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TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus clustering

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第4期19卷 2512-2522页

作者： Gan, Yanglan Li, Ning Guo, Cheng Zou, Guobing Guan, Jihong Zhou, Shuigeng Donghua Univ Sch Comp Sci & Technol Shanghai 200051 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 200092 Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

关键词： Trajectory clustering algorithms Partitioning algorithms Gene expression Diseases Computer science Manifolds Trajectory inference consensus clustering gene partition single-cell transcriptome

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Improving clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

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IEEE ACCESS 2022年 10卷 443-455页

作者： Rouwhorst, George Duque, Edgar Mauricio Salazar Nguyen, Phuong H. Slootweg, Han Eindhoven Univ Technol Dept Elect Engn NL-5612 AP Eindhoven Netherlands Enexis Netbeheer BV Asset Management NL-5223 MB Shertogenbosch Netherlands

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy.

关键词： Load forecasting Transformers Forecasting clustering algorithms Loading Distribution networks Predictive models Aggregated load forecast clustering day-ahead distribution network feature selection gradient boosting

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Spatial Weighted Robust clustering of Multivariate Time Series Based on Quantile Dependence With an Application to Mobility During COVID-19 Pandemic

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第9期30卷 3990-4004页

作者： Lopez-Oriona, Angel D'Urso, Pierpaolo Vilar, Jose A. Lafuente-Rego, Borja Univ A Coruna Res Ctr ICT CITIC Dept Math Res Grp MODES La Coruna Spain Sapienza Univ Rome Dept Social Sci & Econ I-00185 Rome Italy

In this article, a fuzzy clustering model for multivariate time series based on the quantile cross-spectral density and principal component analysis is extended by including: 1) a weighting system which assigns a weight to each principal component in accordance with its importance concerning the underlying clustering structure and 2) a penalization term allowing to take into account the spatial information. The iterative solutions of the new model, which employs the exponential distance in order to gain robustness against outlying series, are derived. A simulation study shows that the weighting system substantially enhances the effectiveness of the former approach. The behavior of the extended model in terms of the spatial penalization term is also analyzed. An application involving multivariate time series of mobility indicators concerning COVID-19 pandemic highlights the usefulness of the proposed technique.

关键词： Time series analysis clustering algorithms COVID-19 Time measurement Principal component analysis Linear programming Stochastic processes clustering COVID-19 multivariate time series PCA quantile cross-spectral density spatial statistics weighting

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Web Services clustering via Exploring Unified Content and Structural Semantic Representation

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第4期19卷 4082-4096页

作者： Kang, Guosheng Liu, Jianxun Xiao, Yong Cao, Yingcheng Cao, Buqing Shi, Min Hunan Univ Sci & Technol Hunan Prov Key Lab Serv Comp & Novel Software Tech Xiangtan 411199 Peoples R China Hunan Univ Sci & Technol Dept Comp Sci & Engn Xiangtan 411199 Peoples R China Washington Univ Sch Med Dept Genet St Louis MO 63110 USA

clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.

关键词： Web services Semantics clustering algorithms Task analysis Tagging Quality of service Mashups Web service services clustering service network network embedding semantic mining

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A Deep Embedded clustering Algorithm for the Binning of Metagenomic Sequences

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IEEE ACCESS 2022年 10卷 54348-54357页

作者： Bao, Huynh Quang Vinh, Le Van Hoai, Tran Van Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn Ho Chi Minh City 70000 Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City 70000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 70000 Vietnam

The study of metagenomic sequences brings a deep understanding of microbial communities. One of the crucial steps in metagenomic projects is to classify sequences into different organisms, named the binning problem. In the emerging methods for classification, deep learning is a potential technology to be applicable with high accuracy. However, it is well-known that reference databases, which are highly required by deep learning based methods, are not always available. As a result, some existing binning solutions have applied unsupervised learning processes, but utilizing the strength of deep learning in an unsupervised model is still a challenging problem. This work proposes a binning algorithm for metagenomic sequences, called MetaDEC, which applies a deep unsupervised learning approach. By following the two-phase paradigm, the algorithm firstly divides sequences into groups of overlapping sequences. The groups are then classified into clusters using an adversarial deep embedded clustering technique. Experimental results show that MetaDEC achieves competitive performance compared to existing methods on both simulated and real metagenomic data.

