ProbBin.flowcytest {rflowcyt}R Documentation

Test the equivalence of two univariate sample distributions by using Probability Binning and plots the probability-binned histograms of the two samples

Description

This function will create a probability binning object called ProbBin.FCS and will perform summary statistics and a plot of the two resulting probability-binned histograms. There can be probability binning based on the combined data of the two samples or just based on one sample, which is labled as the control.

Usage

ProbBin.flowcytest(controldata,
   stimuldata, N = 100, varname = "",
   AnalyType = c("combined", "by.control"),
   title = "",
   MY.DEBUG = FALSE,
   PBobj.plotted=TRUE,
   plots.made=c("both", "stimulated", "unstimulated"),
   ...)

Arguments

controldata numerical vector of the control sample univariate data
stimuldata numerical vector of the stimulated sample of the univariate data
N The nummber of observations in each bin on the data specified in the AnalyType option
varname character string of the variable being investigated (usually, in this analysis, the interferon gamma variable is used after gating and subsetting of the FCS object)
AnalyType Probability Binning either "by.control" or based on the "combined" (control and stimulated) data
title character string denoting the title of the plots
MY.DEBUG boolean; if TRUE, debugging statements are printed; default is FALSE
PBobj.plotted boolean; if TRUE then histograms of the ProbBin.FCS object will be plotted; if FALSE, then these plots are surpressed; default is TRUE
plots.made character string denoting which histogram plot should be displayed; default is "both"
... more plotting options; see plot.ProbBin.FCS and hist for details

Details

The testing performed are summarized in summary.ProbBin.FCS, and the plots are produced by plot.ProbBin.FCS.

Value

A list consisting of:

PBinType Type of Probability Binning:
"by.control"
uses the control dataset to obtain the breaks/cutoffs to bin the stimulated dataset given a certain number of observations in each bin of the control dataset
"combined"
uses the combined dataset (both control and stimulated datasets) to obtain the breaks/cutoffs for the bins given a certain number in each bin
control.bins single column matrix of the counts in each bin of the control dataset
stim.bins single column matrix of the counts in each bin of the stimulated dataset
total.control numeric; total number in the control dataset
total.stim numeric; total number in the stimulated dataset
T.chi.unadj Roederer's unadjusted normalized PB metric statistic which is normalized by subtracting off the mean and then dividing by the standard deviation. This statistic is approximately standard normal.
p.val.2tail.z.unadj Two-tailed standard normal p-value corresponding to the Roederer's unadjusted normalized PB metric statistic which is approximated as a standard normal
p.val.1tail.z.unadj Upper standard normal one-tailed p-value corresponding to the Roederer's unadjusted PB metric statistic which is approximated as a standard normal
PBmetric.unadj Roederer's unadjusted PB metric which is ((n.c + n.s)/(2*nc.*n.s))*Chi-squared or an unadjusted chi-squared statistic, where n.c is the number of control observations (unbinned) and n.s is the number of stimulated observations (unbinned)
PBmetric.adj Baggerly's adjusted PB metric statistic which is a Chi-squared statistic
PB.df The degrees of freedom of the PB metric (adjusted and unadjusted) which is B-1, where B is the number of bins in the eitherthe control or the stimulated binned data
p.val.1tail.chi.adj Upper one-tailed chi-squared p-value corresponding to Baggerly's adjusted PB metric
T.chi.adj Baggerly's PB metric which is normalized by subtracting off the mean and dividing by the standard deviation; This normalized statistic is approximately standard normal.
p.val.1tail.z.adj Upper one-tailed standard normal p-value corresponding to the Baggerly's adjusted normalized PB metric statistic which is approximated as a standard normal
p.val.2tail.z.adj Standard normal two-tailed p-value corresponding to the Baggerly's adjusted PB metric statistic which is approximated as a standard normal
pearson.stat Pearson's Chi-Squared Statistic with degrees of freedom 2B-1, where B is the number of bins in either the control or the stimulated binned data
pearson.df the degrees of freedom for the chi-squared statistic
pearson.p.value The p-value corresponding to the chi-squared distribution
pearson.method string of the indicating the type of test and options performed
pearson.dataname string of the name(s) of the data
pearson.observed a vector of the observed counts
pearson.expected a vector of the expected counts under the null hypothesis
pearson.p.val.PB.df Fisher's Chi-squared statistic with degrees of freedom B-1, where B is the number of bins in either the control or the stimulated binned data


Two histograms, one of each sample, are also plotted.

WARNING

Usually the FCS object is gated and subset prior to this testing and analysis.

Note

Other flowcytests are available such as pkci2.flowcytest, ProbBin.flowcytest, KS.flowcytest, which test the equivalence of two sample distributions. Generally, comparing the control and stimulated samples of the interferon gamma variable is of interest.

Author(s)

A.J. Rossini and J.Y. Wan

References

Keith A. Baggerly "Probability Binning and Test Agreement between Multivariate Immunofluorescence Histograms: Extending the Chi-Squared test" Cytometry 45: 141:150 (2001).

Mario Roederer, et al. "Probability Binning Comparison: A Metric for Quantitating Univariate Distribution Differences" Cytometry 45:37-46 (2001).

See Also

pkci2.flowcytest, WLR.flowcytest, KS.flowcytest, runflowcytests, summary.ProbBin.FCS, ProbBin.FCS, plot.ProbBin.FCS, hist

Examples


if (require(rfcdmin)){

data.there<-is.element(c("st.1829", "unst.1829", "st.DRT", "unst.DRT"),objects())
if ( ( sum(data.there) != length(data.there) )){
## obtaining the FCS objects from VRC data
data(VRCmin)
}

## This only serves as an example.  Usually the FCS object is
## gated and then subset

## HIV negative individual 1829
  IFN.control<-unst.1829@data[1:2000,4]
  IFN.stimul<-st.1829@data[1:2000,4]

## probability binning based on the combined data of both samples
if (interactive()==TRUE){
par(mfrow=c(2,2))
test1.out<-ProbBin.flowcytest(IFN.control, IFN.stimul, varname="Interferon Gamma",
AnalyType="combined", N=200, title="HIV negative individual 1829")
}
## HIV positive individual DRT
  IFN.control2<-unst.DRT@data[1:2000,4]
  IFN.stimul2<-st.DRT@data[1:2000,4]

## probability binning based on the control data only
if (interactive()==TRUE){
test2.out<-ProbBin.flowcytest(IFN.control2, IFN.stimul2,
varname="Interferon Gamma", AnalyType="by.control",
N=100, title="HIV negative individual 1829")
}
## This is an artifical example, but one would expect the
## distributions of the stimulated and control samples
## to be the same in the HIV negative individual 1829
## and to be different in the HIV positive individual DRT
## The test in this example is a bit contrived but
## the bigger picture is achieved.
}


[Package rflowcyt version 1.4.0 Index]