RE: rare event

From: Frederic Preffer (preffer@helix.mgh.harvard.edu)
Date: Tue Dec 07 1999 - 16:29:19 EST


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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