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unsupervised clustering reveals noncanonical myeloid cell subsets in the brain tumor microenvironment

CANCER IMMUNOLOGY IMMUNOTHERAPY 2025年第2期74卷 1-17页

作者： Hermelo, Ismail Virtanen, Tuomo Salonen, Iida Naetkin, Reetta Keitaanniemi, Sofia Tiihonen, Aliisa M. Lehtipuro, Suvi Kummola, Laura Raulamo, Ella Nordfors, Kristiina Haapasalo, Hannu Rauhala, Minna Kesseli, Juha Nykter, Matti Haapasalo, Joonas Rautajoki, Kirsi Tampere Univ Fac Med & Hlth Technol Prostate Canc Res Ctr Tampere Finland Tampere Univ Hosp Tampere Finland Tampere Univ Hosp Unit Pediat Haematol & Oncol Tampere Finland Tampere Univ Tays Canc Ctr Tampere Finland Tampere Univ Fac Med & Hlth Technol Tampere Finland Tampere Univ Hosp Fimlab Labs Ltd Tampere Finland Tampere Univ Hosp Dept Neurosurg Tampere Finland Tampere Univ Tampere Inst Adv Study Tampere Finland

The tumor immune microenvironment (TiME) of human central nervous system (CNS) tumors remains to be comprehensively deciphered. Here, we employed flow cytometry and RNA sequencing analysis for a deep data-driven dissection of a diverse TiME and to uncover noncanonical immune cell types in human CNS tumors by using seven tumors from five patients. Myeloid subsets comprised classical microglia, monocyte-derived macrophages, neutrophils, and two noncanonical myeloid subsets: CD3(+) myeloids and CD19(+) myeloids. T lymphocyte subsets included double-negative (CD4(-) CD8(-)) T cells (DNTs). Noncanonical myeloids and DNTs were explored on independent datasets, suggesting that our DNT phenotype represents gamma delta T cells. Noncanonical myeloids were validated using orthogonal methods across 73 patients from three independent datasets. While the proportions of classical myeloids agreed with reported malignancy type-associated TiMEs, unexpectedly high lymphocyte frequencies were detected in gliosarcoma, which also showed a unique expression pattern of immune-related genes. Our findings highlight the potential of data-driven approaches in resolving CNS TiME to reveal the mosaic of immune cell types constituting TiME, warranting the need for future studies on the nonclassical immune cell subsets.

关键词： Glioblastoma Brain metastasis Tumor microenvironment Immunophenotyping CD3(+) myeloids CD19(+) myeloids unsupervised clustering Single-cell sequencing Deconvolution

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Automatic Detection and unsupervised clustering-Based Classification of Cetacean Vocal Signals

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3585-3585页

作者： Liang, Yinian Wang, Yan Chen, Fangjiong Yu, Hua Ji, Fei Chen, Yankun South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China South China Sea Marine Survey & Technol Ctr State Minist Nat Resources Key Lab Marine Environm Survey Technol & Applicat Guangzhou 510300 Peoples R China

In the ocean environment, passive acoustic monitoring (PAM) is an important technique for the surveillance of cetacean species. Manual detection for a large amount of PAM data is inefficient and time-consuming. To extract useful features from a large amount of PAM data for classifying different cetacean species, we propose an automatic detection and unsupervised clustering-based classification method for cetacean vocal signals. This paper overcomes the limitations of the traditional threshold-based method, and the threshold is set adaptively according to the mean value of the signal energy in each frame. Furthermore, we also address the problem of the high cost of data training and labeling in deep-learning-based methods by using the unsupervised clustering-based classification method. Firstly, the automatic detection method extracts vocal signals from PAM data and, at the same time, removes clutter information. Then, the vocal signals are analyzed for classification using a clustering algorithm. This method grabs the acoustic characteristics of vocal signals and distinguishes them from environmental noise. We process 194 audio files in a total of 25.3 h of vocal signal from two marine mammal public databases. Five kinds of vocal signals from different cetaceans are extracted and assembled to form 8 datasets for classification. The verification experiments were conducted on four clustering algorithms based on two performance metrics. The experimental results confirm the effectiveness of the proposed method. The proposed method automatically removes about 75% of clutter data from 1581.3MB of data in audio files and extracts 75.75 MB of the features detected by our algorithm. Four classical unsupervised clustering algorithms are performed on the datasets we made for verification and obtain an average accuracy rate of 84.83%.

