ProbBin.flowcytest {rflowcyt} | R Documentation |
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.
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"), ...)
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 |
The testing performed are summarized in summary.ProbBin.FCS
,
and the plots are produced by plot.ProbBin.FCS
.
A list consisting of:
PBinType |
Type of Probability Binning:
|
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.
Usually the FCS object is gated and subset prior to this testing and analysis.
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.
A.J. Rossini and J.Y. Wan
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).
pkci2.flowcytest
,
WLR.flowcytest
, KS.flowcytest
,
runflowcytests
, summary.ProbBin.FCS
,
ProbBin.FCS
, plot.ProbBin.FCS
, hist
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. }