plot.GateFinder {GateFinder} | R Documentation

## plot.GateFinder

### Description

Creates a scatter plot for each of the gating steps.

### Usage

    ## S3 method for class 'GateFinder'
    plot(x, y, ncolrow=c(1,max(targetpop)), targetpop=NULL, beta=NULL,  cexs=NULL, cols=NULL, subsample=length(targetpop), max.iter=length(y@gates), pot=TRUE, xlim=NULL, ylim=NULL, asinh.axis=FALSE, ...)

### Arguments

`x` | A flowFrame or an expression matrix in which columns are markers and rows are cells.
`y` | A GatingProjection object.
`ncolrow` | A vector of length 2 indicating the desired number of rows and columns in the plot.
`targetpop` | The target cell type.
`beta` | A positive real value which control the trade-off between precision and recall in the F-measure calculation. Values smaller than 1 (and closer to 0) emphasize recall values and values larger than 1 emphasize precision.
`cexs` | A vector of length 3 indicating the point sizes for 1-previously excluded cells 2-non-selected cells 3-selected cells.
`cols` | A vector of length 3 indicating the point colors for 1-previously excluded cells 2-non-selected cells 3-selected cells.
`subsample` | The number of randomized runs (integer). The results from the best (or median) randomized run will be used. See `selection.criteria`).
`max.iter` | A boolean controling weather the gating strategy calculated using a random subset of the cells should be applied to all cells or not.
`pot` | A boolean value. If true, the points of interest will be ploted on top of other points to increase visibility.
`xlim` | Static x-axis limits for the plots (vector of length 2).
`ylim` | Static y-axis limits for the plots (vector of length 2).
`asinh.axis` | A boolean value indicating if asinh axis ticks should be plotted (usually used for mass cytometry data).
`...` | Other arguments passed to the plot function.

### Value

`Plot` | A GateFinder plot.

### Author(s)

Nima Aghaeepour <naghaeep@gmail.com> and Erin F. Simonds <erin.simonds@gmail.com>.

### Examples

    library(flowCore)

    data(LPSData)
    ##Select the target population. In this case cells with those with a pP38 expression (dimension 34) of higher than 3.5.
    targetpop <- (exprs(rawdata)[,34]>3.5)

    ##Subset the markers that should be considered for gating.
    x=exprs(rawdata)[,prop.markers]
    colnames(x)=marker.names[prop.markers]

    ##Run GateFinder.
    ans=GateFinder(x, targetpop)

    ##Make the plots.
    plot(x, ans, c(2,3), targetpop)
    plot(ans)

    ##Alternatively, using a flowFrame:
    x=new('flowFrame', exprs=x)
    ans=GateFinder(x, targetpop)

    ##Now you can use the gates and filters to subset the flowFrame. E.g.:
    split(x, ans@flowEnv$Filter2)

    ##This function relies on an EXPERIMENTAL feature in flowUtils. Please be cautious when replying on this.
    ##Don't run without the optional flowUtils package installed.
    ##To write the gates into a GatingML file:
    ##library(flowUtils)
    ##write.gatingML(ans@flowEnv, 'GatingML.xml')

[Package GateFinder version 1.8.0 Index]