Consensus Clustering Framework Research Articles

Successive Consensus Clustering for Unsupervised Video-Based Person Re-Identification

Person re-identification is to match the same person between non-overlapping cameras. This paper focuses on unsupervised video-based person re-identification. The mainstream approach is to obtain pseudo-labels by clustering samples for training the classification model. In this scheme, a potential threat is that noisy pseudo-labels may damage the optimization of the model. To mitigate this danger, we propose using a Successive Consensus Clustering framework for optimizing the pseudo-labels and the model iteratively. First, we leverage consensus clustering with respect to multiple frames of a video, which can generate high-quality pseudo-labels for pedestrians. Secondly, we develop contrastive learning based on the cluster successive memory mechanism, which can establish the correlation between different epochs of clustering so that makes the training of the model stable. Experiments on three large-scale data sets show that our method outperforms the previous state-of-the-art method, surpassing 10.6% for rank-1 and 18.6% for mAP on Mars, and 9.6% for rank-1 and 13.3% for mAP on DukeMTMC-VideoReID.

MCC: a Multiple Consensus Clustering Framework

Consensus clustering has emerged as an important extension of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings. There is a significant drawback in generating a single consensus clustering since different input clusterings could differ significantly. In this paper, we develop a new framework, called Multiple Consensus Clustering (MCC), to explore multiple clustering views of a given dataset from a set of input clusterings. Instead of generating a single consensus, we propose two sets of approaches to obtain multiple consensus. One employs the meta clustering method, and the other uses a hierarchical tree structure and further applies a dynamic programming algorithm to generate a flat partition from the hierarchical tree using the modularity measure. Multiple consensuses are finally obtained by applying consensus clustering algorithms to each cluster of the partition. Extensive experimental results on 11 real-world datasets and a case study on a Protein-Protein Interaction (PPI) dataset demonstrate the effectiveness of the MCC framework.

Protein interactions and consensus clustering analysis uncover insights into herpesvirus virion structure and function relationships.

Infections with human herpesviruses are ubiquitous and a public health concern worldwide. Current treatments reduce the severity of some symptoms associated to herpetic infections but neither remove the viral reservoir from the infected host nor protect from the recurrent symptom outbreaks that characterise herpetic infections. The difficulty in therapeutically tackling these viral systems stems in part from their remarkably large proteomes and the complex networks of physical and functional associations that they tailor. This study presents our efforts to unravel the complexity of the interactome of herpes simplex virus type 1 (HSV1), the prototypical herpesvirus species. Inspired by our previous work, we present an improved and more integrative computational pipeline for the protein–protein interaction (PPI) network reconstruction in HSV1, together with a newly developed consensus clustering framework, which allowed us to extend the analysis beyond binary physical interactions and revealed a system-level layout of higher-order functional associations in the virion proteome. Additionally, the analysis provided new functional annotation for the currently undercharacterised protein pUS10. In-depth bioinformatics sequence analysis unravelled structural features in pUS10 reminiscent of those observed in some capsid-associated proteins in tailed bacteriophages, with which herpesviruses are believed to share a common ancestry. Using immunoaffinity purification (IP)–mass spectrometry (MS), we obtained additional support for our bioinformatically predicted interaction between pUS10 and the inner tegument protein pUL37, which binds cytosolic capsids, contributing to initial tegumentation and eventually virion maturation. In summary, this study unveils new, to our knowledge, insights at both the system and molecular levels that can help us better understand the complexity behind herpesvirus infections.

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

BackgroundHuman cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.MethodsIn this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.ResultsApplied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.ConclusionsOur analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

Molecular classification and subtype-specific drug sensitivity research of uterine carcinosarcoma under multi-omics framework

ABSTRACTObjective: Uterine carcinosarcomas (UCSs) are aggressive rare tumors recognized as malignancies composed of metaplastic transformation of epithelial elements. Nay no comprehensive molecular classification has been applied to UCS to guide targeted therapies so far, which motivated us to subtyping UCS by aggregating multiple genomic platform data.Methods: We classified UCS into three distinct subtypes with different clinicopathologic and molecular characterization by using similarity network fusion under consensus clustering framework (SNFCC+) to aggregate four genomic data platforms of 55 UCS patients. Differences across subtypes were extracted by functional enrichment, gene mutations and clinical features. Subtypes were further distinguished by putative biomarkers. We also determined associations between individual oncogenes and chemotherapeutics to discuss subtype-specific drug sensitivity.Results: Functional enrichment analysis of the subtype-specific differential expression genes endowed three subtypes new designation: Myo, Cell and Hormone. Mutations in PTEN, PIK3CA, ARID1A and PPP2R1A altered across subtypes. The epithelial-to-mesenchymal transition (EMT) score distinguished Myo from another two subtypes whereby a high EMT scores prevalently existed and each case was judged as M (mesenchymal) phenotype in Myo subtype. Through the drug sensitivity analysis, we found that the response to – tinib drugs is quite different across subtypes according to expression level. Additionally, different subtypes’ response to broad-spectrum anti-cancer drug paclitaxel may be also different.Conclusions: In this study, we identified three distinct molecular subtypes of UCS with different features. Subtypes were also revealed to have different sensitivity to existing chemotherapy drugs, which may support in-depth study of subtype-specific dosing regimens.

