Class GatingHierarchy

Description

GatingHierarchy is a class for representing the gating hierarchy,which can be either imported from a flowJo workspace or constructed in R.

Details

There is a one-to-one correspondence between GatingHierarchy objects and FCS files in the flowJo workspace. Each sample (FCS file) is associated with it's own GatingHierarchy. It is also more space efficient by storing gating results as logical/bit vector instead of copying the raw data.

Given a GatingHierarchy, one can extract the data associated with any subpopulation, extract gates, plot gates, and extract population proportions. This facilitates the comparison of manual gating methods with automated gating algorithms.

See Also

GatingSet

Examples

require(flowWorkspaceData)
	d<-system.file("extdata",package="flowWorkspaceData")
	wsfile<-list.files(d,pattern="A2004Analysis.xml",full=TRUE)
	ws <- openWorkspace(wsfile);
	G<-try(parseWorkspace(ws,path=d,name=1));
 gh <- G[[1]]
	getPopStats(gh);
	plotPopCV(gh)
 nodes <- getNodes(gh)
 thisNode <- nodes[4]
	plotGate(gh,thisNode);
	getGate(gh,thisNode);
	getData(gh,thisNode)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> library(flowWorkspace)
Loading required package: flowCore
Loading required package: flowViz
Loading required package: lattice
Loading required package: ncdfFlow
Loading required package: RcppArmadillo
Loading required package: BH
Loading required package: gridExtra
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/flowWorkspace/GatingHierarchy-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GatingHierarchy-class
> ### Title: Class GatingHierarchy
> ### Aliases: GatingHierarchy-class show,GatingHierarchy-method
> 
> ### ** Examples
> 
> require(flowWorkspaceData)
Loading required package: flowWorkspaceData
> 	d<-system.file("extdata",package="flowWorkspaceData")
> 	wsfile<-list.files(d,pattern="A2004Analysis.xml",full=TRUE)
> 	ws <- openWorkspace(wsfile);
> 	G<-try(parseWorkspace(ws,path=d,name=1));
Parsing 2 samples
mac version of flowJo workspace recognized.
Creating ncdfFlowSet...
All FCS files have the same following channels:
FSC-A
FSC-H
FSC-W
SSC-A
SSC-H
SSC-W
Am Cyan-A
Am Cyan-H
Pacific Blue-A
Pacific Blue-H
APC-A
APC-H
APC-CY7-A
APC-CY7-H
Alexa 700-A
Alexa 700-H
FITC-A
FITC-H
PerCP-CY5-5-A
PerCP-CY5-5-H
PE-CY7-A
PE-CY7-H
Time
done!
loading data: /home/ddbj/local/lib64/R/library/flowWorkspaceData/extdata/a2004_O1T2pb05i_A1_A01.fcs
Compensating
gating ...
write a2004_O1T2pb05i_A1_A01.fcs_61832 to empty cdf slot...
loading data: /home/ddbj/local/lib64/R/library/flowWorkspaceData/extdata/a2004_O1T2pb05i_A2_A02.fcs
Compensating
gating ...
write a2004_O1T2pb05i_A2_A02.fcs_45363 to empty cdf slot...
done!
>  gh <- G[[1]]
> 	getPopStats(gh);
    flowCore.freq flowJo.freq flowJo.count flowCore.count             node
 1:   1.000000000 1.000000000        61832          61832             root
 2:   0.800297581 0.801235606        49542          49484             Live
 3:   0.084027160 0.083585645         4141           4158              APC
 4:   0.592352092 0.548418256         2271           2463           B Cell
 5:   0.123376623 0.121226757          502            513              mDC
 6:   0.005847953 0.003984064            2              3        mDC/IFNa+
 7:   0.042884990 0.043824701           22             22        mDC/IL-6+
 8:   0.005847953 0.003984064            2              3       mDC/IL-12+
 9:   0.140350877 0.141434263           71             72        mDC/TNFa+
10:   0.107984608 0.107703453          446            449              pDC
11:   0.002227171 0.002242152            1              1        pDC/IFNa+
12:   0.000000000 0.000000000            0              0        pDC/IL-6+
13:   0.561247216 0.560538117          250            252       pDC/IL-12+
14:   0.000000000 0.000000000            0              0        pDC/TNFa+
15:   0.544074852 0.540854225        26795          26923       CD14-MHC2-
16:   0.058928138 0.059161923         2931           2916        Monocytes
17:   0.003772291 0.004435346           13             11  Monocytes/IFNa+
18:   0.237654321 0.236779256          694            693  Monocytes/IL-6+
19:   0.047325103 0.049812351          146            138 Monocytes/IL-12+
20:   0.250685871 0.257250085          754            731  Monocytes/TNFa+
> 	plotPopCV(gh)
>  nodes <- getNodes(gh)
>  thisNode <- nodes[4]
> 	plotGate(gh,thisNode);
> 	getGate(gh,thisNode);
Polygonal gate 'B Cell' with 24 vertices in dimensions <Am Cyan-A> and <APC-A>
> 	getData(gh,thisNode)
flowFrame object '1be493f5-51dd-4359-b2ed-524cd104eb5f'
with 2463 cells and 23 observables:
                 name  desc      range   minRange   maxRange
$P1             FSC-A  <NA> 262254.000 -111.00000 262143.000
$P2             FSC-H  <NA> 262143.000    0.00000 262143.000
$P3             FSC-W  <NA> 262143.000    0.00000 262143.000
$P4             SSC-A  <NA> 262254.000 -111.00000 262143.000
$P5             SSC-H  <NA> 262143.000    0.00000 262143.000
$P6             SSC-W  <NA> 262143.000    0.00000 262143.000
$P7       <Am Cyan-A> CD123   3661.959  435.34379   4097.303
$P8         Am Cyan-H CD123   3641.837  455.00000   4096.837
$P9  <Pacific Blue-A> IL-12   3927.974  169.60860   4097.582
$P10   Pacific Blue-H IL-12   3641.837  455.00000   4096.837
$P11          <APC-A> CD11c   4405.818 -308.01302   4097.805
$P12            APC-H CD11c   3641.837  455.00000   4096.837
$P13      <APC-CY7-A>  IL-6   3714.446  382.93207   4097.378
$P14        APC-CY7-H  IL-6   3641.837  455.00000   4096.837
$P15    <Alexa 700-A>  TNFa   3712.753  384.62271   4097.376
$P16      Alexa 700-H  TNFa   3641.837  455.00000   4096.837
$P17         <FITC-A>  IFNa   4180.519  -82.81306   4097.706
$P18           FITC-H  IFNa   3641.837  455.00000   4096.837
$P19  <PerCP-CY5-5-A> MHCII   4942.398 -844.59317   4097.805
$P20    PerCP-CY5-5-H MHCII   3641.837  455.00000   4096.837
$P21       <PE-CY7-A>  CD14   4942.398 -844.59317   4097.805
$P22         PE-CY7-H  CD14   3641.837  455.00000   4096.837
$P23             Time  <NA>     99.184    0.89000    100.074
322 keywords are stored in the 'description' slot
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>