Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

Liu, Peng; Liu, Silvia; Fang, Yusi; Xue, Xiangning; Zou, Jian; Tseng, George C.; Konnikova, Liza

doi:10.3389/fcell.2020.00234

Cited by 29 publications

(24 citation statements)

References 75 publications

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“…More advanced strategies allowing for the discovery and quantification of cell populations and/or differentiation pathways have been developed. [17][18][19][20] Their fields of application are mass cytometry, classical flow cytometry, and more recent technologies of single-cell molecular analyses. Of note, there is an increasing concomitant and interchangeable development of such algorithms for cellular and genomic data.…”

Section: A Time-rel Ated Overvie W Of Prog Re Ss In Mfc Analys Ismentioning

confidence: 99%

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Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm

Béné

Lacombe

Porwit

2021

Int J Lab Hematology

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Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high‐dimensional data sets at the single‐cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step‐by‐step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.

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Section: A Time-rel Ated Overvie W Of Prog Re Ss In Mfc Analys Ismentioning

confidence: 99%

“…More advanced strategies allowing for the discovery and quantification of cell populations and/or differentiation pathways have been developed 17‐20 . Their fields of application are mass cytometry, classical flow cytometry, and more recent technologies of single‐cell molecular analyses.…”

Section: A Time‐related Overview Of Progress In Mfc Analysismentioning

confidence: 99%

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm

Béné

Lacombe

Porwit

2021

Int J Lab Hematology

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“…PhenoGraph [7], flowMeans [8] and SamSPECTRAL [9] are some of the most popular unsupervised cell phenotyping algorithms [1]. A brief description of the methods can be found in Table 2.…”

Section: Existing Phenotyping Algorithmsmentioning

confidence: 99%

“…Thus, manual gating is not only prone to human error but also time consuming and costly. Algorithms have already been developed to tackle these same phenotyping issues for multiplex technologies that analyze single cells in a liquid suspension without spatial resolution, namely flow and mass cytometry [1]. In particular, automatic gating methods using machine learning algorithms have become more and more popular in flow and mass cytometry data as the number of analyzed parameters has increased [2] .…”

Section: Introductionmentioning

confidence: 99%

On Clustering for Cell Phenotyping in Multiplex Immunohistochemistry (mIHC) and Multiplexed Ion Beam Imaging (MIBI) Data

Seal

Wrobel

Johnson

et al. 2021

Preprint

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Problem: Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) images are usually phenotyped using a manual thresholding process. The thresholding is prone to biases, especially when examining multiple images with high cellularity. Results: Unsupervised cell phenotyping methods including PhenoGraph, flowMeans, and SamSPECTRAL, primarily used in flow cytometry data, often perform poorly or need elaborate tuning to perform well in the context of mIHC and MIBI data. We show that, instead, semi-supervised cell clustering using Random Forests, linear and quadratic discriminant analysis are superior. We test the performance of the methods on two mIHC datasets from the University of Colorado School of Medicine and a publicly available MIBI dataset. Each dataset contains numerous highly complex images.

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“…As the final and one of the most challenging steps, data analysis is traditionally performed by using a two-parameter gating strategy in which the signal of two channels is depicted as a dot plot. Newer ways to analyze complex and high-dimensional data use more sophisticated algorithms that extract useful information [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Flow Cytometry: A Blessing and a Curse

Drescher

Weiskirchen

2021

Biomedicines

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Flow cytometry is a laser-based technology generating a scattered and a fluorescent light signal that enables rapid analysis of the size and granularity of a particle or single cell. In addition, it offers the opportunity to phenotypically characterize and collect the cell with the use of a variety of fluorescent reagents. These reagents include but are not limited to fluorochrome-conjugated antibodies, fluorescent expressing protein-, viability-, and DNA-binding dyes. Major developments in reagents, electronics, and software within the last 30 years have greatly expanded the ability to combine up to 50 antibodies in one single tube. However, these advances also harbor technical risks and interpretation issues in the identification of certain cell populations which will be summarized in this viewpoint article. It will further provide an overview of different potential applications of flow cytometry in research and its possibilities to be used in the clinic.

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Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

Cited by 29 publications

References 75 publications

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm

On Clustering for Cell Phenotyping in Multiplex Immunohistochemistry (mIHC) and Multiplexed Ion Beam Imaging (MIBI) Data

Flow Cytometry: A Blessing and a Curse

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