Guten Morgen Seamus, An example: Sample is stained with CD19/Fitc.So I collect 10,000 cells. On FSC v SSC can see good distinction between RBC's, dead cells, lymphs and mono's. Set a gate on the lymphs, look at FLi v FL2 with the gate on. There is a total of 638 events in this profile, 632 events fall in the NEG quadrant and 6 events in the POS quadrant which are well separated. So that is : 0.94% positives within the lymphocyte gate which equals 0.06 positives of the total population. So what I think you're saying is that the 0.06% value is not accurate due to the frequency distribution being below 50 cells. Could be, but was it the issue?? What I'm saying is that if I split the sample in two and analyse both fractions I get the same percentages for both fractions i.e good precision. Which is what Hai Qi is not getting. all the best Ann At 13:33 03.12.99 -0600, you wrote: > >Ann - -I am a little puzzled. The statistics of a 0.05% population in 10,000 >total cells is only 5 cells. Even with the best possible background, the >confidence limits of that frequency are statistically pretty poor. In the >paper I gave to Hai Qi (see corrected reference on Flow Cytometry Mailing >List), we explored this issue and showed that accurate frequency prediction >is relatively poor below about 20 cells and only really gets stable at about >50 cells. If you see something that I don't, I would really want to know! >--Jim Leary > >-----Original Message----- >From: Ann Atzberger [mailto:Ann.Atzberger@EMBL-Heidelberg.de] >Sent: Friday, December 03, 1999 7:32 AM >To: cyto-inbox >Subject: Re: rare event > > > >Hallo Hai Qi, > >if I understand correctly, you ran a total of 200 thousand cells of the >same sample through the machine twice. As you say the subpopulation is not >that rare at 0.15%. I have often analysed samples with rare events at >0.05% of the total population with good precision,(granted the signal was >way above background) and only saving 10,000 events. > >If you have a lot of debris or dead cells in the sample the %s can vary; so >you can either: 1.use a ficoll gradient to clean up the sample. >2.reset your threshold value to cut out as much debris as possible. >3.live gate. >4.Run the cells slowly through the machine. >5.Make sure the machine is clean, as bits of junk stuck in the sample probe >can effect your values. > >I might be wrong but I think saving 200,000 cells has to be more than ample >to detect this subpopulation. > >Ann >At 16:49 01.12.99 -0600, you wrote: >> >> >>It is really not too rare, a cell population about 0.04% to 0.15%, while >the >>background is 0.01-0.05%. The problem is the variation among different >>measurements, presumably due to the systematic error. For example, I had >>10e7 total cells and ran 2*10e5 out of them for two times: one time I got >>0.04% and the other time I got 0.15%. In order to have smaller variation, >>obviously I have to increase my sample size, but the last thing I want to >do >>is to run through all 10e7 cells. Can anyone give me a handy statistical >>method for estimating the sample size that makes a cost-effective >>compromise? Thank you very much. >> >>Hai Qi >>Dept. of Pathology, UTMB >> >> > >
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