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Huang H, Wu H, Wei X, Zhou Y. Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm. Biomimetics (Basel) 2023; 9:3. [PMID: 38275451 DOI: 10.3390/biomimetics9010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/14/2023] [Accepted: 12/14/2023] [Indexed: 01/27/2024] Open
Abstract
Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of dc when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize dc for maximizing accuracy, achieving automatic determination of dc. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC.
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Affiliation(s)
- Huajuan Huang
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Hao Wu
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Xiuxi Wei
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- School of Computer Science & Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, China
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Guan J, Li S, He X, Zhu J, Chen J, Si P. SMMP: A Stable-Membership-Based Auto-Tuning Multi-Peak Clustering Algorithm. IEEE Trans Pattern Anal Mach Intell 2023; 45:6307-6319. [PMID: 36219667 DOI: 10.1109/tpami.2022.3213574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype clustering algorithms have been proposed. Nevertheless, the automatic estimation of the number of clusters and the detection of complex shapes are still challenging, and to solve such problems usually relies on user-specified parameters and may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak clustering algorithm (SMMP) is proposed, which can achieve fast, automatic, and effective multi-prototype clustering without iteration. A dynamic association-transfer method is designed to learn the representativeness of points to sub-cluster centers during the generation of sub-clusters by applying the density peak clustering technique. According to the learned representativeness, a border-link-based connectivity measure is used to achieve high-fidelity similarity evaluation of sub-clusters. Meanwhile, based on the assumption that a reasonable clustering should have a relatively stable membership state upon the change of clustering thresholds, SMMP can automatically identify the number of sub-clusters and clusters, respectively. Also, SMMP is designed for large datasets. Experimental results on both synthetic and real datasets demonstrated the effectiveness of SMMP.
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Guan J, Li S, He X, Chen J. Clustering by fast detection of main density peaks within a peak digraph. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Scheele CLGJ, Herrmann D, Yamashita E, Celso CL, Jenne CN, Oktay MH, Entenberg D, Friedl P, Weigert R, Meijboom FLB, Ishii M, Timpson P, van Rheenen J. Multiphoton intravital microscopy of rodents. Nat Rev Methods Primers 2022; 2:89. [PMID: 37621948 PMCID: PMC10449057 DOI: 10.1038/s43586-022-00168-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/12/2022] [Indexed: 08/26/2023]
Abstract
Tissues are heterogeneous with respect to cellular and non-cellular components and in the dynamic interactions between these elements. To study the behaviour and fate of individual cells in these complex tissues, intravital microscopy (IVM) techniques such as multiphoton microscopy have been developed to visualize intact and live tissues at cellular and subcellular resolution. IVM experiments have revealed unique insights into the dynamic interplay between different cell types and their local environment, and how this drives morphogenesis and homeostasis of tissues, inflammation and immune responses, and the development of various diseases. This Primer introduces researchers to IVM technologies, with a focus on multiphoton microscopy of rodents, and discusses challenges, solutions and practical tips on how to perform IVM. To illustrate the unique potential of IVM, several examples of results are highlighted. Finally, we discuss data reproducibility and how to handle big imaging data sets.
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Affiliation(s)
- Colinda L. G. J. Scheele
- Laboratory for Intravital Imaging and Dynamics of Tumor Progression, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - David Herrmann
- Cancer Ecosystems Program, Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Department, Sydney, New South Wales, Australia
- St. Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Erika Yamashita
- Department of Immunology and Cell Biology, Graduate School of Medicine and Frontier Biosciences, Osaka University, Osaka, Japan
- WPI-Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Laboratory of Bioimaging and Drug Discovery, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Cristina Lo Celso
- Department of Life Sciences and Centre for Hematology, Imperial College London, London, UK
- Sir Francis Crick Institute, London, UK
| | - Craig N. Jenne
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Maja H. Oktay
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
| | - David Entenberg
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
| | - Peter Friedl
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, Netherlands
- David H. Koch Center for Applied Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Roberto Weigert
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Franck L. B. Meijboom
- Department of Population Health Sciences, Sustainable Animal Stewardship, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
- Faculty of Humanities, Ethics Institute, Utrecht University, Utrecht, Netherlands
| | - Masaru Ishii
- Department of Immunology and Cell Biology, Graduate School of Medicine and Frontier Biosciences, Osaka University, Osaka, Japan
- WPI-Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Laboratory of Bioimaging and Drug Discovery, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Paul Timpson
- Cancer Ecosystems Program, Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Department, Sydney, New South Wales, Australia
- St. Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jacco van Rheenen
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, Netherlands
- Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
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Peng D, Gui Z, Wang D, Ma Y, Huang Z, Zhou Y, Wu H. Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nat Commun 2022; 13. [PMID: 36114209 PMCID: PMC9481560 DOI: 10.1038/s41467-022-33136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 09/05/2022] [Indexed: 11/30/2022] Open
Abstract
Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks. Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. Here the authors propose a local direction centrality clustering algorithm that copes with heterogeneous density and weak connectivity issues.
