summary.ProbBin.FCS {rflowcyt} | R Documentation |
This function provides summary statistics for the test of distribution difference of two samples that have been probability-binned or in histogram form.
Given two probability-binned samples, of which one will be called the stimulated sample and the other the unstimulated/control sample, the null hypothesis is that both the unstimulated/Control Data Histogram/Bins are the statistically the same as the Stimulated Data Histogram/Bins. Thus, the two samples have the same distribution in the null hypothesis.
The alternative hypothesis is that the Unstimulated/Control Data Histogram/Bins are significantly different from the Stimulated Data Histogram/Bins. Thus, the two distributions have a different distribution.
summary.ProbBin.FCS(object, verbose=FALSE,...)
object |
ProbBin.FCS object |
verbose |
Boolean whether to output all the counts in each bin |
... |
not used |
There are four main test statistics involved which are the following:
1. Test1: T.chi.unadj=max(0,(PBmetric-mean(PBmetric)) / SD(PBmetric)) is approximately standard normal (by the Central Limit Theorem (CLT)). Thus, the test of significance used the standard normal test as proposed by Mario Roederer.
2. Test2: Adjusted PB metric statistic is distributed as a chi-squared statistics. Thus, the test of significance uses the chi-squared test as proposed by Keith A. Baggerly.
3. Test3: Adjusted T.chi.unadj statistic is approximately the standard normal (by CLT). Thus the test of significance uses the standard normal test as proposed by Keith A. Baggerly.
4. Test4: Pearson's statistic using the Chi-Squared Test. There has been a suggestion of using a different number of degrees of freedom
Please note that all four tests use different statistics to test the same null hypothesis against the same alternative hypothesis.
Test 2 and 3 are ajusted forms of the statistics mentioned in Test 1.
Different p-values both one and two-sided are given for those applicable statistics.
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 |
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).
Documentation for chisq.test
.
ProbBin.FCS
, ProbBin.flowcytest
,
chisq.test
if (require(rfcdmin)){ ## obtaining the FCS objects from VRC data if ( !(is.element("unst.1829", objects()) & is.element("st.1829", objects())) ){ data(VRCmin) } IFN.gamma.1<-unst.1829@data[1:2000,4] IFN.gamma.2<-st.1829@data[1:2000,4] #Probability binning using the control dataset to determine the breaks PB1<-ProbBin.FCS(IFN.gamma.1, IFN.gamma.2, 200, varname=colnames(unst.1829@data)[4], PBspec="by.control",MY.DEBUG=FALSE) sum.PB1.1<-summary(PB1) sum.PB1.2<-summary.ProbBin.FCS(PB1) }