.8 CytoML_2.6.0 humarrow_0.0.0.9000 ggplot2_3.3.5
[17] dplyr_1.0.8 readr_2.1.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 ggsignif_0.6.3 ellipsis_0.3.2 rprojroot_2.0.3 cytolib_2.6.2 base64enc_0.1-3
[7] fs_1.5.2 rstudioapi_0.13 hexbin_1.28.2 remotes_2.4.2 bit64_4.0.5 fansi_1.0.3
[13] xml2_1.3.3 cachem_1.0.6 knitr_1.39 pkgload_1.2.4 jsonlite_1.8.0 broom_0.8.0
[19] png_0.1-7 graph_1.72.0 BiocManager_1.30.17 compiler_4.1.0 httr_1.4.2 backports_1.4.1
[25] Matrix_1.3-3 fastmap_1.1.0 cli_3.2.0 htmltools_0.5.2 prettyunits_1.1.1 tools_4.1.0
[31] gtable_0.3.0 glue_1.6.2 rappdirs_0.3.3 Rcpp_1.0.8.3 carData_3.0-5 Biobase_2.54.0
[37] jquerylib_0.1.4 cellranger_1.1.0 vctrs_0.4.1 svglite_2.1.0 xfun_0.30 stringr_1.4.0
[43] ps_1.7.0 brio_1.1.3 testthat_3.1.3 rvest_1.0.2 lifecycle_1.0.1 devtools_2.4.3
[49] rstatix_0.7.0 XML_3.99-0.9 zlibbioc_1.40.0 scales_1.2.0 vroom_1.5.7 RProtoBufLib_2.6.0
[55] hms_1.1.1 parallel_4.1.0 RBGL_1.70.0 RColorBrewer_1.1-3 yaml_2.3.5 curl_4.3.2
[61] reticulate_1.24 memoise_2.0.1 aws.signature_0.6.0 sass_0.4.1 latticeExtra_0.6-29 stringi_1.7.6
[67] highr_0.9 S4Vectors_0.32.4 desc_1.4.1 BiocGenerics_0.40.0 pkgbuild_1.3.1 rlang_1.0.2
[73] pkgconfig_2.0.3 systemfonts_1.0.4 matrixStats_0.62.0 evaluate_0.15 lattice_0.20-44 purrr_0.3.4
[79] bit_4.0.4 tidyselect_1.1.2 processx_3.5.3 magrittr_2.0.3 R6_2.5.1 generics_0.1.2
[85] pillar_1.7.0 withr_2.5.0 abind_1.4-5 tibble_3.1.6 crayon_1.5.1 car_3.0-12
[91] utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.14 aws.s3_0.3.21 jpeg_0.1-9 usethis_2.1.5
[97] readxl_1.4.0 data.table_1.14.2 callr_3.7.0 Rgraphviz_2.38.0 digest_0.6.29 webshot_0.5.3
[103] tidyr_1.2.0 RcppParallel_5.1.5 stats4_4.1.0 munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1
[109] sessioninfo_1.2.2