关键词： Genomics clustering algorithms Classification algorithms Bioinformatics Feature extraction Sequential analysis Organisms Algorithm clustering deep learning metagenomics DNA sequence

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User clustering and Resource Allocation in Hybrid NOMA-OMA Systems Under Nakagami-m Fading

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IEEE ACCESS 2022年 10卷 38709-38728页

作者： Mahmoudi, Ali Abolhassani, Bahman Razavizadeh, S. Mohammad Nguyen, Ha H. Iran Univ Sci & Technol Sch Elect Engn Tehran *** Iran Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

In this paper, we tackle the problem of optimizing user clustering, power, and resource (time slot or bandwidth) allocation in the downlink of a hybrid non-orthogonal multiple access (NOMA)-orthogonal multiple access (OMA) system. In such a system, users are organized into several clusters under one of the following scenarios: (1) fixed cluster size, (2) fixed number of clusters, and (3) variable number of clusters and variable cluster size. A power domain NOMA (PD-NOMA) scheme is used in each cluster, while OMA is employed for allocating resources to different clusters. The goal is to maximize the minimum success probability (which is equivalent to minimizing the maximum outage probability) among all users to guarantee fairness. We prove that at the optimal solution, all users have the same success probability, which is called the common success probability (CSP). Then, we propose an efficient algorithm for finding the optimal CSP and cluster resource allocation factors simultaneously. The optimal power allocation factors and the optimal decoding order of users in each cluster are then derived in closed-form expressions based on the obtained optimal CSP. Simulation results show considerable performance gains by the proposed scheme, compared to existing schemes in terms of fairness, the minimum success probability of users, and the sum throughput.

关键词： Resource management NOMA clustering algorithms Heuristic algorithms Decoding Closed-form solutions Throughput Hybrid NOMA-OMA user clustering power allocation resource allocation fairness

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PCSP: A Novel Class Discovery Algorithm for Radio Signal Classification

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第4期10卷 1190-1203页

作者： Chen, Jie Zhao, Zhijin Zheng, Shilian Zhang, Luxin Qiu, Kunfeng Yang, Xiaoniu Hangzhou Dianzi Univ Telecommun Sch Hangzhou 310018 Peoples R China Natl Key Lab Electromagnet Space Secur Innovat Studio Academician Yang Jiaxing 314033 Peoples R China

Novel class discovery (NCD) aims to cluster novel signals in an unlabeled set by using prior knowledge of the labeled signal sets. In order to improve the recognition ability of radio signals, in this paper, we propose a prototype classifier and similarity prediction method (PCSP) for NCD of radio signals. Firstly, focusing on the different recognition abilities of the network for labeled and unlabeled signals, PCSP adopts different similarity prediction strategies and comparative learning, so that the network can obtain a better cluster representation. Secondly, in view of the limitation of the closed set assumption of the traditional deep learning network on the expression ability of unlabeled data, PCSP uses prototype encoding for each type of signal to obtain a prototype classifier, so that the network has a more compact intra-class feature representation. Finally, simulation experiments are carried out on the interference signal dataset and the modulation signal dataset respectively. The results show that the performance of PCSP is much better than the existing algorithms.

关键词： Classification algorithms Feature extraction clustering algorithms Self-supervised learning Prototypes Prediction algorithms Modulation Novel class discovery interference recognition automatic modulation classification deep learning

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A Novel Multidimensional Statistics Denoising Algorithm Based on Gaussian Mixture Model for Photon-Counting LiDAR Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 13308-13323页

作者： Chen, Sitong Nie, Sheng Xi, Xiaohuan Xia, Shaobo Zhu, Feng Wang, Cheng Zhu, Xiaoxiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Changsha Univ Sci & Technol Dept Geomat Engn Changsha 410114 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China China Earthquake Adm Inst Geol State Key Lab Earthquake Dynam Beijing 100029 Peoples R China

