Multi-set Pre-processing of Multicolor Flow Cytometry Data

Abstract Flow Cytometry is an analytical technology to simultaneously measure multiple markers per single cell. Ten thousands to millions of single cells can be measured per sample and each sample may contain a different number of cells. All samples may be bundled together, leading to a ‘multi-set’ structure. Many multivariate methods have been developed for Flow Cytometry data but none of them considers this structure in their quantitative handling of the data. The standard pre-processing used by existing multivariate methods provides models mainly influenced by the samples with more cells, while such a model should provide a balanced view of the biomedical information within all measurements. We propose an alternative ‘multi-set’ preprocessing that corrects for the difference in number of cells measured, balancing the relative importance of each multi-cell sample in the data while using all data collected from these expensive analyses. Moreover, one case example shows how multi-set pre-processing may benefit removal of undesired measurement-to-measurement variability and another where class-based multi-set pre-processing enhances the studied response upon comparison to the control reference samples. Our results show that adjusting data analysis algorithms to consider this multi-set structure may greatly benefit immunological insight and classification performance of Flow Cytometry data.

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Features of lymphocytes during programmed cell death in patients with atopic bronchial asthma

Rossiiskii Allergologicheskii ZHurnal ◽

10.36691/rja870 ◽

2009 ◽

Vol 7 (4) ◽

pp. 32-39

Author(s):

Y V Pinchuk ◽

A C Vodunon ◽

I G Mustafin ◽

Z I Abramova

Keyword(s):

Flow Cytometry ◽

Cell Death ◽

Programmed Cell Death ◽

Morphological Difference ◽

Disease Severity Score ◽

Electronic Microscopy ◽

Disease Pathogenesis ◽

Atopic Bronchial Asthma ◽

The Difference ◽

Number Of Cells

Background. To establish features of programmed cell death of lymphocytes depending on disease severity score. Methods. The morphology of lymphocytes was investigated with a method of electronic microscopy. Apoptotic cells were defined by flow cytometry. Results. We have revealed lymphocytes morphological difference between asthmatics and normal donors. Also we found out the difference between number of cells during the incubation process. Conclusion. These findings can promote deeper understanding of the disease pathogenesis.

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Automatic Clustering of Flow Cytometry Data with Density-Based Merging

Advances in Bioinformatics ◽

10.1155/2009/686759 ◽

2009 ◽

Vol 2009 ◽

pp. 1-7 ◽

Cited By ~ 27

Author(s):

Guenther Walther ◽

Noah Zimmerman ◽

Wayne Moore ◽

David Parks ◽

Stephen Meehan ◽

...

Keyword(s):

Flow Cytometry ◽

Parametric Model ◽

Two Dimensional ◽

Laboratory Setting ◽

Flow Cytometry Data ◽

Current Limit ◽

Automatic Clustering ◽

Automated Tools ◽

A Current ◽

Number Of Cells

The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

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Managing Multi-center Flow Cytometry Data for Immune Monitoring

Cancer Informatics ◽

10.4137/cin.s16346 ◽

2014 ◽

Vol 13s7 ◽

pp. CIN.S16346 ◽

Cited By ~ 3

Author(s):

Scott White ◽

Karoline Laske ◽

Marij J.P. Welters ◽

Nicole Bidmon ◽

Sjoerd H. Van Der Burg ◽

...

Keyword(s):

Flow Cytometry ◽

Large Scale ◽

Single Cells ◽

Large Data ◽

Automated Analysis ◽

Information Management System ◽

Immune Monitoring ◽

Quality Data ◽

High Quality ◽

Flow Cytometry Data

With the recent results of promising cancer vaccines and immunotherapy 1 – 5 , immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a rate of tens of thousands of cells per second. Given the complexity of flow cytometry assays, reproducibility is a major concern, especially for multi-center studies. A promising approach for improving reproducibility is the use of automated analysis borrowing from statistics, machine learning and information visualization 21 – 23 , as these methods directly address the subjectivity, operator-dependence, labor-intensive and low fidelity of manual analysis. However, it is quite time-consuming to investigate and test new automated analysis techniques on large data sets without some centralized information management system. For large-scale automated analysis to be practical, the presence of consistent and high-quality data linked to the raw FCS files is indispensable. In particular, the use of machine-readable standard vocabularies to characterize channel metadata is essential when constructing analytic pipelines to avoid errors in processing, analysis and interpretation of results. For automation, this high-quality metadata needs to be programmatically accessible, implying the need for a consistent Application Programming Interface (API). In this manuscript, we propose that upfront time spent normalizing flow cytometry data to conform to carefully designed data models enables automated analysis, potentially saving time in the long run. The ReFlow informatics framework was developed to address these data management challenges.

