'flowFrame': a class for storing observed quantitative properties for a population of cells from a FACS run

Description

This class represents the data contained in a FCS file or similar data structure. There are three parts of the data:

1. a numeric matrix of the raw measurement values with rows=events and columns=parameters

2. annotation for the parameters (e.g., the measurement channels, stains, dynamic range)

3. additional annotation provided through keywords in the FCS file

Details

Objects of class flowFrame can be used to hold arbitrary data of cell populations, acquired in flow-cytometry.

FCS is the Data File Standard for Flow Cytometry, the current version is FCS 3.0. See the vignette of this package for additional information on using the object system for handling of flow-cytometry data.

Creating Objects

Objects can be created using
new("flowFrame",
exprs = ...., Object of class matrix
parameters = ...., Object of class AnnotatedDataFrame
description = ...., Object of class list
)

or the constructor flowFrame, with mandatory arguments exprs and optional arguments parameters and description.

flowFrame(exprs, parameters, description=list())

To create a flowFrame directly from an FCS file, use function read.FCS. This is the recommended and safest way of object creation, since read.FCS will perform basic data quality checks upon import. Unless you know exactly what you are doing, creating objects using new or the constructor is discouraged.

Slots

exprs:

Object of class matrix containing the measured intensities. Rows correspond to cells, columns to the different measurement channels. The colnames attribute of the matrix is supposed to hold the names or identifiers for the channels. The rownames attribute would usually not be set.

parameters:

An AnnotatedDataFrame containing information about each column of the flowFrame. This will generally be filled in by read.FCS or similar functions using data from the FCS keywords describing the parameters.

description:

A list containing the meta data included in the FCS file.

Methods

There are separate documentation pages for most of the methods listed here which should be consulted for more details.

[

Subsetting. Returns an object of class flowFrame. The subsetting is applied to the exprs slot, while the description slot is unchanged. The syntax for subsetting is similar to that of data.frames. In addition to the usual index vectors (integer and logical by position, character by parameter names), flowFrames can be subset via filterResult and filter objects.

Usage:

flowFrame[i,j]

flowFrame[filter,]

flowFrame[filterResult,]

Note that the value of argument drop is ignored when subsetting flowFrames.

$

Subsetting by channel name. This is similar to subsetting of columns of data.frames, i.e., frame$FSC.H is equivalent to frame[, "FSC.H"]. Note that column names may have to be quoted if they are no valid R symbols (e.g. frame$"FSC-H").

exprs, exprs<-

Extract or replace the raw data intensities. The replacement value must be a numeric matrix with colnames matching the parameter definitions. Implicit subsetting is allowed (i.e. less columns in the replacement value compared to the original flowFrame, but all have to be defined there).

Usage:

exprs(flowFrame)

exprs(flowFrame) <- value

head, tail

Show first/last elements of the raw data matrix

Usage:

head(flowFrame)

tail(flowFrame)

description, description<-

Extract or replace the whole list of annotation keywords. Usually one would only be interested in a subset of keywords, in which case the keyword method is more appropriate. The optional hideInternal parameter can be used to exclude internal FCS parameters starting with$.

Usage:

description(flowFrame)

description(flowFrame) <- value

keyword, keyword<-

Extract ore replace one or more entries from the description slot by keyword. Methods are defined for character vectors (select a keyword by name), functions (select a keyword by evaluating a function on their content) and for lists (a combination of the above). See keyword for details.

Usage:

keyword(flowFrame)

keyword(flowFrame, character)

keyword(flowFrame, list)

keyword(flowFrame) <- list(value)

parameters, parameters<-

Extract parameters and return an object of class AnnotatedDataFrame, or replace such an object. To access the actual parameter annotation, use pData(parameters(frame)). Replacement is only valid with AnnotatedDataFrames containing all varLabels name, desc, range, minRange and maxRange, and matching entries in the name column to the colnames of the exprs matrix. See parameters for more details.

