James, what my researchers are using are overlays of the two histograms for a picture and then a statistical comparison of the geometric means of the two populations, usually a simple "flourescence intensity of population B is n times greater than that of population A." Sometimes it gets a bit more complicated when each population has its own negative that needs to be subtracted, but I first do subtracted overlays for the individual populations and then overlay the subtracted peaks to compare the two and use the geometric means of the subracted peaks for statistics. This is all done in CellQuest on a FACSCalibur. I hope this helps. (Geometric mean is important to use for logrithmically acquired data.) Candace Enockson Medical University of South Carolina --On Tue, Oct 12, 1999 5:32 PM -0400 James Liou <jliou@bu.edu> wrote: > > Hi all, > > I am also interested in determining statistical difference > between the histogram distribution of two samples (i.e. -/+ Rx), > but find this K-S stuff very confusing. My simple question is: Is it > possible to just do the experiment 3 independent times and use basic > standard deviation/t-test statistics? If so, does one use the mean > fluorescent intensity or peak fluorescent intensity from the experiments > for calculating the difference? Thanks for any/all answers and help. > > Sincerely, > > James Liou > > > > > At 12:38 PM 10/12/99 +0100, Ulrik Sprogøe-Jakobsen wrote: >> >> My two cents... >> >> The use of K-S or other appropriate statistics to the comparison of two >> histograms (e.g. sample A versus sample B) will reveal the true >> statistical difference (i.e. either a p-value or a confidence interval). >> Given the high number of events (typically 5 - 10,000 or more), the >> uncertainty of the mean (aka. standard error of the mean) is very very >> small, even though the distribution of each histogram is broad and >> overlapping. Consequently, even the smallest difference in mean >> arbitrary fluorescence of sample A versus sample B, is perceived as >> statistically significant. Therefore, as reported to this list, running >> the same sample twice, will often lead to statistical significance when >> comparing results of the two runs by the K-S test. >> >> However, this is not what we really want to know !!! >> >> What we want to know is: as evaluated by measurement of fluorescence, >> does sample A belong to population of cells (or individuals) different >> from that of sample B ? >> >> To answer this question we have to know the variation in mean (geometric >> mean or median) fluorescence when running the same sample multiple times >> on the flow cytometer. Next, to compare the difference of mean >> fluorescence of sample A and sample B with this uncertainty of the mean. >> If the former is bigger than the latter, it may be concluded that sample >> A and sample B belong to two different populations of cells. Formally, >> this may be expressed as: >> >> Mean fluoresc. sample A - mean fluoresc. sample B > SQRoot 2 x standard >> error of the mean (of fluorescence measurement) >> >> To conclude, the relevant comparison is not that of 2 means from 2 >> distributions of events (each from a single run), but the comparison of 2 >> means from 2 distributions of means (from multiple runs). In the latter >> case, knowledge of a general uncertainty of measurement of mean >> fluorescence (CV% or standard deviation), may substitute for the repeat >> measuring of the 2 samples, according to the abovementioned formula. >> >> Please note: The above does not take into account the variations >> introduced by staining, lysing, washing, etc, only the crude variation >> in fluorescence measurement by the flow cytometer. >> >> >> Please comment, >> >> >> Ulrik Sprogoe-Jakobsen, M.D. >> >> Dept. Clinical Immunology >> Odense University Hospital >> 5000 Denmark >> >> E-mail: ulrik.sprogoe-jakobsen@ouh.dk > > > >
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