Optimizing transformations for flow cytometry data.

Description

This is the principal function in the package. The function takes data, a transformation function name, dimension names, and optional preselection parameter as input, and runs the maximum likelihood optimization on the data, performs the transformation, and returns the transformed data together with a list of transformation paramters for each dimension. The optimization is set to maximize the likelihood of the parameters given the data, such that the transformed data is normally distributed. An optional parameter n2f=[T,F] allows an automated preselection of an approximately bivariate normal population via the norm2Filter function. The transformation parameters are then optimized for this preselected region, and finally applied globally to the entire range of data. The optional argument parameters.only=[T,F] specifies whether to return the parameters or the transformed data.

Usage

flowTrans(dat, fun, dims, n2f,parameters.only)

Arguments

Details

The transformation functions are multivariate, common parameter transformations. The implementation utilizes a look up table to call optimization routines for the different transformations as well as optional flowCore and flowClust transformation implementations to optimize parameters and transform data from a single interface. Parameters are optimized such that the transformed data is multivariate-normal.

Value

if parameters.only = FALSE then:

If parameters.only=TRUE the returned value will be a vector of common transformation parameters: if mclMultivArcSinh is called, the returned vector will contain a, b, and c=0; if mclMultivBiexp is called, the returned vector will contain a, b, c, d, w; if mclMultivBoxCox is called, the returned vector will contain theta; if mclMultivLinLog is called, the returned vector will contain theta.

Author(s)

Greg Finak <greg.finak@ircm.qc.ca>, Raphael Gottardo <raphael.gottardo@ircm.qc.ca>

References

Finak G, Perez J M, Weng A, Gottardo R. Optimizing Transformations for Flow Cytometry. (Submitted)

Examples

data(GvHD)
result <- flowTrans(GvHD[[1]], "mclMultivArcSinh",
          colnames(GvHD[[1]])[1:2], n2f=FALSE, parameters.only=FALSE)
par(mfrow=c(1,2))
contour(GvHD[[1]][,1:2], main="Untransformed FSC vs SSC")
contour(result$result[,1:2], main="Transformed FSC vs SSC")
summary(result)

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.
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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(flowTrans)
Loading required package: flowCore
Loading required package: flowViz
Loading required package: lattice
Loading required package: flowClust
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:flowCore':

    normalize, sort

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: graph
Loading required package: RBGL
Loading required package: ellipse
Loading required package: mnormt
Loading required package: corpcor
Loading required package: clue

Attaching package: 'flowClust'

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

    box

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/flowTrans/flowTrans.Rd_%03d_medium.png", width=480, height=480)
> ### Name: flowTrans
> ### Title: Optimizing transformations for flow cytometry data.
> ### Aliases: flowTrans
> ### Keywords: models optimize
> 
> ### ** Examples
> 
> data(GvHD)
> result <- flowTrans(GvHD[[1]], "mclMultivArcSinh",
+           colnames(GvHD[[1]])[1:2], n2f=FALSE, parameters.only=FALSE)
> par(mfrow=c(1,2))
> contour(GvHD[[1]][,1:2], main="Untransformed FSC vs SSC")
> contour(result$result[,1:2], main="Transformed FSC vs SSC")
> summary(result)
Transformation: 
[1] "mclMultivArcSinh"
Transformed Results: 
flowFrame object 's5a01'
with 3420 cells and 8 observables:
      name              desc        range minRange     maxRange
$P1N FSC-H        FSC-Height     7.624613 4.787555     7.624613
$P2N SSC-H        SSC-Height     7.624613 2.644115     7.624613
$P3N FL1-H         CD15 FITC 10000.000000 1.000000 10000.000000
$P4N FL2-H           CD45 PE  9221.665909 1.000000  9221.665909
$P5N FL3-H        CD14 PerCP  1131.783715 1.000000  1131.783715
$P6N FL2-A              <NA>  1023.000000 0.000000  1023.000000
$P7N FL4-H          CD33 APC  1162.769539 1.000000  1162.769539
$P8N  Time Time (51.20 sec.)   755.000000 1.000000   755.000000
153 keywords are stored in the 'description' slot

Transformation parameters:
        a         b         c 
0.9999977 0.9999939 0.0000000 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>