Parametric analysis of histograms measured in flow cytometry

Flow cytometric histograms frequently consist of several components that show various degrees of overlap. For many types of analysis it is of great importance to decompose the original histogram into its components. To that purpose, we investigated the maximum likelihood approach in detail. It is sh...

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Published inCytometry (New York, N.Y.) Vol. 4; no. 1; pp. 75 - 82
Main Authors Mann, R. C., Hand, R. E., Braslawsky, G. R.
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.1983
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ISSN0196-4763
1097-0320
1097-0320
DOI10.1002/cyto.990040111

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Abstract Flow cytometric histograms frequently consist of several components that show various degrees of overlap. For many types of analysis it is of great importance to decompose the original histogram into its components. To that purpose, we investigated the maximum likelihood approach in detail. It is shown that the iterative method to solve the maximum likelihood equations is well behaved for a variety of initial values. Algorithms to obtain initial values are presented, and the performance of the method is tested when applied to the analysis of DNA measurements from heterogeneous cell populations that differ with respect to DNA content.
AbstractList Flow cytometric histograms frequently consist of several components that show various degrees of overlap. For many types of analysis it is of great importance to decompose the original histogram into its components. To that purpose, we investigated the maximum likelihood approach in detail. It is shown that the iterative method to solve the maximum likelihood equations is well behaved for a variety of initial values. Algorithms to obtain initial values are presented, and the performance of the method is tested when applied to the analysis of DNA measurements from heterogeneous cell populations that differ with respect to DNA content.Flow cytometric histograms frequently consist of several components that show various degrees of overlap. For many types of analysis it is of great importance to decompose the original histogram into its components. To that purpose, we investigated the maximum likelihood approach in detail. It is shown that the iterative method to solve the maximum likelihood equations is well behaved for a variety of initial values. Algorithms to obtain initial values are presented, and the performance of the method is tested when applied to the analysis of DNA measurements from heterogeneous cell populations that differ with respect to DNA content.
Flow cytometric histograms frequently consist of several components that show various degrees of overlap. For many types of analysis it is of great importance to decompose the original histogram into its components. To that purpose, we investigated the maximum likelihood approach in detail. It is shown that the iterative method to solve the maximum likelihood equations is well behaved for a variety of initial values. Algorithms to obtain initial values are presented, and the performance of the method is tested when applied to the analysis of DNA measurements from heterogeneous cell populations that differ with respect to DNA content.
Author Hand, R. E.
Mann, R. C.
Braslawsky, G. R.
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Cites_doi 10.1002/cyto.990020305
10.1093/carcin/3.8.847
10.1093/biomet/56.3.463
10.1002/cyto.990030204
10.1109/TPAMI.1981.4767074
10.1111/j.1749-6632.1976.tb41607.x
10.1016/0010-4809(76)90006-9
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Notes This research was sponsored jointly by the Office of Health and Environmental Research, U.S. Department of Energy, under contract W‐7405‐eng‐26 with the Union Carbide Corporation, and the National Institute of Environmental Health Sciences under Interagency Agreement 40–689–78 (222‐YO1‐ES‐20041). This paper was originally presented at the Combined International Conference on Analytical Cytology and Cytometry IX and the 6th International Symposium on Flow Cytometry, Schloss Elmau, October 18–23, 1982.
R.C. Mann is a recipient of a Feodor–Lynen Fellowship from the Alexander von Humboldt–Foundation, Bonn, Federal Republic of Germany.
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Snippet Flow cytometric histograms frequently consist of several components that show various degrees of overlap. For many types of analysis it is of great importance...
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SubjectTerms Animals
Cell Cycle
Cell Line
DNA - analysis
Flow Cytometry - methods
heterogeneous cell populations
Interphase
Mathematics
Maximum likelihood estimation
Mitosis
mixture identification
Neoplasms - pathology
Probability
Rats
Trachea
Title Parametric analysis of histograms measured in flow cytometry
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