OpenCyto - getting the gates correct
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Last seen 5.6 years ago

Hello Everyone,

I am trying to construct a GatingSet and apply it to a few samples. I have difficulties getting the gates to end where they should. In particular I often find the CD8+ gate being utterly wrong (see the attached bitmap). Is there any way I can modify things so the gates end up in a more correct place?

I would also like to gate on CD45, but so far the only thing I can get working is `mindensity` and I am not happy with the result. I have tried `flowClust.2d` but it dies on me ( For the CD45 gate, For now I would like to eventually capture lymphocytes and monocytes in one gate, but later I would also like to look at the eosinophils/neutrophils as well as the basophils and rectangular gates are not aproppriate. Any suggestions on the gates I can use?

I would like to do it all data driven.

The data is from a Beckman Coulter Navios.

Thanks for any suggestions.

Best wishes,


gs <- GatingSet(panel.set.norm)

# Get singlets
thisData <- getData(gs)
singlet_gate <- fsApply(thisData, function(fr){
  openCyto:::.singletGate(fr, channel = c("FS INT LIN", "FS PEAK LIN"))
add(gs, singlet_gate, parent = "root", name = "singlets")

# Get CD45pos
# Rm("cd45.pos", gs)
thisData <- getData(gs, "singlets")
cd45pos_gate <- fsApply(thisData, function(fr){
  openCyto::mindensity(fr, channel = c("FL10 INT LOG"))
  # openCyto::flowClust.2d(fr, xChannel = "FL10 INT LOG", yChannel = "SS INT LIN")
#   sqrcut <- matrix(c(4.5, 0, 4.5, 280, 5.3, 400, 5.6, 600, 7.4, 600, 7.4, 200, 6.5, 0), ncol = 2, byrow = TRUE)
#   colnames(sqrcut) <- c("FL10 INT LOG", "SS INT LIN")
  # polygonGate(boundaries = sqrcut)#"FL6 INT LOG"=c(0, 4), "FL7 INT LOG"=c(6, 9), filterId = "cd8.pos")
add(gs, cd45pos_gate, parent = "singlets", name = "cd45.pos")

# Get CD3pos
thisData <- getData(gs, "cd45.pos")
cd3pos_gate <- fsApply(thisData, function(fr){
  openCyto::mindensity(fr, channel = c("FL8 INT LOG"))
add(gs, cd3pos_gate, parent = "cd45.pos", name = "cd3.pos")

# CD4 CD8
thisData <- getData(gs, "cd3.pos")
cd4cd8_gate <- fsApply(thisData, function(fr){
                         channel = c("FL6 INT LOG", "FL7 INT LOG"),
                         gFunc = "mindensity")
add(gs, cd4cd8_gate, parent = "cd3.pos", name = c("cd8+", "cd4+_cd8+", "cd4+", "cd4-_cd8-"))

> sessionInfo()
R version 3.2.5 (2016-04-14)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 15.10

 [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C               LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8     LC_MONETARY=de_DE.UTF-8   
 [6] LC_MESSAGES=de_DE.UTF-8    LC_PAPER=de_DE.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            

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

other attached packages:
[1] openCyto_1.8.4            flowWorkspace_3.16.15     gridExtra_2.2.1           ncdfFlow_2.16.1           BH_1.60.0-1               RcppArmadillo_0.6.600.4.0
[7] flowViz_1.34.1            lattice_0.20-33           flowCore_1.36.9          

loaded via a namespace (and not attached):
 [1] nlme_3.1-127          mcmc_0.9-4            matrixStats_0.50.1    pbkrtest_0.4-6        RColorBrewer_1.1-2    Rgraphviz_2.14.0      tools_3.2.5          
 [8] R6_2.1.2              KernSmooth_2.23-15    DBI_0.3.1             BiocGenerics_0.16.1   mgcv_1.8-12           colorspace_1.2-6      nnet_7.3-12          
[15] sp_1.2-2              GGally_1.0.1          chron_2.3-47          sgeostat_1.0-27       graph_1.48.0          quantreg_5.21         Biobase_2.30.0       
[22] flowClust_3.8.0       SparseM_1.7           sROC_0.1-2            flowStats_3.28.1      scales_0.4.0          lmtest_0.9-34         DEoptimR_1.0-4       
[29] hexbin_1.27.1         mvtnorm_1.0-5         robustbase_0.92-5     RBGL_1.46.0           stringr_1.0.0         multicool_0.1-9       minqa_1.2.4          
[36] R.utils_2.2.0         MCMCpack_1.3-5        rrcov_1.3-11          lme4_1.1-11           zoo_1.7-12            jsonlite_0.9.19       gtools_3.5.0         
[43] dplyr_0.4.3           car_2.1-2             R.oo_1.20.0           magrittr_1.5          Matrix_1.2-5          Rcpp_0.12.4           munsell_0.4.3        
[50] R.methodsS3_1.7.1     yaml_2.1.13           stringi_1.0-1         cvTools_0.3.2         MASS_7.3-45           zlibbioc_1.16.0       plyr_1.8.3           
[57] grid_3.2.5            misc3d_0.8-4          parallel_3.2.5        pls_2.5-0             splines_3.2.5         knitr_1.12.3          RUnit_0.4.31         
[64] fda_2.4.4             corpcor_1.6.8         codetools_0.2-14      stats4_3.2.5          XML_3.98-1.4          latticeExtra_0.6-28   data.table_1.9.6     
[71] vcd_1.4-1             nloptr_1.0.4          MatrixModels_0.4-1    VIM_4.4.1             gtable_0.2.0          clue_0.3-51           reshape_0.8.5        
[78] assertthat_0.1        ks_1.10.2             ggplot2_2.1.0         e1071_1.6-7           coda_0.18-1           flowUtils_1.34.0      class_7.3-14         
[85] IDPmisc_1.1.17        pcaPP_1.9-60          robCompositions_2.0.0 mvoutlier_2.0.6       cluster_2.0.4         rgl_0.95.1441   
opencyto gating • 978 views

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