关键词： passive acoustic monitoring empirical mode decomposition endpoint detection unsupervised clustering marine mammal vocal signals

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Utilizing structural MRI and unsupervised clustering to differentiate schizophrenia and Alzheimer's disease in late-onset psychosis

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BEHAVIOURAL BRAIN RESEARCH 2025年 480卷 115386页

作者： Hojjati, Seyed Hani Chen, Kewei Chiang, Gloria C. Kuceyeski, Amy Wang, Xiuyuan H. Razlighi, Qolamreza R. Pahlajani, Silky Glodzik, Lidia Tanzi, Emily B. Reinhardt, Michael Butler, Tracy A. Weill Cornell Med Brain Hlth Imaging Inst Dept Radiol 401 East 61st StRoom RR 214 New York NY 10065 USA Arizona State Univ Coll Hlth Solut Phoenix AZ USA Arizona State Univ Sch Math & Stat Phoenix AZ USA Univ Arizona Coll Med Dept Neurol Phoenix AZ USA Banner Alzheimers Inst Phoenix AZ USA Cornell Univ Dept Computat Biol Ithaca NY USA Rutgers State Univ Brain Hlth Inst Dept Psychiat Piscataway NJ USA SUNY Downstate Med Ctr Dept Psychiat BROOKLYN NY USA

Late-onset psychosis (LOP) represents a highly heterogeneous and understudied condition, with potential origins ranging from atypically late onset of schizophrenia (SCZ) to Alzheimer's Disease (AD). Despite the clinical necessity of differentiating these conditions to guide effective treatment, achieving an accurate diagnosis remains challenging. This study aimed to utilize data-driven analyses of structural magnetic resonance imaging (MRI) to distinguish between these diagnostic possibilities. Utilizing publicly available datasets of MRI scans from 699 healthy control (HC) participants and 469 patients diagnosed with SCZ or AD, our analysis focused on bilateral subcortical volumetric measures in the caudate, hippocampus, putamen, and amygdala. We first trained an unsupervised K-means clustering algorithm based on SCZ and AD patients and achieved a clustering accuracy of 81 % and an area under curvature (AUC) of 0.79 in distinguishing between these two groups. Subsequently, we calculated the Euclidean distance between the AD and SCZ cluster centroids for each of ten patients with unexplained onset of psychosis after age 45 from a clinical MRI registry. Six patients were classified as AD and four as SCZ. Our findings revealed that among LOP participants, those classified in the SCZ cluster exhibited significantly greater right putamen volumes compared to those in the AD cluster (p < 0.0025). There were also intriguing clinical differences. While we do not have diagnostic biomarker information to confirm these classifications, this study sheds light on the heterogeneity of psychoses in late life and illustrates the potential use of widely available structural MRI and data-driven methods to enhance diagnostic accuracy and treatment outcomes for LOP patients.

关键词： Late-life psychosis (LOP) Schizophrenia (SCZ) Alzheimer's disease (AD) Structural magnetic resonance imaging (MRI) unsupervised clustering

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unsupervised clustering of dominant scenes in sports video

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PATTERN RECOGNITION LETTERS 2003年第15期24卷 2651-2662页

作者： Lu, H Tan, YP Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

We propose a new and efficient approach for clustering dominant scenes in sports video. To perform clustering in an unsupervised manner, we devise a recursive peer-group filtering (PGF) scheme to identify prototypical shots for each dominant scene, and examine time coverage of these prototypical shots to decide the number of dominant scenes for each sports video under analysis. To improve clustering efficiency, we employ principal component analysis and linear discriminant analysis to project high dimensional shot features to lower dimensional spaces suitable for classification. The main contribution of the paper lies in the formulation of clustering dominant scenes in sports video and the development of an efficient, unsupervised solution making use of PGF, time-coverage criterion, and subspace analysis. Experimental results obtained from various types of sports videos are presented to show the efficacy of the proposed approach. (C) 2003 Elsevier B.V. All rights reserved.

关键词： content-based video analysis unsupervised clustering peer-group filtering principal component analysis linear discriminant analysis

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unsupervised clustering in mRNA expression profiles

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COMPUTERS IN BIOLOGY AND MEDICINE 2006年第10期36卷 1126-1142页

作者： Tasoulis, D. K. Plagianakos, V. P. Vrahatis, M. N. Univ Patras Dept Math Computat Intelligence Lab GR-26110 Patras Greece Univ Patras Artificial Intelligence Res Ctr GR-26110 Patras Greece

The development of microarray technologies gives scientists the ability to examine, discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis. The most commonly used computational approach for analyzing microarray data is cluster analysis, since the number of genes is usually very high compared to the number of samples. In this paper, we investigate the application of the recently proposed k-windows clustering algorithm on gene expression microarray data. This algorithm apart from identifying the clusters present in a data set also calculates their number and thus requires no special knowledge about the data. To improve the quality of the clustering, we employ various dimension reduction techniques and propose a hybrid one. The results obtained by the application of the algorithm exhibit high classification success. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： bioinformatics gene expression analysis mRNA expression profiles k-windows algorithm unsupervised clustering cluster analysis dimension reduction

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unsupervised clustering using fractal dimension

