R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Class "curv1Filter"

curv1Filter-class

R Documentation

Class "curv1Filter"

Description

Class and constructor for data-driven filter objects that selects high-density regions in one dimension.

Usage


curv1Filter(x, bwFac=1.2, gridsize=rep(401, 2),
filterId="defaultCurv1Filter")

Arguments

`x`	Character giving the name of the measurement parameter on which the filter is supposed to work on. This can also be a list containing a single character scalar for programmatic access.
`filterId`	An optional parameter that sets the `filterId` slot of this filter. The object can later be identified by this name.
`bwFac, gridsize`	Numerics of length 1 and 2, respectively, used to set the `bwFac` and `gridsize` slots of the object.

Details

Areas of high local density in one dimensions are identified by detecting significant curvature regions. See Duong, T. and Cowling, A. and Koch, I. and Wand, M.P., Computational Statistics and Data Analysis 52/9, 2008 for details. The constructor curv1Filter is a convenience function for object instantiation. Evaluating a curv1Filter results in potentially multiple sub-populations, an hence in an object of class multipleFilterResult. Accordingly, curv1Filters can be used to split flow cytometry data sets.

Value

Returns a curv1Filter object for use in filtering flowFrames or other flow cytometry objects.

Extends

Class "parameterFilter", directly.

Class "concreteFilter", by class parameterFilter, distance 2.

Class "filter", by class parameterFilter, distance 3.

Slots

bwFac:: Object of class "numeric". The bandwidth factor used for smoothing of the density estimate.
gridsize:: Object of class "numeric". The size of the bins used for density estimation.
parameters:: Object of class "character", describing the parameter used to filter the flowFrame.
filterId:: Object of class "character", referencing the filter.

Objects from the Class

Objects can be created by calls of the form new("curvFilter", ...) or using the constructor curv1Filter. Using the constructor is the recommended way of object instantiation:

Methods

%in%: signature(x = "flowFrame", table = "curv1Filter"): The workhorse used to evaluate the filter on data. This is usually not called directly by the user, but internally by calls to the filter methods.
show: signature(object = "curv1Filter"): Print information about the filter.

Note

See the documentation in the flowViz package for plotting of curv1Filters.

Author(s)

Florian Hahne

Examples


library(flowStats)
## Loading example data
dat <- read.FCS(system.file("extdata","0877408774.B08",
package="flowCore"))

## Create directly. Most likely from a command line
curv1Filter("FSC-H", filterId="myCurv1Filter", bwFac=2)

## To facilitate programmatic construction we also have the following
c1f <- curv1Filter(filterId="myCurv1Filter", x=list("FSC-H"), bwFac=2)

## Filtering using curv1Filter
fres <- filter(dat, c1f)
fres
summary(fres)
names(fres)

## The result of curv1 filtering are multiple sub-populations
## and we can split our data set accordingly
split(dat, fres)

## We can limit the splitting to one or several sub-populations
split(dat, fres, population="rest")
split(dat, fres, population=list(keep=c("peak 2", "peak 3")))

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(flowStats)
Loading required package: flowCore
Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:flowCore':

    %&%


Attaching package: 'fda'

The following object is masked from 'package:graphics':

    matplot

Loading required package: mvoutlier
Loading required package: sgeostat
sROC 0.1-2 loaded
Loading required package: cluster
Loading required package: flowWorkspace
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/flowStats/curv1Filter-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: curv1Filter-class
> ### Title: Class "curv1Filter"
> ### Aliases: curv1Filter-class curv1Filter show,curv1Filter-method
> ###   %in%,flowFrame,curv1Filter-method
> ###   summarizeFilter,multipleFilterResult,curv1Filter-method
> ### Keywords: classes methods
> 
> ### ** Examples
> 
> 
> library(flowStats)
> ## Loading example data
> dat <- read.FCS(system.file("extdata","0877408774.B08",
+ package="flowCore"))
> 
> ## Create directly. Most likely from a command line
> curv1Filter("FSC-H", filterId="myCurv1Filter", bwFac=2)
1D curvature filter 'myCurv1Filter' in dimension FSC-H
with settings:
  bwFac=2
  gridsize=401,401
> 
> ## To facilitate programmatic construction we also have the following
> c1f <- curv1Filter(filterId="myCurv1Filter", x=list("FSC-H"), bwFac=2)
> 
> ## Filtering using curv1Filter
> fres <- filter(dat, c1f)
> fres
A filterResult produced by the filter named 'myCurv1Filter'
 resulting in multiple populations:
	 rest
	 peak 1
	 peak 2
	 peak 3
	 peak 4
> summary(fres)
rest: 3581 of 10000 events (35.81%)
peak 1: 331 of 10000 events (3.31%)
peak 2: 5575 of 10000 events (55.75%)
peak 3: 455 of 10000 events (4.55%)
peak 4: 58 of 10000 events (0.58%)
> names(fres)
[1] "rest"   "peak 1" "peak 2" "peak 3" "peak 4"
> 
> ## The result of curv1 filtering are multiple sub-populations
> ## and we can split our data set accordingly
> split(dat, fres)
$rest
flowFrame object '0877408774.B08 (rest)'
with 3581 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
165 keywords are stored in the 'description' slot

$`peak 1`
flowFrame object '0877408774.B08 (peak 1)'
with 331 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
165 keywords are stored in the 'description' slot

$`peak 2`
flowFrame object '0877408774.B08 (peak 2)'
with 5575 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
165 keywords are stored in the 'description' slot

$`peak 3`
flowFrame object '0877408774.B08 (peak 3)'
with 455 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
165 keywords are stored in the 'description' slot

$`peak 4`
flowFrame object '0877408774.B08 (peak 4)'
with 58 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
165 keywords are stored in the 'description' slot

> 
> ## We can limit the splitting to one or several sub-populations
> split(dat, fres, population="rest")
$rest
flowFrame object '0877408774.B08 (rest)'
with 3581 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
165 keywords are stored in the 'description' slot

> split(dat, fres, population=list(keep=c("peak 2", "peak 3")))
$keep
flowFrame object '0877408774.B08 (peak 2,peak 3)'
with 6030 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H             FSC-H  1024        0     1023
$P2 SSC-H             SSC-H  1024        0     1023
$P3 FL1-H              <NA>  1024        1    10000
$P4 FL2-H              <NA>  1024        1    10000
$P5 FL3-H              <NA>  1024        1    10000
$P6 FL1-A              <NA>  1024        0     1023
$P7 FL4-H              <NA>  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
3 keywords are stored in the 'description' slot

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>