I had trouble sending this email the first time so sorry about any duplicates: Michelle - Howard Shapiro wrote this email about a year ago and it was so good of a tutorial that I kept it and attached it in this email. maciej --- Howard Shapiro <hms@shapirolab.com> wrote: > Jim Houston wrote- > > >Those of us doing CD34 enumeration are routinely asked to give > results > >sometimes as low as .1%. In fact normal adults have a measurable > percentage > >as low as .02%. When I first started CD34 enumeration 5 years ago > I was > >told you had to have at least 50 of these positive events to be > significant. > >Needless to say we acquire alot of data to get these numbers. > > > >20 years ago I believed the error by flow to be around 2-5%. If > you run > >the same tube 10 times you will get some variance in the > percentage you are > >looking for, particularly in the levels of .1%. The problem > occurring is > >the significance of an increase from a measured .2% to a .4% > level. Is this > >increase real or is it a factor of the instrument. When this > number is used > >to calculate Absolute numbers in a leukopheresis product then it > can be > >significant. > > > >Now I have been requested to give numbers lower than .01%. How to > make this > >accurate? Good question. I assume that most populations have > some inherent > >properties to them. They scatter light in discrete patterns and > have some > >sort of phenotype unique to them. The more parameters I use the > better the > >confidence level. If I set all analysis markers by isotype > controls then > >that could make my decision easier, but these give erroneous > results if you > >look at the data carefully. If you collect 500,000 cells and then > see a > >population of 16 are these real???? > > > >In practice the low %'s <1% are at best sometimes subjective to > the > >operator's experience. > > > >A good question is not only the ability by flow to give these low > %'s but > >how reproducible is it. Some labs are running their samples in > doublets or > >triplets then averaging. This will drive the cost up a bit. > > > >Are there any statisticians out there who can answer some of these > >questions? > > As I pointed out in an earlier posting on rare event analysis, it > is > possible, under the right circumstances, to detect one cell in 10 > million. > > The question Jim has asked, though, refers to the accuracy and > precision of > estimating counts of rare events, in this case, the number and/or > percentage of CD34+ cells. > > When anything is being counted, Poisson statistics come into play; > if you > count n of anything, the standard deviation will be the square root > of > n. The coefficient of variation (CV), in per cent, will be 100 > divided by > the square root of n. This assumes that the counting process > itself is > perfect; the point is that if you count 25 objects, you'll get a > standard > deviation of five objects, and a CV of 100/5, or 20 per cent; if > you count > 100 objects, the standard deviation will be 10, and the CV will be > 10 per > cent, and it is easy to see that getting precision to 1 per cent > requires > that you count 10,000 objects. If these objects are rare cell > types > present at a frequency of one per million cells in the sample, you > have to > analyze 10 billion cells to find the 10,000 cells you're interested > in. > > The same statistics apply to counting everything from photons and > photoelectrons (peaks from dimly fluorescent cells have bigger CV's > than > peaks from brightly fluorescent cells because fewer photons are > collected > from the dim cells) to votes (if 3,000,000 votes are counted, one > expects a > standard deviation of 1,732 votes, or roughly 6 parts in 10,000, > meaning > that if the process is supposedly only 99.9% reliable, or accurate > (10 > parts in 10,000), as it was widely stated to be, neither Bush nor > Gore has > a strong claim to having won the Florida Presidential vote). > > We have a little more control over cell counting than over vote > counting. If you count enough cells, you can accurately > discriminate > between, say, .01% and .02%. If you only count 10,000 cells total, > you'd > expect to find one cell (and a CV of 100%) in the sample with .01% > and 2 > cells (CV of 70.7%) in the sample with .02%; so 10,000 cells total > is too > small a sample to let you discriminate. If you count 1,000,000 > cells > total, you end up with 100 cells in the .01% sample (CV 10%) and > 200 cells > (7.1% CV) in the .02% sample, and this difference will be > statistically > significant. > > The best way to do counts - although almost nobody does them this > way - is > to always count the same number of cells of interest, which gives > you equal > precision no matter what the value is. Normally, we do absolute > counts by > analyzing a fixed volume of blood (or other sample) and percentage > counts > by analyzing a fixed number of cells. The alternative is to decide > on the > level of precision you want - suppose it is 5%. Then you have to > count 400 > cells (the square root of 400 is 20, and 100/20 = 5). What you do > is > measure the volume of sample (in the case of absolute counts), or > the total > number of cells (in the case of percentage counts), which has to be > analyzed to yield 400 of the cells of interest. If the cells of > interest > are at .01%,, you'll have to count 4,000,000 cells total to find > your 400 > cells of interest; if they are at 1%, you'll only have to count > 40,000 > cells, but, instead of the .01% value being much less precise than > the 1% > value, both will have the same 5% precision. The down side of this > is that > it requires some reprogramming of the apparatus, and possibly uses > more > reagent, but, if you want good numbers, there is simply no > alternative. > > -Howard > > __________________________________________________ Do You Yahoo!? Make a great connection at Yahoo! Personals. http://personals.yahoo.com
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