The micropulse multibeam photon-counting laser altimeter significantly improves the sampling density along the orbit while introducing abundant noise. Therefore, noise removal is crucial for the subsequent applications of the photon-counting laser altimeter. This study proposes a new multidimensional statistics algorithm based on the Gaussian mixture model for photon noise removal. The method can simultaneously utilize multiple point cloud statistics and avoid manually selecting denoising thresholds, dividing signals and noise adaptively. To evaluate the performance of the algorithm, it is applied to both simulated ICESat-2 data and real ICESat-2 data, and evaluation indicators including recall, precision, and the harmonic means of recall and precision are used for analysis. Experimental results indicate that compared to denoising using a single statistic, denoising with multidimensional statistics (specifically, the sum of the distance of k-nearest neighbors, the photon density, and the standard deviation of the height difference within an elliptic neighborhood of the target photon) generally yields better results. Multidimensional statistics can influence and constrain each other, leading to improved decision-making outcomes. After comparing the algorithm with mainstream DBSCAN and KNN methods, the results indicate that the GMM method achieves the best denoising results, with average F-values 5.22% and 7.26% higher than those of DBSCAN and KNN methods, respectively, across all datasets. In conclusion, this algorithm can comprehensively assess and make decisions based on different statistical measures, demonstrating robustness and adaptability in extracting signal photons, and is worth promoting in the photon denoising applications.

关键词： Photonics Noise Noise reduction Classification algorithms Point cloud compression clustering algorithms Training And land elevation satellite-2 (ICESat-2) cloud gaussian mixture model (GMM) ice multidimensional statistics photon denoising photon-counting lidar

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Unveiling the Effects of Slightly Skewed Labels on Traffic Data Analysis

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Pei, Jiaming Li, Wei Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia

Data heterogeneity is a prevalent challenge in intelligent transportation systems (ITS), often arising from variations in traffic patterns across different regions or time periods. For instance, certain traffic events, such as congestion, may be more frequent in urban areas during peak hours, while other events, like accidents, might occur more often in suburban regions, leading to slightly skewed label distributions. While federated learning provides an effective solution for distributed data, its performance can degrade when client datasets exhibit such label skew. To address this, we propose a strategy that combines Gaussian mixture clustering with oversampling. Gaussian mixture clustering can handle overlapping data points in model parameters, but insufficient client samples may limit local model training. To overcome this, we introduce a Gaussian mixture-based oversampling method to generate additional samples, enhancing the robustness of federated learning under slightly skewed label scenarios. Our experiments demonstrate that this method outperforms or matches existing approaches, ensuring more reliable and accurate ITS applications.

关键词： Data models Training clustering algorithms Federated learning Accuracy Accidents Performance evaluation Partitioning algorithms Robustness Data privacy clustering Gaussian mixture oversampling

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Mining Both Commonality and Specificity From Multiple Documents for Multi-Document Summarization

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IEEE ACCESS 2024年 12卷 54371-54381页

作者： Ma, Bing Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original multiple documents and satisfies content diversity. To fulfill the dual requirements of coverage and diversity in multi-document summarization, this study introduces a novel method. Initially, a class tree is constructed through hierarchical clustering of documents. Subsequently, a sentence selection method based on class tree is proposed for generating a summary. Specifically, a top-down traversal is performed on the class tree, during which sentences are selected from each node based on their similarity to the centroid of the documents within the node and their dissimilarity to the centroid of documents not belonging to the node. Sentences selected from the root node reflect the commonality of all document, and sentences selected from the sub nodes reflect the distinct specificity of the respective subclasses. Experimental results on standard text summarization datasets DUC'2002, DUC'2003, and DUC'2004 demonstrate that the proposed method significantly outperforms the variant method that considers only commonality of all documents, achieving average improvements of up to 1.54 and 1.42 in ROUGE-1 and ROUGE-L scores, respectively. Additionally, the method demonstrates significant superiority over another variant method that considers only the specificity of subclasses, achieving average improvements of up to 2.16 and 2.01 in ROUGE-1 and ROUGE-L scores, respectively. Furthermore, extensive experiments on DUC'2004 and Multi-News datasets show that the proposed method outperforms lots of competitive supervised and unsupervised multi-document summarization methods and yields considerable results.

关键词： Vectors Task analysis Feature extraction Natural languages clustering algorithms Symmetric matrices Standards Class tree commonality and specificity hierarchical clustering of documents multi-document summarization pre-trained embedding representation

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