optCluster: An R Package for Determining the Optimal Clustering Algorithm

Sekula, Michael; Datta, Somnath; Datta, Susmita

doi:10.6026/97320630013101

Cited by 43 publications

(37 citation statements)

References 7 publications

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“…The R package optCluster (R ver. 3.4.3) was then used to determine the best clustering algorithm among agnes, clara, diana, hierarchical, kmeans, pam, and sota clustering methods using the validation methods connectivity, Dunn index and silhouette width, and the number of clusters set to ten 83 . Clusters that showed similar expression profiles were then merged.…”

Section: Methodsmentioning

confidence: 99%

Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints

Bezawork-Geleta

Dong

et al. 2018

Nat Commun

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Cell growth and survival depend on a delicate balance between energy production and synthesis of metabolites. Here, we provide evidence that an alternative mitochondrial complex II (CII) assembly, designated as CIIlow, serves as a checkpoint for metabolite biosynthesis under bioenergetic stress, with cells suppressing their energy utilization by modulating DNA synthesis and cell cycle progression. Depletion of CIIlow leads to an imbalance in energy utilization and metabolite synthesis, as evidenced by recovery of the de novo pyrimidine pathway and unlocking cell cycle arrest from the S-phase. In vitro experiments are further corroborated by analysis of paraganglioma tissues from patients with sporadic, SDHA and SDHB mutations. These findings suggest that CIIlow is a core complex inside mitochondria that provides homeostatic control of cellular metabolism depending on the availability of energy.

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Section: Methodsmentioning

confidence: 99%

Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints

Bezawork-Geleta

Dong

et al. 2018

Nat Commun

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“…We employed the R package optCluster (R version 3.4.3) to determine the optimal clustering method and the optimal number of clusters [16]. We implemented the five clustering methods (agglomerative hierarchical, hierarchical divisive, K-means, K-medoids and model-based clustering) with the number of clusters ranging between 4-10 and evaluated the clustering results using the Dunn index, silhouette width and adjusted connectivity as validating metrics.…”

Section: Cluster Validationmentioning

confidence: 99%

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Sangkaew

Tan

et al. 2020

Parasites Vectors

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Background: Dengue is a mosquito-borne viral disease caused by one of four serotypes (DENV1-4). Infection provides long-term homologous immunity against reinfection with the same serotype. Plaque reduction neutralization test (PRNT) is the gold standard to assess serotype-specific antibody levels. We analysed serotype-specific antibody levels obtained by PRNT in two serological surveys conducted in Singapore in 2009 and 2013 using cluster analysis, a machine learning technique that was used to identify the most common histories of DENV exposure. Methods: We explored the use of five distinct clustering methods (i.e. agglomerative hierarchical, divisive hierarchical, K-means, K-medoids and model-based clustering) with varying number (from 4 to 10) of clusters for each method. Weighted rank aggregation, an evaluating technique for a set of internal validity metrics, was adopted to determine the optimal algorithm, comprising the optimal clustering method and the optimal number of clusters. Results: The K-means algorithm with six clusters was selected as the algorithm with the highest weighted rank aggregation. The six clusters were characterised by (i) dominant DENV2 PRNT titres; (ii) co-dominant DENV1 and DENV2 titres with average DENV2 titre > average DENV1 titre; (iii) co-dominant DENV1 and DENV2 titres with average DENV1 titre > average DENV2 titre; (iv) low PRNT titres against DENV1-4; (v) intermediate PRNT titres against DENV1-4; and (vi) dominant DENV1-3 titres. Analyses of the relative size and age-stratification of the clusters by year of sample collection and the application of cluster analysis to the 2009 and 2013 datasets considered separately revealed the epidemic circulation of DENV2 and DENV3 between 2009 and 2013. Conclusion: Cluster analysis is an unsupervised machine learning technique that can be applied to analyse PRNT antibody titres (without pre-established cutoff thresholds to indicate protection) to explore common patterns of DENV infection and infer the likely history of dengue exposure in a population.

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“…This method has been proven as an efficient way to indirectly evaluate the phenotypes of TME (Wang et al, 2018). By using optCluster (Sekula et al, 2017) to evaluate the internal and stability indexes of the seven clustering algorithms (clara, diana, hierarchical, kmeans, model, pam, and sota), the optimal number and the algorithm of clustering were determined. Finally, the Clara algorithm and three groups were selected as the most robust clustering parameters.…”

Section: Unsupervised Clustering Algorithm To Determine Tme Subtypes mentioning

confidence: 99%

Analysis of Gene Signatures of Tumor Microenvironment Yields Insight Into Mechanisms of Resistance to Immunotherapy

Wang

Liu

Ran

et al. 2020

Front. Bioeng. Biotechnol.

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Background: The recent clinical success of immunotherapy represents a turning point in cancer management. But the response rate of immunotherapy is still limited. The inflamed tumor microenvironment has been reported to correlate with response in tumor patients. However, due to the lack of appropriate experimental methods, the reason why the immunotherapeutic resistance still existed on the inflamed tumor microenvironment remains unclear. Materials and Methods: Here, based on single-cell RNA sequencing, we classified the tumor microenvironment into inflamed immunotherapeutic responsive and inflamed non-responsive. Then, phenotype-specific genes were identified to show mechanistic differences between distant microenvironment phenotypes. Finally, we screened for some potential drugs that can convert an unfavorable microenvironment phenotype to a favorable one to aid current immunotherapy. Results: Multiple signaling pathways were phenotypes-specific dysregulated. Compared to non-inflamed microenvironment, the expression of interleukin signaling pathways-associated genes was upregulated in inflamed microenvironment. Compared to inflamed responsive microenvironment, the PPAR signaling pathway-related genes and multiple epigenetic pathways-related genes were, respectively, suppressed and upregulated in the inflamed non-responsive microenvironment, suggesting a potential mechanism of immunotherapeutic resistance. Interestingly, some of the identified phenotype-specific gene signatures have shown their potential to enhance the efficacy of current immunotherapy. Conclusion: These results may contribute to the mechanistic understanding of immunotherapeutic resistance and guide rational therapeutic combinations of distant targeted chemotherapy agents with immunotherapy.

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optCluster: An R Package for Determining the Optimal Clustering Algorithm

Cited by 43 publications

References 7 publications

Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints

Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Analysis of Gene Signatures of Tumor Microenvironment Yields Insight Into Mechanisms of Resistance to Immunotherapy

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