Usage:

parameters(flowFrame)

parameters(flowFrame) <- value

show

Display details about the flowFrame object.

summary

Return descriptive statistical summary (min, max, mean and quantile) for each channel

Usage:

summary(flowFrame)

plot

Basic plots for flowFrame objects. If the object has only a single parameter this produces a histogram. For exactly two parameters we plot a bivariate density map (see smoothScatter and for more than two parameters we produce a simple splom plot. To select specific parameters from a flowFrame for plotting, either subset the object or specify the parameters as a character vector in the second argument to plot. The smooth parameters lets you toggle between density-type smoothScatter plots and regular scatterplots. For far more sophisticated plotting of flow cytometry data, see the flowViz package.

Usage:

plot(flowFrame, ...)

plot(flowFrame, character, ...)

plot(flowFrame, smooth=FALSE, ...)

ncol, nrow, dim

Extract the dimensions of the data matrix.

Usage:

ncol(flowFrame)

nrow(flowFrame)

dim(flowFrame)

featureNames, colnames, colnames<-

. colnames and featureNames are synonyms, they extract parameter names (i.e., the colnames of the data matrix) . For colnames there is also a replacement method. This will update the name column in the parameters slot as well.

Usage:

featureNames(flowFrame)

colnames(flowFrame)

colnames(flowFrame) <- value

names

Extract pretty formated names of the parameters including parameter descriptions.

Usage:

names(flowFrame)

identifier

Extract GUID of a flowFrame. Returns the file name if no GUID is available. See identifier for details.

Usage:

identifier(flowFrame)

range

Get dynamic range of the flowFame. Note that this is not necessarily the range of the actual data values, but the theoretical range of values the measurement instrument was able to capture. The values of the dynamic range will be transformed when using the transformation methods for flowFrames. Additional character arguments are evaluated as parameter names for which to return the dynamic range.

Usage:

range(flowFrame, ...)

each_row, each_col

Apply functions over rows or columns of the data matrix. These are convenience methods. See each_col for details.

Usage:

each_row(flowFrame, function, ...)

each_col(flowFrame, function, ...)

transform

Apply a transformation function on a flowFrame object. This uses R's transform function by treating the flowFrame like a regular data.frame. flowCore provides an additional inline mechanism for transformations (see %on%) which is strictly more limited than the out-of-line transformation described here.

Usage:

transform(flowFrame, translist, ...)

filter

Apply a filter object on a flowFrame object. This returns an object of class filterResult, which could then be used for subsetting of the data or to calculate summary statistics. See filter for details.

Usage:

filter(flowFrame, filter)

split

Split flowFrame object according to a filter, a filterResult or a factor. For most types of filters, an optional flowSet=TRUE parameter will create a flowSet rather than a simple list. See split for details.

Usage:

split(flowFrame, filter, flowSet=FALSE, ...)

split(flowFrame, filterResult, flowSet=FALSE, ...)

split(flowFrame, factor, flowSet=FALSE, ...)

Subset

Subset a flowFrame according to a filter or a logical vector. The same can be done using the standard subsetting operator with a filter, filterResult, or a logical vector as first argument.

Usage:

Subset(flowFrame, filter)

Subset(flowFrame, logical)

cbind2

Expand a flowFrame by the data in a numeric matrix of the same length. The matrix must have column names different from those of the flowFrame. The additional method for numerics only raises a useful error message.

Usage:

cbind2(flowFrame, matrix)

cbind2(flowFrame, numeric)

compensate

Apply a compensation matrix (or a compensation object) on a flowFrame object. This returns a compensated flowFrame.

Usage:

compensate(flowFrame, matrix) compensate(flowFrame, data.frame)

spillover

Extract spillover matrix from description slot if present. It is equivalent to keyword(x, c("spillover", "SPILL")) Thus will simply return a list of keywords value for "spillover" and "SPILL".