Greedy optimization for K-means-based consensus clustering

Consensus clustering aims to fuse several existing basic partitions into an integrated one; this has been widely recognized as a promising tool for multi-source and heterogeneous data clustering. Owing to robust and high-quality performance over traditional clustering methods, consensus clustering attracts much attention, and much efforts have been devoted to develop this field. In the literature, the K-means-based Consensus Clustering (KCC) transforms the consensus clustering problem into a classical K-means clustering with theoretical supports and shows the advantages over the state-of-the-art methods. Although KCC inherits the merits from K-means, it suffers from the initialization sensitivity. Moreover, the current consensus clustering framework separates the basic partition generation and fusion into two disconnected parts. To solve the above two challenges, a novel clustering algorithm, named Greedy optimization of K-means-based Consensus Clustering (GKCC) is proposed. Inspired by the well-known greedy K-means that aims to solve the sensitivity of K-means initialization, GKCC seamlessly combines greedy K-means and KCC together, achieves the merits inherited by GKCC and overcomes the drawbacks of the precursors. Moreover, a 59-sampling strategy is conducted to provide high-quality basic partitions and accelerate the algorithmic speed. Extensive experiments on 36 benchmark datasets demonstrate the significant advantages of GKCC over KCC and KCC++ in terms of the objective function values and standard deviations and external cluster validity.

Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data

Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.

A robust unsupervised consensus control chart pattern recognition framework

Early identification and detection of abnormal patterns is vital for a number of applications. In manufacturing for example, slide shifts and alterations of patterns might be indicative of some production process anomaly, such as machinery malfunction. Usually due to the continuous flow of data, monitoring of manufacturing processes and other types of applications requires automated control chart pattern recognition (CCPR) algorithms. Most of the CCPR literature consists of supervised classification algorithms. Fewer studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough and might vary significantly from one algorithm to another. In this paper, we propose the use of a consensus clustering framework that takes care of this shortcoming and produces results that are robust with respect to the chosen pool of algorithms. Computational results show that the proposed method achieves not less than 79.10% G-mean with most of test instances achieving higher than 90%. This happens even when in the algorithmic pool are included algorithms with performance less than 15%. To our knowledge, this is the first paper proposing an unsupervised consensus learning approach in CCPR. The proposed approach is promising and provides a new research direction in unsupervised CCPR literature.

SC³: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles

In order to perform successful diagnosis and treatment of cancer, discovering, and classifying cancer types correctly is essential. One of the challenging properties of class discovery from cancer data sets is that cancer gene expression profiles not only include a large number of genes, but also contains a lot of noisy genes. In order to reduce the effect of noisy genes in cancer gene expression profiles, we propose two new consensus clustering frameworks, named as triple spectral clustering-based consensus clustering (SC3) and double spectral clustering-based consensus clustering (SC2Ncut) in this paper, for cancer discovery from gene expression profiles. SC3 integrates the spectral clustering (SC) algorithm multiple times into the ensemble framework to process gene expression profiles. Specifically, spectral clustering is applied to perform clustering on the gene dimension and the cancer sample dimension, and also used as the consensus function to partition the consensus matrix constructed from multiple clustering solutions.Compared with SC3, SC2Ncut adopts the normalized cut algorithm, instead of spectral clustering, as the consensus function.Experiments on both synthetic data sets and real cancer gene expression profiles illustrate that the proposed approaches not only achieve good performance on gene expression profiles, but also outperforms most of the existing approaches in the process of class discovery from these profiles.

Robust consensus clustering for identification of expressed genes linked to malignancy of human colorectal carcinoma.

Previous studies have been conducted in gene expression profiling to identify groups of genes that characterize the colorectal carcinoma disease. Despite the success of previous attempts to identify groups of genes in the progression of the colorectal carcinoma disease, their methods either require subjective interpretation of the number of clusters, or lack stability during different runs of the algorithms. All of which limits the usefulness of these methods. In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis. Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework. The results of the experiment show that the robustness of our method provides a consistent structure of clusters, similar to the structure found in the previous study. Furthermore, our algorithm outperforms any single clustering algorithms in terms of the cluster quality score.