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Understanding the contribution of operator measurement variability within Flow Cytometry data analysis for Quality Control of Cell and Gene Therapy manufacturing

Measurement ◽

10.1016/j.measurement.2019.106998 ◽

2020 ◽

Vol 150 ◽

pp. 106998

Author(s):

Rebecca Grant ◽

Karen Coopman ◽

Nicholas Medcalf ◽

Sandro Silva-Gomes ◽

Jonathan J. Campbell ◽

...

Keyword(s):

Gene Therapy ◽

Flow Cytometry ◽

Quality Control ◽

Data Analysis ◽

Measurement Variability ◽

Flow Cytometry Data ◽

Cell And Gene Therapy

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Comparison of bromodeoxyuridine labeling indices obtained from tissue sections and flow cytometry of brain tumors

Journal of Neurosurgery ◽

10.3171/jns.1988.68.3.0388 ◽

1988 ◽

Vol 68 (3) ◽

pp. 388-392 ◽

Cited By ~ 24

Author(s):

Tadashi Nagashima ◽

Takao Hoshino ◽

Kyung G. Cho ◽

Morris Senegor ◽

Frederick Waldman ◽

...

Keyword(s):

Monoclonal Antibody ◽

Flow Cytometry ◽

Brain Tumors ◽

Deoxyribonucleic Acid ◽

Single Cells ◽

Fluorescein Isothiocyanate ◽

Content Type ◽

Treatment Regimens ◽

Tissue Sections ◽

The Difference

Sixteen patients with brain tumors were given a 30- to 60-minute intravenous infusion of bromodeoxyuridine (BUdR), 200 mg/sq m. Grossly viable fragments were taken from the biopsied tumor specimens and divided into two portions. One portion was dissociated into single cells, stained both with fluorescein isothiocyanate (FITC) using anti-BUdR monoclonal antibody as the first antibody and with propidium iodide (for deoxyribonucleic acid), and analyzed by flow cytometry (FCM). The labeling index (LI) was calculated as the number of FITC-labeled cells expressed as a percentage of the total number of cells analyzed. The other portion was fixed in 70% ethanol, embedded in paraffin, sectioned, and stained with immunoperoxidase using anti-BUdR monoclonal antibody as the first antibody. The LI of these tissue sections was calculated in two ways: from selected areas in which the labeled cells were evenly distributed and from the entire tissue section. The LI's obtained by FCM correlated closely with those from the entire tissue sections (r = 0.99, p < 0.000001) and were usually lower than LI's from selected areas of tissue sections. The LI's determined by FCM also correlated well with the LI's from selected areas of tissue sections (r = 0.82, p < 0.00012), despite the difference in values between them. Thus, the FCM-derived LI and the tissue LI can both provide useful information for predicting the biological malignancy of individual tumors and for designing treatment regimens for individual patients with brain tumors; however, different standards should be used to interpret the LI's obtained by these two methods.

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Structure Extension of Tree-Augmented Naive Bayes

Entropy ◽

10.3390/e21080721 ◽

2019 ◽

Vol 21 (8) ◽

pp. 721 ◽

Cited By ~ 1

Author(s):

YuGuang Long ◽

LiMin Wang ◽

MingHui Sun

Keyword(s):

Naive Bayes ◽

Classification Performance ◽

Naïve Bayes ◽

Training Data ◽

Conditional Probability Distribution ◽

Independence Assumption ◽

Bayesian Network Classifiers ◽

Leibler Divergence ◽

The Difference ◽

Structure Extension

Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler divergence, the difference between NB and its variations lies in different orders of conditional mutual information represented by these augmenting edges in the tree-shaped network structure. In this paper, we propose to relax the independence assumption by further generalizing tree-augmented naive Bayes (TAN) from 1-dependence Bayesian network classifiers (BNC) to arbitrary k-dependence. Sub-models of TAN that are built to respectively represent specific conditional dependence relationships may “best match” the conditional probability distribution over the training data. Extensive experimental results reveal that the proposed algorithm achieves bias-variance trade-off and substantially better generalization performance than state-of-the-art classifiers such as logistic regression.

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