Usage:

spillover(flowFrame)

==

Test equality between two flowFrames

<, >, <=, >=

These operators basically treat the flowFrame as a numeric matrix.

initialize(flowFrame):

Object instantiation, used by new; not to be called directly by the user.

Author(s)

F. Hahne, B. Ellis, P. Haaland and N. Le Meur

See Also

flowSet, read.FCS

Examples

## load example data
data(GvHD)
frame <- GvHD[[1]]

## subsetting
frame[1:4,]
frame[,3]
frame[,"FSC-H"]
frame$"SSC-H"

## accessing and replacing raw values
head(exprs(frame))
exprs(frame) <- exprs(frame)[1:3000,]
frame
exprs(frame) <- exprs(frame)[,1:6]
frame

## access FCS keywords
head(description(frame))
keyword(frame, c("FILENAME", "$FIL"))

## parameter annotation
parameters(frame)
pData(parameters(frame))

## summarize frame data
summary(frame)

## plotting
plot(frame)
if(require(flowViz)){
plot(frame)
plot(frame, c("FSC-H", "SSC-H"))
plot(frame[,1])
plot(frame, c("FSC-H", "SSC-H"), smooth=FALSE)
}

## frame dimensions
ncol(frame)
nrow(frame)
dim(frame)

## accessing and replacing parameter names
featureNames(frame)
all(featureNames(frame) == colnames(frame))
colnames(frame) <- make.names(colnames(frame))
colnames(frame)
parameters(frame)$name
names(frame)

## accessing a GUID
identifier(frame)
identifier(frame) <- "test"

## dynamic range of a frame
range(frame)
range(frame, "FSC.H", "FL1.H")
range(frame)$FSC.H

## iterators
head(each_row(frame, mean))
head(each_col(frame, mean))

## transformation
opar <- par(mfcol=c(1:2))
if(require(flowViz))
plot(frame, c("FL1.H", "FL2.H"))
frame <- transform(frame, transformList(c("FL1.H", "FL2.H"), log))
if(require(flowViz))
plot(frame, c("FL1.H", "FL2.H"))
par(opar)
range(frame)

## filtering of flowFrames
rectGate <- rectangleGate(filterId="nonDebris","FSC.H"=c(200,Inf))
fres <- filter(frame, rectGate)
summary(fres)

## splitting of flowFrames
split(frame, rectGate)
split(frame, rectGate, flowSet=TRUE)
split(frame, fres)
f <- cut(exprs(frame$FSC.H), 3)
split(frame, f)

## subsetting according to filters and filter results
Subset(frame, rectGate)
Subset(frame, fres)
Subset(frame, as.logical(exprs(frame$FSC.H) < 300))
frame[rectGate,]
frame[fres,]

