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 (https://support.bioconductor.org/p/81247/). 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,
Ulrik
library(openCyto)
gs <- GatingSet(panel.set.norm)
# Get singlets
thisData <- getData(gs)
singlet_gate <- fsApply(thisData, function(fr){
print(keyword(fr)$`$FIL`)
openCyto:::.singletGate(fr, channel = c("FS INT LIN", "FS PEAK LIN"))
})
add(gs, singlet_gate, parent = "root", name = "singlets")
recompute(gs)
# 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")
recompute(gs)
# Get CD3pos
thisData <- getData(gs, "cd45.pos")
cd3pos_gate <- fsApply(thisData, function(fr){
print(keyword(fr)$`$FIL`)
openCyto::mindensity(fr, channel = c("FL8 INT LOG"))
})
add(gs, cd3pos_gate, parent = "cd45.pos", name = "cd3.pos")
recompute(gs)
# CD4 CD8
thisData <- getData(gs, "cd3.pos")
cd4cd8_gate <- fsApply(thisData, function(fr){
print(keyword(fr)$`$FIL`)
openCyto::quadGate.seq(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-"))
recompute(gs)
> sessionInfo()
R version 3.2.5 (2016-04-14)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 15.10
locale:
[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
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=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