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Pizzagalli DU, Bordini J, Morone D, Pulfer A, Carrillo-Barberà P, Thelen B, Ceni K, Thelen M, Krause R, Gonzalez SF. CANCOL, a Computer-Assisted Annotation Tool to Facilitate Colocalization and Tracking of Immune Cells in Intravital Microscopy. J Immunol 2022; 208:1493-1499. [PMID: 35181636 DOI: 10.4049/jimmunol.2100811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/30/2021] [Indexed: 06/14/2023]
Abstract
Two-photon intravital microscopy (2P-IVM) has become a widely used technique to study cell-to-cell interactions in living organisms. Four-dimensional imaging data obtained via 2P-IVM are classically analyzed by performing automated cell tracking, a procedure that computes the trajectories followed by each cell. However, technical artifacts, such as brightness shifts, the presence of autofluorescent objects, and channel crosstalking, affect the specificity of imaging channels for the cells of interest, thus hampering cell detection. Recently, machine learning has been applied to overcome a variety of obstacles in biomedical imaging. However, existing methods are not tailored for the specific problems of intravital imaging of immune cells. Moreover, results are highly dependent on the quality of the annotations provided by the user. In this study, we developed CANCOL, a tool that facilitates the application of machine learning for automated tracking of immune cells in 2P-IVM. CANCOL guides the user during the annotation of specific objects that are problematic for cell tracking when not properly annotated. Then, it computes a virtual colocalization channel that is specific for the cells of interest. We validated the use of CANCOL on challenging 2P-IVM videos from murine organs, obtaining a significant improvement in the accuracy of automated tracking while reducing the time required for manual track curation.
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Affiliation(s)
- Diego Ulisse Pizzagalli
- Euler Institute, Università della Svizzera Italiana, Lugano, Switzerland
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland
| | - Joy Bordini
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland
| | - Diego Morone
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland; and
| | - Alain Pulfer
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Pau Carrillo-Barberà
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland
| | - Benedikt Thelen
- Euler Institute, Università della Svizzera Italiana, Lugano, Switzerland
| | - Kevin Ceni
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland
| | - Marcus Thelen
- Euler Institute, Università della Svizzera Italiana, Lugano, Switzerland
| | - Rolf Krause
- Euler Institute, Università della Svizzera Italiana, Lugano, Switzerland;
| | - Santiago Fernandez Gonzalez
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Ticino, Switzerland;
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Junyi G, li S, Xiongxiong H, Jiajia C. A novel clustering algorithm by adaptively merging sub-clusters based on the Normal-neighbor and Merging force. Pattern Anal Appl 2021; 24:1231-1248. [DOI: 10.1007/s10044-021-00981-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Pizzagalli DU, Latino I, Pulfer A, Palomino-Segura M, Virgilio T, Farsakoglu Y, Krause R, Gonzalez SF. Characterization of the Dynamic Behavior of Neutrophils Following Influenza Vaccination. Front Immunol 2019; 10:2621. [PMID: 31824481 PMCID: PMC6881817 DOI: 10.3389/fimmu.2019.02621] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 10/22/2019] [Indexed: 12/24/2022] Open
Abstract
Neutrophils are amongst the first cells to respond to inflammation and infection. Although they play a key role in limiting the dissemination of pathogens, the study of their dynamic behavior in immune organs remains elusive. In this work, we characterized in vivo the dynamic behavior of neutrophils in the mouse popliteal lymph node (PLN) after influenza vaccination with UV-inactivated virus. To achieve this, we used an image-based systems biology approach to detect the motility patterns of neutrophils and to associate them to distinct actions. We described a prominent and rapid recruitment of neutrophils to the PLN following vaccination, which was dependent on the secretion of the chemokine CXCL1 and the alarmin molecule IL-1α. In addition, we observed that the initial recruitment occurred mainly via high endothelial venules located in the paracortical and interfollicular regions of the PLN. The analysis of the spatial-temporal patterns of neutrophil migration demonstrated that, in the initial stage, the majority of neutrophils displayed a patrolling behavior, followed by the formation of swarms in the subcapsular sinus of the PLN, which were associated with macrophages in this compartment. Finally, we observed using multiple imaging techniques, that neutrophils phagocytize and transport influenza virus particles. These processes might have important implications in the capacity of these cells to present viral antigens.
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Affiliation(s)
- Diego Ulisse Pizzagalli
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
- Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Irene Latino
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Alain Pulfer
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Miguel Palomino-Segura
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Tommaso Virgilio
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | | | - Rolf Krause
- Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Santiago F. Gonzalez
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
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