## accessing the spillover matrix
try(spillover(frame))

## check equality
frame2 <- frame
frame == frame2
exprs(frame2) <- exprs(frame)*2
frame == frame2

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(flowCore)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/flowCore/flowFrame-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: flowFrame-class
> ### Title: 'flowFrame': a class for storing observed quantitative
> ###   properties for a population of cells from a FACS run
> ### Aliases: flowFrame-class flowFrame [,flowFrame,ANY-method
> ###   [,flowFrame,filter-method [,flowFrame,filterResult-method $.flowFrame
> ###   exprs exprs<- exprs,flowFrame-method exprs<-,flowFrame,matrix-method
> ###   exprs<-,flowFrame,ANY-method initialize,flowFrame-method
> ###   head,flowFrame-method tail,flowFrame-method description
> ###   description,flowFrame-method description<-,flowFrame,list-method
> ###   description<-,flowFrame,ANY-method show,flowFrame-method
> ###   plot,flowFrame,ANY-method plot,flowFrame-method
> ###   summary,flowFrame-method ncol,flowFrame-method nrow,flowFrame-method
> ###   dim dim,flowFrame-method featureNames featureNames,flowFrame-method
> ###   colnames,flowFrame-method colnames<- colnames<-,flowFrame-method
> ###   names names,flowFrame-method range range,flowFrame-method
> ###   cbind2,flowFrame,matrix-method cbind2,flowFrame,numeric-method
> ###   transform,flowFrame-method compensate,flowFrame,matrix-method
> ###   compensate,flowFrame,data.frame-method
> ###   compensate,flowFrame,compensation-method
> ###   ==,flowFrame,filterResult-method ==,flowFrame,flowFrame-method
> ###   <,flowFrame,ANY-method <=,flowFrame,ANY-method >,flowFrame,ANY-method
> ###   >=,flowFrame,ANY-method spillover,flowFrame-method
> ### Keywords: classes
> 
> ### ** Examples
> 
> 
> ## load example data
> data(GvHD)
> frame <- GvHD[[1]]
> 
> ## subsetting
> frame[1:4,]
flowFrame object 's5a01'
with 4 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H        FSC-Height  1024        0     1023
$P2 SSC-H        SSC-Height  1024        0     1023
$P3 FL1-H         CD15 FITC  1024        1    10000
$P4 FL2-H           CD45 PE  1024        1    10000
$P5 FL3-H        CD14 PerCP  1024        1    10000
$P6 FL2-A              <NA>  1024        0     1023
$P7 FL4-H          CD33 APC  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
153 keywords are stored in the 'description' slot
> frame[,3]
flowFrame object 's5a01'
with 3420 cells and 1 observables:
     name      desc range minRange maxRange
$P3 FL1-H CD15 FITC  1024        1    10000
119 keywords are stored in the 'description' slot
> frame[,"FSC-H"]
flowFrame object 's5a01'
with 3420 cells and 1 observables:
     name       desc range minRange maxRange
$P1 FSC-H FSC-Height  1024        0     1023
119 keywords are stored in the 'description' slot
> frame$"SSC-H"
flowFrame object 's5a01'
with 3420 cells and 1 observables:
     name       desc range minRange maxRange
$P2 SSC-H SSC-Height  1024        0     1023
119 keywords are stored in the 'description' slot
> 
> ## accessing and replacing raw values
> head(exprs(frame))
     FSC-H SSC-H       FL1-H       FL2-H     FL3-H FL2-A     FL4-H Time
[1,]   371   396 2432.871983  507.887297 18.156914   110 21.739192    1
[2,]   190    62    7.513726 1006.775298 26.982678   213  1.000000    1
[3,]   141   197    3.194470  597.239582  3.109343   132 29.524716    1
[4,]   167   265 1977.824185  143.998435  5.839470    28  4.579326    1
[5,]   128    30    1.321941    1.640793  5.632914     0  1.655632    1
[6,]   208    60    8.295949 1121.639700 10.868463   240  1.000000    1
> exprs(frame) <- exprs(frame)[1:3000,]
> frame
flowFrame object 's5a01'
with 3000 cells and 8 observables:
     name              desc range minRange maxRange
$P1 FSC-H        FSC-Height  1024        0     1023
$P2 SSC-H        SSC-Height  1024        0     1023
$P3 FL1-H         CD15 FITC  1024        1    10000
$P4 FL2-H           CD45 PE  1024        1    10000
$P5 FL3-H        CD14 PerCP  1024        1    10000
$P6 FL2-A              <NA>  1024        0     1023
$P7 FL4-H          CD33 APC  1024        1    10000
$P8  Time Time (51.20 sec.)  1024        0     1023
153 keywords are stored in the 'description' slot
> exprs(frame) <- exprs(frame)[,1:6]
> frame
flowFrame object 's5a01'
with 3000 cells and 6 observables:
     name       desc range minRange maxRange
$P1 FSC-H FSC-Height  1024        0     1023
$P2 SSC-H SSC-Height  1024        0     1023
$P3 FL1-H  CD15 FITC  1024        1    10000
$P4 FL2-H    CD45 PE  1024        1    10000
$P5 FL3-H CD14 PerCP  1024        1    10000
$P6 FL2-A       <NA>  1024        0     1023
153 keywords are stored in the 'description' slot
> 
> ## access FCS keywords
> head(description(frame))
$FCSversion
[1] "2"

