CLICOM: Cliques for combining multiple clusterings

Mimaroglu, Selim; Yağcı, A. Murat

doi:10.1016/j.eswa.2011.08.059

Cited by 17 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(A) Clustering analysis of the data using modified CLICOM clustering, using five underlying clustering setups, evidence threshold of 50%, and a minimal cluster size of 75 (Mimaroglu and Yagci, 2012). Clusters are sorted by their size.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, RNAlysis allows users to cluster genes based on the similarity of their expression patterns. RNAlysis supports an extensive selection of clustering algorithms, including distance-based clustering (K-Means, K-Medoids, Hierarchical clustering), density-based clustering (HDBSCAN) (McInnes et al, 2017), and ensemble-based clustering (a modified version of the CLICOM algorithm) (Mimaroglu and Yagci, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RNAlysis: analyze your RNA sequencing data without writing a single line of code

Teichman

Cohen²,

Ganon

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Amongst the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code, since the majority of available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. Results: We have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files, through exploratory data analysis and data visualization, clustering analysis, and gene-set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA data from different studies using C. elegans nematodes. We note that the software is equally applicable to data obtained from any organism. Conclusions: RNAlysis is suitable for investigating a variety of biological questions, and allows researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RNAlysis: analyze your RNA sequencing data without writing a single line of code

Teichman

Cohen²,

Ganon

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A cluster is organized as Undirected, Weighted and Complete graph [3], [12]. Vertices of the graph are the clusters and weights of the edges are the Similarity Within Cluster (SWC) values between corresponding clusters with n vertices and n (n-1) edges.…”

Section: E Undirected Weighted Graph Constructionmentioning

confidence: 99%

Clustering fusion with automatic cluster number

Muneeswaran

Velvizhy

Kannan

2014

2014 International Conference on Recent Trends in Information Technology

View full text Add to dashboard Cite

Most of the real world applications use data clustering techniques for effective data analysis. All clustering techniques have some assumptions on the underlying dataset. We can get accurate clusters if the assumptions hold good. But it is difficult to satisfy all assumptions. Currently, not a single clustering algorithm is available to find all types of cluster shapes and structures. Therefore, an ensemble clustering algorithm is proposed in this paper in order to produce accurate clusters. Moreover, the existing clustering ensemble methods require more number of clusters in advance to produce final clusters. In this paper, we propose a novel method which groups a set of clusters into accurate final clusters to enhance the decision accuracy. This method does not need the number of clusters as input but produces the clusters automatically assuming the no of clusters. Index Terms-Clustering, Clustering ensemble.I.

show abstract

“…For example, if we assumed that the co-occurrence matrix represented edge weights (a numerical value indicating the strength of connection) connecting the data points, we could traverse these weights to find maximally connected subgraphs and provide a different representation of robust clusters (61). With this concept of graph theory representations of ensemble clustering, we discovered that robustly clustered dynamic tyrosine phosphorylation data uncovered molecularlevel interactions (8).…”

Section: Ensemble Generation Finishing and Visualizationmentioning

confidence: 99%

Avoiding common pitfalls when clustering biological data

2016

View full text Add to dashboard Cite

Clustering is an unsupervised learning method, which groups data points based on similarity, and is used to reveal the underlying structure of data. This computational approach is essential to understanding and visualizing the complex data that are acquired in high-throughput multidimensional biological experiments. Clustering enables researchers to make biological inferences for further experiments. Although a powerful technique, inappropriate application can lead biological researchers to waste resources and time in experimental follow-up. We review common pitfalls identified from the published molecular biology literature and present methods to avoid them. Commonly encountered pitfalls relate to the high-dimensional nature of biological data from high-throughput experiments, the failure to consider more than one clustering method for a given problem, and the difficulty in determining whether clustering has produced meaningful results. We present concrete examples of problems and solutions (clustering results) in the form of toy problems and real biological data for these issues. We also discuss ensemble clustering as an easy-to-implement method that enables the exploration of multiple clustering solutions and improves robustness of clustering solutions. Increased awareness of common clustering pitfalls will help researchers avoid overinterpreting or misinterpreting the results and missing valuable insights when clustering biological data.

show abstract

CLICOM: Cliques for combining multiple clusterings

Cited by 17 publications

References 15 publications

RNAlysis: analyze your RNA sequencing data without writing a single line of code

RNAlysis: analyze your RNA sequencing data without writing a single line of code

Clustering fusion with automatic cluster number

Avoiding common pitfalls when clustering biological data

Contact Info

Product

Resources

About