Mathematical Analysis of Dna Distributions Derived From Flow Microfluorometry

Dean, Phillip N.; Jett, James H.

doi:10.1083/jcb.60.2.523

Cited by 748 publications

(266 citation statements)

References 5 publications

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“…The average number of evaluated nuclei per specimen was 20.000 and the number of nuclei scanned was 120/s. DNA histogram derived from flow cytometry was obtained with a computer program for Dean and Jett mathematical analysis (Dean and Jett 1974). Data analysis was conducted using DNA analysis program MODFIT (Verity Software House, Inc., PO Box 247, Topsham, ME, 04086 USA, version: 2.0 powers Mac with 131,072 kb Registration No: 42000960827-16193213 Date made: 16-September, 1996).…”

Section: Flow Cytometric Analysismentioning

confidence: 99%

Mentha piperita as a pivotal neuro-protective agent against gamma irradiation induced DNA fragmentation and apoptosis

2012

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Section: Flow Cytometric Analysismentioning

confidence: 99%

Mentha piperita as a pivotal neuro-protective agent against gamma irradiation induced DNA fragmentation and apoptosis

2012

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“…The PSF usually employed for FCM is a Gaussian family using a constant coefficient of variation, one for each channel (3).…”

Section: Methodsmentioning

confidence: 99%

A count‐dependent filter for smoothing flow cytometric histograms

et al. 1984

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An adaptive count-dependent algorithm for smoothing statistically limited histograms has been developed. It considers both the spatial frequency limitations of the measurement system (described by the measurement system point spread function) and the reliability of the measured data (indicated by the effective number of counts influencing each channel of the histogram.Windows for smoothing flow cytometric histograms are derived from an assumed Gaussain-shaped point spread function (PSF) with a constant coefficient of variation. The windows are developed by scaling the variances of the Gaussian functions inversely with the statistical reliability of the data contained in each channel of the measured histogram.The reliability of this data is determined by taking the square root of the number of counts influencing the value tabulated for each channel.Using the algorithm, a smoothed version of the measured histogram may be developed from a linear sum of the products of the individual scaled Gaussian functions and the original measured histogram.Data are presented demonstrating the advantages of count-dependent smoothing over non-count-dependent smoothing using synthesized DNA histograms as a function of sample size.

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“…Flow cytometric measurement distortions for singleparameter systems are described by the system measurement PSF, which is usually assumed to be a set of Gaussian functions with a constant coefficient of variation (CV) (2). These functions may be approximated in discreet form by means of the normal distribution equation.…”

Section: Key Terms: Smoothing Flow Cytometry Histogrammentioning

confidence: 99%

“…This two-dimensional relative PSF will be developed as a set of 9-by-9 matrices centered over each element of the measured distribution, These Gaussian-shaped functions with a peak value of 1 are generated for the X-axis axes by rewriting equation (1) with a unity scaling coefficient to the left of the exponential term and performing the substitution called for in equation (2).…”

Section: Relative Psfmentioning

confidence: 99%

Count‐Dependent filter for smoothing bivariate FCM histograms

1986

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A data‐smoothing filter has been developed that permits the improvement in accuracy of individual elements of a bivariate flow cytometry (FCM) histogram by making use of data from adjacent elements, a knowledge of the two‐dimensional measurement system point spread function (PSF), and the local count density. For FCM data, the PSF is assumed to be a set of two‐dimensional Gaussian functions with a constant coefficient of variation for each axis. A set of space variant smoothing kernels are developed from the basic PSF by adjusting the orthogonal standard deviations of each Gaussian smoothing kernel according to the local count density. This adjustment in kernel size matches the degree of smoothing to the local reliability of the data. When the count density is high, a small kernel is sufficient. When the density is low, however, a broader kernel should be used. The local count density is taken from a region defined by the measurement PSF. The smoothing algorithm permits the reduction in statistical fluctuations present in bivariate FCM histograms due to the low count densities often encountered in some elements. This reduction in high‐frequency spatial noise aids in the visual interpretation of the data. Additionally, by making more efficient use of smaller samples, systematic errors due to system drift may be minimized.

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Mathematical Analysis of Dna Distributions Derived From Flow Microfluorometry

Cited by 748 publications

References 5 publications

Mentha piperita as a pivotal neuro-protective agent against gamma irradiation induced DNA fragmentation and apoptosis

Mentha piperita as a pivotal neuro-protective agent against gamma irradiation induced DNA fragmentation and apoptosis

A count‐dependent filter for smoothing flow cytometric histograms

Count‐Dependent filter for smoothing bivariate FCM histograms

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