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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