Complete mathematical modeling method for the analysis of immunofluorescence distributions composed of negative and weakly positive cells.
In a recent paper (Lampariello: Cytometry 15:294-301, 1994), we proposed a method for the automated evaluation of the percentage of positive cells from flow cytometric immunofluorescence histograms. The method is based on a suitable mathematical representation of the control histogram, which is used to identify the negative cell distribution in the test histogram. In this paper we present an improvement of the previous method, where we assume that the positive cell distribution in the test can also be modeled making use of an empirical distribution of the same kind as employed for modeling the control. The parameters of this distribution are estimated directly from the test. In this way, a mathematical representation of the whole test distribution is calculated without having to set up a purely positive control. In order to evaluate the accuracy of the method in the determination of the positive percentage, we carried out a set of measurements of double-labeled and suitably treated cells, mixed in different ratios with control cells, and from each sample we obtained histograms with overlapped and well-separated positive and negative distributions. These last histograms allow us to determine the actual positive percentages and thus to evaluate the performance of the analysis method applied to the histograms with overlapped distributions.
Lampariello
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Instituto di Analisi dei Sistemi ed Informatica del CNR, Rome, Italy. lampariello@iasi.rm.cnr.it
Aiello
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D000465:Algorithms; D015703:Antigens, CD / Q000032:analysis; D018952:Antigens, CD34 / Q000032:analysis; D015214:Antigens, Differentiation, Myelomonocytic / Q000032:analysis; D015415:Biomarkers / Q000032:analysis*; D005434:Flow Cytometry / Q000379:methods / Q000706:statistics & numerical data*; D005455:Fluorescent Antibody Technique / Q000706:statistics & numerical data*; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D063268:Sialic Acid Binding Ig-like Lectin 3; D014407:Tumor Cells, Cultured
D015703:Antigens, CD; D018952:Antigens, CD34; D015214:Antigens, Differentiation, Myelomonocytic; D015415:Biomarkers; C571163:CD33 protein, human; D063268:Sialic Acid Binding Ig-like Lectin 3