A fresh look at FC data analysis is welcome. I must say I enjoyed the comments.. However, one should not forget to read what others have done for a few decades.. A long discussion on KS statistics has gone on in the past, maybe the participants would like to comment. Only two points: mathematical modeling of "difficult" distributions (a few dimly expressing cells and a majority of non-expressing ones) can be effectively performed (1, 2). Whether or not these and similar papers have been well understood and/or put to practice is a different issue.. Furthermore, to perform statistics on populations of expressing cells (positives) that are clearly separated from non-expressing cells (negatives) is very different from when these are merged in a continuous distribution (3, 4). Obviously, the word 'positive' in the first instance isn't arbitrary at all, and '% positive' may be a rather concise and meaningful way to describe the sample in a yes-or-no situation, e.g. gene transfection. In case number two the capacity to go back to case number one, whether mathematically (1, 2) or electronically (3, 4), is a distinct advantage.. Saverio 1. Lampariello, F. Evaluation of the number of positive cells from flow cytometric immunoassays by mathematical modeling of cellular autofluorescence, Cytometry. 15: 294-301, 1994. 2. Lampariello, F. and Aiello, A. Complete mathematical modeling method for the analysis of immunofluorescence distributions composed of negative and weakly positive cells, Cytometry. 32: 241-254, 1998. 3. Alberti, S., Parks, D. R., and Herzenberg, L. A. A single laser method for subtraction of cell autofluorescence in flow cytometry, Cytometry. 8: 114-9, 1987. 4. Alberti, S., Bucci, C., Fornaro, M., Robotti, A., and Stella, M. Immunofluorescence analysis in flow cytometry: better selection of antibody-labeled cells after fluorescence overcompensation in the red channel, J. Histochem. Cytochem. 39: 701-6, 1991. Saverio Alberti Head, Lab. of Experimental Oncology Department of Cell Biology and Oncology Consorzio Mario Negri Sud 66030 Santa Maria Imbaro (Chieti), Italy Phone: (39-0872) 570.293 FAX: (39-0872) 570.412 E-mail: alberti@negrisud.it On Thu, 23 May 2002, Sergey Dzekunov wrote: > I am a new member of the discussion group and would like to address a few > questions of general nature. > > 1) I have noticed in the previous messages that when talking about > descriptive statistics of FC data, > some people refer to the Kolmogorov-Smirnov test as the tool to determine > which distribution better > describes a data set. I am not sure about specific implementations of this > test, but am afraid that it > applies to continuous distribution functions, whereas binned distributions > should be challenged by > the Chi-square test for the same purpose. > > 2) Another common task is comparison of distribution means. To generalize on > the first comment, I'd like > to share with the group the following logistics that I was glad to find in > this famous book: "Numerical recipes in C. The art > of scientific computing." Second edition, Cambridge U. press, 1988-1992, > ISBN 0521431085 > Here is the scheme: > Q: Do two samples have different means? > A: Prior to comparing the averages, one should do the following > Step 1: Run Chi-square test to see if the two distributions are different. > "No" -- go to step 2. "Yes" -- go to step 5. > Step 2: F-test to find if the two data sets have the same variances. > "Yes" -- go to step 3. "No" -- go to step 4. > Step 3: t-test to see if two samples have the same means (or significantly > different ones). > Done. > Step 4: Use the Unequal-variance t-test, but be careful with the > distributions which are > substantially different in shape. With this in mind, consider it done. > Step 5: This situation is very likely to be identical to the "peanuts and > oranges" one. > Since the distributions are different, the conclusions about any > differences in their means > may be quite speculative. > > 3) This part refers to the procedure of determining "percent positive" > cells, which I think everyone > working with flow cytometry has to deal with at least sometimes. Although > there is little if any of statistical > power in this parameter, it is widely used in validation and comparison of > assays and is almost universal > in reporting results of gene transfection/expression. > The "Percent positive" is determined as the number of cells above a > threshold arbitrarily established on a > control distribution. Such thresholding has no ability to distinguish > between the cells that have increased > their fluorescence either by one percent of ten-fold -- as long as both have > crossed the threshold. > Nevertheless, given the popularity and simplicity of this parameter, it > would be worthwhile to standardize > it in some fashion. I have put together a simple algorithm that is capable > of computing a single number for > "percent positive" which is immune to the influence from the user. However, > prior to splitting hairs > on others and posting this algorithm, I would like to know if someone has > already tried to solve the same problem. > What does the discussion group think about it? Has any FC society ever > posted some guidelines on certain > algorithms and specifically thresholding? > > I highly regard flow cytometry as an "intuitive" and "artistic" technique > which has as much power as a researcher > is capable to make use of -- I must explicitly say this in respect of > talented scientists who think far and wide > when looking at the data. However, as a biophysicist I can't help it but try > to bring more sense into the numbers > as long as those are such a big part of our scientific language :) > I sincerely wish that articles like this one appeared more often: > Durand R.E. Calibration of Flow Cytometer Detection Systems. Methods in Cell > Biology, Acad.Press 1990, Vol.33 p.647 > > Sincerely, > Sergey M. Dzekunov > MaxCyte, Inc. Rockville, MD > > >
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