[BioC] flowStats quadrantGate and rangeGate Question

[Frederico Arnoldi]

Hello Aric,

If I understood your problem correctly, why dont you just select your data with values greater than 0 before using rangeGate ou quadrant? 

You could do it using, for example, rectangleGate from flowCore like this:

#Define gating values for each fluorochrome, in this case APC and FITC, with values from 0 to 15
rect_gate <- rectangleGate(filterId="my_ractangleGate", "APC-A"=c(0,15), "FITC-A"=c(0,15))
#Get this subset from your data 
rect_gated_data <- Subset(yourdata, rect_gate)

I hope I made myself clear and have helped you.

Good luck,
Frederico Arnoldi


Aric wrote:

Hello,

I am trying out the quadrant and rangeGate methods in flowStats with a small 
sample of data. It seems that they do not play well with asinh transformed 
data when there is a small, but significant, population below zero. I have 
tuned the adjustable parameters sd, alpha and borderQuant as much as 
possible, but with less than ideal results. What I am looking for I suppose 
is a way to tell the algorithm to ignore points below 0 when calculating the 
gates. 

For example, in the fluorochrome channels there are two distinct populations 
that have values above zero, and there is a small population with values 
below zero. For determining positive and negative gates, only the two 
populations that have values above zero should be considered. The values 
below zero should be lumped in with the non-zero but still negative numbers. 
Just wondering if there is a way to do this without having to manually 
program the gates. 

Thanks, Aric

wf <- workFlow(fs, name="2009_03_05 Asinh Workflow")
# remove boundry events before transformation as transform SSC
boundaryfilter <- boundaryFilter(filterId = "boundaryfilter",
                                 x = c("FSC.A", "SSC.A"))
add(wf, boundaryfilter)
singletfilter = polygonGate(`FSC.A` = 
c(10000,13000,20000,40000,60000,80000,100000,160000,200000,263000,263000, 
200000,160000,100000,80000,60000,40000,20000,14000,8000),
  `FSC.H` = 
c(5000,5000,10000,22000,34000,50000,65000,115000,150000,200000,220000,170000,130000,78000,62000,45000,30000,15000,10000,5000),
  filterId="Singlet")
add(wf, singletfilter, parent="boundaryfilter+")
asinhtf <- transformList(colnames(fs)[4:19],
                         asinh,
                         transformationId="asinh")
add(wf, asinhtf, parent="Singlet+")
lymphfilter <- lymphGate(Data(wf[["asinh"]]), channels=c("FSC.A", "SSC.A"), 
preselection="APC.H7.A", filterId="Lymphs", eval=FALSE, scale=2)
add(wf, lymphfilter$n2gate, parent="asinh")
pars <- colnames(Data(wf[["base view"]]))[c(10,11,12,13,18,19)] 
norm <- normalization(normFun = function(x, parameters, ...)
                      warpSet(x, parameters, ...),
                      parameters = pars,
                      normalizationId= "Warping")
add(wf, norm, parent="Lymphs+")
## absolute=T & borderQuant=0.9 help with CD3, but not CD4
qgate <- quadrantGate(Data(wf[["Warping"]]),
                      stains=c("AmCyan.A", "APC.H7.A"),
                      plot=FALSE,
                      filterId="CD3CD4",
                      absolute=TRUE,
                      borderQuant=0.8,
                      alpha=0.9,
                      sd=0.1)
add(wf, qgate, parent="Warping")
## can't get much better than this without removing negative numbers
qgateLive <- quadrantGate(Data(wf[["Warping"]]),
                      stains=c("Indo.1..Violet..A", "APC.H7.A"),
                      plot=TRUE,
                      filterId="LiveCD4",
                      absolute=TRUE,
                      borderQuant=1, #0 is wrong direction
                      alpha=1,#0.9
                      sd=0)

> sessionInfo()
R version 2.11.0 (2010-04-22) 
amd64-portbld-freebsd8.0 

locale:
[1] C

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

other attached packages:
 [1] flowStats_1.6.0     cluster_1.12.3      fda_2.2.1          
 [4] zoo_1.6-3           flowQ_1.8.0         latticeExtra_0.6-11
 [7] RColorBrewer_1.0-2  parody_1.6.0        bioDist_1.20.0     
[10] KernSmooth_2.23-3   mvoutlier_1.4       outliers_0.13-2    
[13] flowViz_1.12.0      lattice_0.18-5      flowCore_1.14.1    
[16] rrcov_1.0-00        pcaPP_1.8-1         mvtnorm_0.9-9      
[19] robustbase_0.5-0-1  Biobase_2.8.0       fortunes_1.3-7     

loaded via a namespace (and not attached):
 [1] AnnotationDbi_1.10.1 DBI_0.2-5            MASS_7.3-5          
 [4] RSQLite_0.9-0        annotate_1.26.0      feature_1.2.4       
 [7] geneplotter_1.26.0   graph_1.26.0         grid_2.11.0         
[10] ks_1.6.12            stats4_2.11.0        xtable_1.5-6
 		 	   		  
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