Limiting dilution assays for the determination of immunocompetent cell frequencies. I. Data analysis.

1981 The Journal of Immunology 1,249 citations

Abstract

Abstract A statistical method was developed for the analysis of experimental data from limiting dilution assays. Formulas for the estimation of the frequency of immunocompetent cells within a test population were derived by the statistical methods of weighted averaging, likelihood maximization, and X2 minimization. Equations for the latter 2 were solved by Newton's method of iterative approximation. Estimates obtained by these methods were found to be more valid than those obtained by least squares (LS) fitting as judged by the X2 test and as established by Monte Carlo experiments. X2 minimization was chosen as the preferable estimation method with maximum accuracy and precision (minimum bias and variance) for the standard determination of frequencies; likelihood maximization was used only for the confirmation of results. When data from previously published experiments were reanalyzed, both results and conclusions were found to differ significantly from those originally obtained by LS fitting, thus demonstrating the importance of using proper data analysis methods. In conjunction with the use of available calculators or microcomputers, the method presented here provides a simple and rapid procedure for the valid determination of immunocompetent cell frequencies.

Keywords

MaximizationMinificationExpectation–maximization algorithmMathematicsStatisticsLimitingMonte Carlo methodApplied mathematicsLeast-squares function approximationAlgorithmStandard errorComputer scienceMaximum likelihoodMathematical optimizationEstimator

MeSH Terms

ImmunityCellularIndicator Dilution TechniquesStatistics as Topic

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Publication Info

Year
1981
Type
article
Volume
126
Issue
4
Pages
1614-1619
Citations
1249
Access
Closed

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Cite This

Carl Taswell (1981). Limiting dilution assays for the determination of immunocompetent cell frequencies. I. Data analysis.. The Journal of Immunology , 126 (4) , 1614-1619. https://doi.org/10.4049/jimmunol.126.4.1614

Identifiers

DOI
10.4049/jimmunol.126.4.1614
PMID
7009746

Data Quality

Data completeness: 86%