I would absolutely concur with Maryalice, for interpreting both research data and clinical diagnoses; ...I use an adage I borrowed from the U.S.M.C. - 'all i need are a few good dots', with the appropriate cautions Maryalice nicely summarized below. F Preffer At 10:17 AM 12/06/1999 -0400, Maryalice Stetler-Stevenson wrote: > >Actually, 5 cells can mean something in an analysis, although one has to be >cautious. If you are just setting quadrants or gates based upon isotype >controls, then it is not meaningful. With distictly abnormal tumor cells, >you can have a pattern of staining that is far removed from background. >Furthermore there is a cluster, not a spread, and this cluster is seen in >multiple parameter analysis, including size and side scatter. I most often >seen this with Hairy cell leukemia.If 1)the cells are clustering in an >abnormally bright pattern for CD20 and CD22 and positive for kappa but >negative for lambda, 2) lying between normal lymphs and monocytes on FSC >vrs SSC 3) and others tubes are consistent-e.g. Bright CD25+, Bright >CD11c+, Bright CD20 and CD103+ population that forms a nice cluster,this is >detection of tumor in a patient with a history of hairy cell leukemia >matching this immunophenotype. It is the homogeneous, tight clustering >pattern over multiple parameters that allows one to see that this is not >background. This is not generally applicable, but does apply in some >clinical specimens. I still would cautiously word a written diagnosis on >such a case but would verbally convey my impression to the clinician. Now >this may not pertain to the discussion at hand, but I felt chatty this >morning. > > Maryalice > >>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 >>> >>> > >Maryalice Stetler-Stevenson >Director Flow Cytometry Unit >Laboratory of Pathology, NCI, NIH > > ````````````````````````````````````````````````` Frederic I. Preffer preffer@helix.mgh.harvard.edu Department of Pathology- Room 7140 149 13th Street Massachusetts General Hospital-East Charlestown, MA 02129 v(617) 726-7481 fax (617) 724-3164 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
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