Class

A filter holding an expression that can be evaluated to a logical vector or a vector of factors.

expressionFilter(expr, ..., filterId="defaultExpressionFilter")
char2ExpressionFilter(expr, ..., filterId="defaultExpressionFilter")

Arguments

filterId	An optional parameter that sets the `filterId` of this `filter`. The object can later be identified by this name.
expr	A valid R expression or a character vector that can be parsed into an expression.
...	Additional arguments that are passed to the evaluation environment of the expression.

Value

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

Details

The expression is evaluated in the environment of the flow cytometry values, hence the parameters of a flowFrame can be accessed through regular R symbols. The convenience function char2ExpressionFilter exists to programmatically construct expressions.

Slots

expr

The expression that will be evaluated in the context of the flow cytometry values.

args

An environment providing additional parameters.

deparse

A character scalar of the deparsed expression.

filterId

The identifier of the filter.

Extends

Class "concreteFilter", directly.

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

Objects from the Class

Objects can be created by calls of the form new("expressionFilter", ...), using the expressionFilter constructor or, programmatically, from a character string using the char2ExpressionFilter function.

Methods

%in%

signature(x = "flowFrame", table = "expressionFilter"): The workhorse used to evaluate the gate on data. This is usually not called directly by the user, but internally by calls to the filter methods.

show

signature(object = "expressionFilter"): Print information about the gate.

Examples


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

#Create the filter
ef <- expressionFilter(`FSC-H` > 200, filterId="myExpressionFilter")
ef
#> expression filter 'myExpressionFilter' evaluating the expression:
#> `FSC-H` > 200

## Filtering using sampeFilters
fres <- filter(dat, ef)
fres
#> A filterResult produced by the filter named 'myExpressionFilter'
summary(fres)
#> myExpressionFilter+: 9465 of 10000 events (94.65%)

## The result of sample filtering is a logical subset
newDat <- Subset(dat, fres)
all(exprs(newDat)[,"FSC-H"] > 200)
#> [1] TRUE

## We can also split, in which case we get those events in and those
## not in the gate as separate populations
split(dat, fres)
#> $`myExpressionFilter+`
#> flowFrame object '0877408774.B08 (myExpressionFilter+)'
#> with 9465 cells and 8 observables:
#>      name              desc range minRange maxRange
#> $P1 FSC-H             FSC-H  1024 0.000000     1023
#> $P2 SSC-H             SSC-H  1024 0.000000     1023
#> $P3 FL1-H              <NA>  1024 1.009044    10000
#> $P4 FL2-H              <NA>  1024 1.009044    10000
#> $P5 FL3-H              <NA>  1024 1.009044    10000
#> $P6 FL1-A              <NA>  1024 0.000000     1023
#> $P7 FL4-H              <NA>  1024 1.009044    10000
#> $P8  Time Time (51.20 sec.)  1024 0.000000     1023
#> 164 keywords are stored in the 'description' slot
#> 
#> $`myExpressionFilter-`
#> flowFrame object '0877408774.B08 (myExpressionFilter-)'
#> with 535 cells and 8 observables:
#>      name              desc range minRange maxRange
#> $P1 FSC-H             FSC-H  1024 0.000000     1023
#> $P2 SSC-H             SSC-H  1024 0.000000     1023
#> $P3 FL1-H              <NA>  1024 1.009044    10000
#> $P4 FL2-H              <NA>  1024 1.009044    10000
#> $P5 FL3-H              <NA>  1024 1.009044    10000
#> $P6 FL1-A              <NA>  1024 0.000000     1023
#> $P7 FL4-H              <NA>  1024 1.009044    10000
#> $P8  Time Time (51.20 sec.)  1024 0.000000     1023
#> 164 keywords are stored in the 'description' slot
#> 

## Programmatically construct an expression
dat <- dat[,-8]
r <- range(dat)
cn <- paste("`", colnames(dat), "`", sep="")
exp <- paste(cn, ">", r[1,], "&", cn, "<", r[2,], collapse=" & ")
ef2 <- char2ExpressionFilter(exp, filterId="myExpressionFilter")
ef2
#> expression filter 'myExpressionFilter' evaluating the expression:
#> `FSC-H` > 0 & `FSC-H` < 1023 & `SSC-H` > 0 & `SSC-H` < 1023 & `FL1-H` > 1.00904391657007 & `FL1-H` < 10000 & `FL2-H` > 1.00904391657007 & `FL2-H` < 10000 & `FL3-H` > 1.00904391657007 & `FL3-H` < 10000 & `FL1-A` > 0 & `FL1-A` < 1023 & `FL4-H` > 1.00904391657007 & `FL4-H` < 10000
fres2 <- filter(dat, ef2)
fres2
#> A filterResult produced by the filter named 'myExpressionFilter'
summary(fres2)
#> myExpressionFilter+: 4224 of 10000 events (42.24%)

Class "expressionFilter"