$`$BYTEORD`
[1] "4,3,2,1"

$`$DATATYPE`
[1] "I"

$`$NEXTDATA`
[1] "0"

$`$SYS`
[1] "Macintosh" "System"    "Software"  "9.0.4"    

$CREATOR
[1] "CELLQuest252" "3.3"          

> keyword(frame, c("FILENAME", "$FIL"))
$FILENAME
[1] "s5a01"

$`$FIL`
[1] "A01"

> 
> ## parameter annotation
> parameters(frame)
An object of class 'AnnotatedDataFrame'
  rowNames: $P1 $P2 ... $P6 (6 total)
  varLabels: name desc ... maxRange (5 total)
  varMetadata: labelDescription
> pData(parameters(frame))
     name       desc range minRange maxRange
$P1 FSC-H FSC-Height  1024        0     1023
$P2 SSC-H SSC-Height  1024        0     1023
$P3 FL1-H  CD15 FITC  1024        1    10000
$P4 FL2-H    CD45 PE  1024        1    10000
$P5 FL3-H CD14 PerCP  1024        1    10000
$P6 FL2-A       <NA>  1024        0     1023
> 
> ## summarize frame data
> summary(frame)
         FSC-H  SSC-H     FL1-H   FL2-H    FL3-H   FL2-A
Min.      59.0    6.0     1.000    1.00    1.000    0.00
1st Qu.  115.0   82.0     4.877   39.38    1.947    3.00
Median   199.0  145.5    18.820  109.90    4.037   20.00
Mean     244.6  202.4   875.700  336.70   11.280   71.74
3rd Qu.  337.0  237.0  1112.000  540.90    8.447  121.00
Max.    1023.0 1023.0 10000.000 9222.00 1132.000 1023.00
> 
> ## plotting
> plot(frame)
For plotting, please attach the 'flowViz' package.
   i.e., 'library(flowViz)'
> if(require(flowViz)){
+ plot(frame)
+ plot(frame, c("FSC-H", "SSC-H"))
+ plot(frame[,1])
+ plot(frame, c("FSC-H", "SSC-H"), smooth=FALSE)
+ }
Loading required package: flowViz
Loading required package: lattice
Warning messages:
1: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
2: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
3: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
4: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
5: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
6: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
7: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
8: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
9: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
10: In KernSmooth::bkde2D(x, gridsize = nbin, bandwidth = bandwidth,  :
  Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'
> 
> ## frame dimensions
> ncol(frame)
[1] 6
> nrow(frame)
[1] 3000
> dim(frame)
    events parameters 
      3000          6 
> 
> ## accessing and replacing parameter names
> featureNames(frame)
   $P1N    $P2N    $P3N    $P4N    $P5N    $P6N 
"FSC-H" "SSC-H" "FL1-H" "FL2-H" "FL3-H" "FL2-A" 
> all(featureNames(frame) == colnames(frame))
[1] TRUE
> colnames(frame) <- make.names(colnames(frame))
> colnames(frame)
[1] "FSC.H" "SSC.H" "FL1.H" "FL2.H" "FL3.H" "FL2.A"
> parameters(frame)$name
[1] "FSC.H" "SSC.H" "FL1.H" "FL2.H" "FL3.H" "FL2.A"
> names(frame)
[1] "<FSC.H> FSC.H" "<SSC.H> SSC.H" "<FL1.H> FL1.H" "<FL2.H> FL2.H"
[5] "<FL3.H> FL3.H" "<FL2.A> FL2.A"
> 
> ## accessing a GUID
> identifier(frame)
[1] "s5a01"
> identifier(frame) <- "test"
> 
> ## dynamic range of a frame
> range(frame)
    FSC.H SSC.H FL1.H FL2.H FL3.H FL2.A
min     0     0     1     1     1     0
max  1023  1023 10000 10000 10000  1023
> range(frame, "FSC.H", "FL1.H")
    FSC.H FL1.H
min     0     1
max  1023 10000
> range(frame)$FSC.H
[1]    0 1023
> 
> ## iterators
> head(each_row(frame, mean))
[1] 639.31937 251.04528 178.92390 431.27701  27.76594 274.80069
> head(each_col(frame, mean))
    FSC.H     SSC.H     FL1.H     FL2.H     FL3.H     FL2.A 
244.58633 202.40967 875.71021 336.73157  11.27785  71.74367 
> 
> ## transformation
> opar <- par(mfcol=c(1:2))
> if(require(flowViz))
+ plot(frame, c("FL1.H", "FL2.H"))
> frame <- transform(frame, transformList(c("FL1.H", "FL2.H"), log))
> if(require(flowViz))
+ plot(frame, c("FL1.H", "FL2.H"))
> par(opar)
> range(frame)
    FSC.H SSC.H   FL1.H   FL2.H FL3.H FL2.A
min     0     0 0.00000 0.00000     1     0
max  1023  1023 9.21034 9.21034 10000  1023
> 
> ## filtering of flowFrames
> rectGate <- rectangleGate(filterId="nonDebris","FSC.H"=c(200,Inf))
> fres <- filter(frame, rectGate)
> summary(fres)
nonDebris+: 1494 of 3000 events (49.80%)
> 
> ## splitting of flowFrames
> split(frame, rectGate)
$`nonDebris+`
flowFrame object 'test (nonDebris+)'
with 1494 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

