A count‐dependent filter for smoothing flow cytometric histograms

Schuette, William H.; Shackney, Stanley E.; MacCollum, M. A.; Smith, Charles A.

doi:10.1002/cyto.990050509

Cytometry

1984

DOI: 10.1002/cyto.990050509

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A count‐dependent filter for smoothing flow cytometric histograms

William H. Schuette

Stanley E. Shackney

M. A. MacCollum

et al.

Abstract: 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 coeff… Show more

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Cited by 6 publications

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“…Both deterministic and stochastic noise may contribute to the histograms. Deterministic noise is present as a consequence of (known) unavoidable nonsystematic instrumental errors (17,21,22), whereas stochastic noise may arise from a statistically insufficient number of cells (15,16). The main purpose of analyzing FCM data is the classification and detection of homogeneous (sub)populations.…”

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Spectral analysis of flow cytometric data: Design of a special‐purpose low‐pass digital filter

1987

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Spectral decomposition of flow cytometric datafiles of arbitrary dimension reveal information of both the signal and the noise cornponents that constitute the histograms. This spectral information is used to construct a low-pass digital filter, which removes the cal parameters. high-frequency noise from the actual data. It is shown that this procedure guarantees nontrivial smoothing of the flow cytometric data in accordance with the local experimental situation. Gs a consequence optimal reconstruction of the signal is possible, which facilitates unambiguous interpretation of the data files and mathematical estimation of the statisti-

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confidence: 99%

Spectral analysis of flow cytometric data: Design of a special‐purpose low‐pass digital filter

1987

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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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Quantitative imaging of immunocytochemical (pap) estrogen receptor staining patterns in breast cancer sections

1990

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"Receptogram Analysis" has been developed as a pattern-oriented approach for predicting endocrine response in breast cancer based upon quantification of the estrogen receptor immunocytochemical assay (ERICA), using a Quantimet Imaging System. Response prediction was evaluated in 58 stage I11 and IV patients receiving endocrine therapy (primarily Tamoxifen). The Receptogram is a composite of the univariate distributions of nuclear receptor content, IOD(S), and concentration (MOD), and their bivariate contour plot; where (S) is the calculated nuclear radius in section. MOD distributions were classified into four types based upon peak modality and kurtosis (I-IV), and contour plots were classified into four subtypes (A-D) based upon contour slope. Patients failing therapy were ERICA-or their receptogram revealed co-existent ER+ and ER-tumor cells (type 11), highly skewed MOD distributions lacking defined peaks (type IV), or contours with nearly horizontal slope (type C). Response was realized in 9/16 type I patients, with a single positive MOD peak, and in 9/15 type I11 patients, with discrete, multimodal MOD peaks. In contrast, 0/8 type 11,0112 type IV, and 0/10 type C patients were responders. Receptogram analysis was superior to cytosol assay (DCC) as a response discriminant: positive predictive value, 53% vs. 33%; negative predictive value, 100% vs. 75%; sensitivity, 100% vs. 83%; specificity, 68% vs. 23%; and accuracy, 78% vs. 41%, respectively. Alternately, patients were assigned to potentially responsive or nonresponsive groups based upon thresholded mean receptor parameters: field MOD, mean nuclear MOD (NMOD), and mean NMOD(PF) where PF is the ER+ nuclear fraction. While these parameters correlated with DCC (r = .72, 0.69, and 0.69), they were only marginally better in predictive value.Key terms: Steroid receptor heterogeneity, densitometric imaging, monoclonal antibodies, human breast cancer prognosis, peroxidase-antiperoxidase, tamoxifenThe evaluation of estrogen receptor status in breast cancer is a paramount consideration that influences the prognosis and the selection of subsequent anti-estrogen therapy. The recent availability of monoclonal antibodies to estrogen receptor protein (ER) (13)(14)(15)17,18,24) has led to the development of an immunocytochemical assay (ERICA) (24) for detecting ER based upon the indirect immunoperoxidase technique (21,311. This approach provides the specificity required for sensitive detection of high-affinity type I ER (18,34) and the reaction stoichiometry for densitometric evaluation of concentration differences on a true ratio scale (32). Since the ER monoclonal antibodies bind to epitopes distal to the steroid binding site, masking of receptors by steroid or anti-steroid does not lower assay values as in the traditional dextran-coated charcoal method (DCC) (17,23,34). The methodology has revealed heterogeneity of receptor staining in breast cancer cells in many patients with ER+ cytosol assays 'This work was supported by American Cancer Society grant PDT317A (R.J.S.)...

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A count‐dependent filter for smoothing flow cytometric histograms

Cited by 6 publications

References 3 publications

Spectral analysis of flow cytometric data: Design of a special‐purpose low‐pass digital filter

Spectral analysis of flow cytometric data: Design of a special‐purpose low‐pass digital filter

Count‐Dependent filter for smoothing bivariate FCM histograms

Quantitative imaging of immunocytochemical (pap) estrogen receptor staining patterns in breast cancer sections

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