I think you should consider reading the probability binning papers, published sep 2001 (vol 45) in cytometry. Jan Saverio Alberti wrote: > > 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 > > > > > > -- Jan Bayer, Ph.D. Fondation Jean Dausset - CEPH Email : bayer@cephb.fr 27, Rue Juliette Dodu Tel : +33 1 53 72 51 14 75010 Paris, France Fax : +33 1 53 72 51 28
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