$`nonDebris-`
flowFrame object 'test (nonDebris-)'
with 1506 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

> split(frame, rectGate, flowSet=TRUE)
A flowSet with 2 experiments.

An object of class 'AnnotatedDataFrame'
  rowNames: nonDebris+ nonDebris-
  varLabels: name population
  varMetadata: labelDescription

  column names:
  FSC.H SSC.H FL1.H FL2.H FL3.H FL2.A
> split(frame, fres)
$`nonDebris+`
flowFrame object 'test (nonDebris+)'
with 1494 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

$`nonDebris-`
flowFrame object 'test (nonDebris-)'
with 1506 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

> f <- cut(exprs(frame$FSC.H), 3)
> split(frame, f)
$`(58,380]`
flowFrame object 'test'
with 2427 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

$`(380,702]`
flowFrame object 'test'
with 535 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

$`(702,1.02e+03]`
flowFrame object 'test'
with 38 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot

> 
> ## subsetting according to filters and filter results
> Subset(frame, rectGate)
flowFrame object 'test'
with 1494 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot
> Subset(frame, fres)
flowFrame object 'test'
with 1494 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot
> Subset(frame, as.logical(exprs(frame$FSC.H) < 300))
flowFrame object 'test'
with 2075 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot
> frame[rectGate,]
flowFrame object 'test'
with 1494 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot
> frame[fres,]
flowFrame object 'test'
with 1494 cells and 6 observables:
     name       desc range minRange    maxRange
$P1 FSC.H FSC-Height  1024        0  1023.00000
$P2 SSC.H SSC-Height  1024        0  1023.00000
$P3 FL1.H  CD15 FITC  1024        0     9.21034
$P4 FL2.H    CD45 PE  1024        0     9.21034
$P5 FL3.H CD14 PerCP  1024        1 10000.00000
$P6 FL2.A       <NA>  1024        0  1023.00000
166 keywords are stored in the 'description' slot
> 
> ## accessing the spillover matrix
> try(spillover(frame))
Error in .local(x, ...) : No spillover matrix stored in that flowFrame
> 
> ## check equality
> frame2 <- frame
> frame == frame2
[1] TRUE
> exprs(frame2) <- exprs(frame)*2
> frame == frame2
[1] FALSE
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>