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2006年第7期16卷 2073-2079页

作者： Tasoulis, D. K. Vrahatis, M. N. Univ Patras Dept Math Computat Intelligence Lab GR-26110 Patras Greece

clustering can be defined as the process of "grouping" a collection of objects into subsets or clusters. The clustering problem has been addressed in numerous contexts and by researchers in different disciplines. This reflects its broad appeal and usefulness as an exploratory data analysis approach. unsupervised clustering algorithms have been developed to address real world problems in which the number of clusters present in the dataset is unknown. These algorithms approximate the number of clusters while performing the clustering procedure. This paper is a first step towards the development of unsupervised clustering algorithms capable of identifying clusters within clusters. To this end, an unsupervised clustering algorithm is modified so as to take into consideration the fractal dimension of the data. The experimental results indicate that this approach can provide further qualitative information compared to the unsupervised clustering algorithm.

关键词： unsupervised clustering fractal dimension clustering within clusters cluster analysis

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unsupervised clustering of fMRI and MRI time series

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2007年第4期2卷 295-310页

作者： Meyer-Baese, A. Saalbach, A. Lange, O. Wismueller, A. Florida State Univ Tallahassee FL 32306 USA

unsupervised clustering represents a powerful technique for self-organized segmentation of biomedical image time series data describing groups of pixels exhibiting similar properties of local signal dynamics. The theoretical background is presented in the beginning, followed by several medical applications demonstrating the flexibility and conceptual power of these techniques. These applications range from functional MRI data analysis to dynamic contrast-enhanced perfusion MRI and breast MRI. For fMRI, these methods can be employed to identify and separate time courses of interest, along with their associated spatial patterns. When applied to dynamic perfusion MRI, they identify groups of voxels associated with time courses that are clinically informative and straightforward to interpret. In breast MRI, a segmentation of the lesion is achieved and in addition a subclassification is obtained within the lesion with regard to regions characterized by different MRI signal time courses. In the present paper, we conclude that unsupervised clustering techniques provide a robust method for blind analysis of time series image data in the important and current field of functional and dynamic MRI. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： unsupervised clustering fMRI MRI Dynamic perfusion MRI Biomedical image time series

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unsupervised clustering of service performance behaviors

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INFORMATION SCIENCES 2018年 422卷 558-571页

作者： Yahyaoui, Harndi Own, Hala S. Kuwait Univ Comp Sci Dept Kuwait Kuwait Natl Res Inst Astron & Geophys Dept Solar & Space Res Cairo Egypt

We propose in this paper a novel approach for unsupervised clustering of services' behaviors. These behaviors are modeled as multivariate time series that capture the evaluation of several service quality attributes for a period of time. The importance weights of quality attributes are derived based on the Shannon's entropy concept and the service data is flattened in a format that is convenient for clustering. The flattening process spans over a time oriented aggregation transformation, which leverages Haar reduction. The reduction is modeled as a maximization of an objective function. The absence of ground truth is tackled by performing a set of tests to determine the best number of clusters and clustering algorithms. Extensive experiments were conducted to validate the proposed unsupervised clustering approach. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Services Behaviors Performance unsupervised clustering Time series

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unsupervised clustering-Based Short-Term Solar Forecasting

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第4期10卷 2174-2185页

作者： Feng, Cong Cui, Mingjian Hodge, Bri-Mathias Lu, Siyuan Hamann, Hendrik F. Zhang, Jie Univ Texas Dallas Dept Mech Engn Richardson TX 75080 USA Univ Colorado Dept Elect Comp & Energy Engn Boulder CO 80309 USA Natl Renewable Energy Lab Boulder CO 80309 USA IBM TJ Watson Res Ctr Data Intens Phys Analyt Res Grp Yorktown Hts NY 10598 USA IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA IBM TJ Watson Res Ctr Phys Analyt & Cognit IoT Program Yorktown Hts NY 10598 USA

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated clustering (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%;and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

关键词： Solar forecasting unsupervised clustering pattern recognition machine learning sky imaging

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unsupervised clustering for Fault Diagnosis in Nuclear Power Plant Components

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第4期6卷 764-777页

作者： Baraldi, Piero Di Maio, Francesco Zio, Enrico Politecn Milan Dept Energy I-20133 Milan Italy Ecole Cent Paris Chair Syst Sci & Energet Challenge Paris France

The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are 'unlabeled', i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology for the identification of transients of similar characteristics, under the conjecture that faults of the same type lead to similar behavior in the measured signals. The proposed methodology is based on the combined use of Haar wavelet transform, fuzzy similarity, spectral clustering and the Fuzzy C-Means algorithm. A procedure for interpreting the fault cause originating the similar transients is proposed, based on the identification of prototypical behaviors. Its performance is tested with respect to an artificial case study and then applied on transients originated by different faults in the pressurizer of a nuclear power reactor.

关键词： Fault diagnosis unsupervised clustering Haar wavelets fuzzy similarity spectral clustering Fuzzy C-Means

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