'cytoSet': a class for storing raw data from a quantitative cell-based assay

Description

This class is a container for a set of cytoFrame objects

Creating Objects

Objects can be created using the function readCytoSet or via
new('cytoSet',
frames = ...., # environment with cytoFrames
phenoData = .... # object of class phenoData
colnames = .... # object of class character
)

Slots

frames:

An environment containing one or more cytoFrame objects.

phenoData:

A phenoData. Each row corresponds to one of the cytoFrames in the frames slot. It is mandatory that the pData has column named name

colnames:

A character object with the (common) column names of all the data matrices in the cytoFrames.

Methods

[, [[

subsetting. If x is cytoSet, then x[i] returns a cytoSet object, and x[[i]] a cytoFrame object. The semantics is similar to the behavior of the subsetting operators for lists.

colnames, colnames<-

extract or replace the colnames slot.

phenoData, phenoData<-

extract or replace the phenoData slot.

show

display summary.

plot

Scatterplot of one or all (consecutively) cytoFrame objects. The additional argument gate can be used to plot subsets of the data defined by an object of class gate or gateSet.

hist

Draw histogram of the data. The additional argument variable can be used to subset to one variable prior to plotting.

Important note on storage and performance

The bulk of the data in a cytoSet object is stored in an environment, and is therefore not automatically copied when the cytoSet object is copied. If x is an object of class cytoSet, then the code

y <- x

will create a an object y that contains copies of the phenoData and administrative data in x, but refers to the same environment with the actual fluorescence data. See below for how to create proper copies.

The reason for this is performance. The pass-by-value semantics of function calls in R can result in numerous copies of the same data object being made in the course of a series of nested function calls. If the data object is large, this can result in a considerable cost of memory and performance. cytoSet objects are intended to contain experimental data in the order of hundreds of Megabytes, which can effectively be treated as read-only: typical tasks are the extraction of subsets and the calculation of summary statistics. This is afforded by the design of the cytoSet class: an object of that class contains a phenoData slot, some administrative information, and a reference to an environment with the fluorescence data; when it is copied, only the reference is copied, but not the potentially large set of fluorescence data themselves.

However, note that subsetting operations, such as

y <- x[i]

do create proper copies, including a copy of the appropriate part of the fluorescence data, as it should be expected. Thus, to make a proper copy of a cytoSet x, use

y <- x[seq(along=x)]

Author(s)

Florian Hahne, Wolfgang Huber http://www.ebi.ac.uk/huber

See Also

readCytoSet, cytoFrame, gate, gateSet

Examples

cset<-readCytoSet(path=system.file("extdata", package="prada"),
  pattern="[A-Z][0-9][0-9]$")
cset
pData(cset)
cset[[1]]
cset[["fas-Bcl2-plate323-04-04.A02"]]
cset["fas-Bcl2-plate323-04-04.A02"]
cset[1:3]

cset[[1]] <- exprs(cset[[1]])[1:100, ]

## Not run: 
plot(cset[2])

## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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'help.start()' for an HTML browser interface to help.
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> library(prada)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: RColorBrewer
Loading required package: grid
Loading required package: rrcov
Loading required package: robustbase

Attaching package: 'robustbase'

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

    rowMedians

Scalable Robust Estimators with High Breakdown Point (version 1.3-11)

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/prada/cytoSet-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cytoSet-class
> ### Title: 'cytoSet': a class for storing raw data from a quantitative
> ###   cell-based assay
> ### Aliases: cytoSet cytoSet-class [,cytoSet,ANY,missing,missing-method
> ###   [[,cytoSet,ANY,missing-method [[<-,cytoSet-method show,cytoSet-method
> ###   length,cytoSet-method pData,cytoSet-method colnames,cytoSet-method
> ###   colnames<-,cytoSet-method phenoData,cytoSet-method
> ###   phenoData<-,cytoSet,AnnotatedDataFrame-method
> ###   plot,cytoSet,missing-method hist,cytoSet-method
> ###   split,cytoSet,ANY,ANY-method split,cytoSet,ANY-method
> ###   split,cytoSet-method
> ### Keywords: classes
> 
> ### ** Examples
> 
> cset<-readCytoSet(path=system.file("extdata", package="prada"),
+   pattern="[A-Z][0-9][0-9]$")
> cset
cytoSet object with 6 cytoFrames and colnames
 FSC-H SSC-H FL1-H FL2-H FL3-H FL2-A FL4-H Time 
> pData(cset)
                         name
1 fas-Bcl2-plate323-04-04.A01
2 fas-Bcl2-plate323-04-04.A02
3 fas-Bcl2-plate323-04-04.A03
4 fas-Bcl2-plate323-04-04.B01
5 fas-Bcl2-plate323-04-04.B02
6 fas-Bcl2-plate323-04-04.B03
> cset[[1]]
cytoFrame object with 2115 cells and 8 observables:
FSC-H SSC-H FL1-H FL2-H FL3-H FL2-A FL4-H Time
slot 'description' has 148 elements
> cset[["fas-Bcl2-plate323-04-04.A02"]]
cytoFrame object with 2400 cells and 8 observables:
FSC-H SSC-H FL1-H FL2-H FL3-H FL2-A FL4-H Time
slot 'description' has 148 elements
> cset["fas-Bcl2-plate323-04-04.A02"]
cytoSet object with 1 cytoFrames and colnames
 FSC-H SSC-H FL1-H FL2-H FL3-H FL2-A FL4-H Time 
> cset[1:3]
cytoSet object with 3 cytoFrames and colnames
 FSC-H SSC-H FL1-H FL2-H FL3-H FL2-A FL4-H Time 
> 
> cset[[1]] <- exprs(cset[[1]])[1:100, ]
> 
> ## Not run: 
> ##D plot(cset[2])
> ## End(Not run)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>