Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

Reynolds, Alan; Richards, Graeme; Iglesia, Beatriz de la; Rayward‐Smith, V. J.

doi:10.1007/s10852-005-9022-1

Cited by 514 publications

(387 citation statements)

References 8 publications

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“…These profiles were concatenated with the profiles of 1,267 samples distributed with the IGC. A Jensen-Shannon divergence matrix was calculated using the phyloseq 61 implementation over all profiles, and partitioning around medoids 62 was performed to obtain enterotype annotations for each sample.…”

Section: Methodsmentioning

confidence: 99%

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

et al. 2016

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The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra-and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

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Section: Methodsmentioning

confidence: 99%

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

et al. 2016

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“…In accordance with this, an initial clustering analysis is conducted by an unsupervised learning machine, Partition Around Mediods (PAM; [25]; R package cluster). One advantage of this partition clustering technique relies on the fact that it can work with different similarity measurements other than the Euclidean distance.…”

Section: Classification Processmentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.

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“…The expression of each gene was split into 'high' and 'low' based on Partition Around Medoids clustering. 21,22 The performance of a gene was evaluated using a REM comparing the 10-year metastasis rate in high versus low expression also using the R 'meta' package. Pooled multivariate logistic regression analysis was performed, with age analyzed per year, Gleason and PSA stratified into high/low (Gleason 8-10/ ⩽ 7, PSA48/ ⩽ 8), and stratification by cohort as described previously 18 to account for baseline differences, both measured and unmeasured, between cohorts.…”

Section: Univariate and Multivariate Analysesmentioning

confidence: 99%

High-Throughput Transcriptomic Analysis Nominates Proteasomal Genes As Age Specific Biomarkers and Therapeutic Targets in Prostate Cancer

Zhao

Jackson

Kothari

et al. 2015

International Journal of Radiation Oncology*Biology*Physics

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BACKGROUND: Although prostate cancer (PCa) is hypothesized to differ in nature between younger versus older patients, the underlying molecular distinctions are poorly understood. We hypothesized that high-throughput transcriptomic analysis would elucidate biological differences in PCas arising in younger versus older men, and would nominate potential age-specific biomarkers and therapeutic targets. METHODS: The high-density Affymetrix GeneChip platform, encompassing 41 million genomic loci, was utilized to assess gene expression in 1090 radical prostatectomy samples from patients with long-term follow-up. We identified genes associated with metastatic progression by 10 years post-treatment in younger (ageo65) versus older (age ⩾ 65) patients, and ranked these genes by their prognostic value. We performed Gene Set Enrichment Analysis (GSEA) to nominate biological concepts that demonstrated agespecific effects, and validated a target by treating with a clinically available drug in three PCa cell lines derived from younger men. RESULTS: Over 80% of the top 1000 prognostic genes in younger and older men were specific to that age group. GSEA nominated the proteasome pathway as the most differentially prognostic in younger versus older patients. High expression of proteasomal genes conferred worse prognosis in younger but not older men on univariate and multivariate analysis. Bortezomib, a Food and Drug Administration approved proteasome inhibitor, decreased proliferation in three PCa cell lines derived from younger patients. CONCLUSIONS: Our data show significant global differences in prognostic genes between older versus younger men. We nominate proteasomeal gene expression as an age-specific biomarker and potential therapeutic target specifically in younger men. Limitations of our study include clinical differences between cohorts, and increased comorbidities and lower survival in older patients. These intriguing findings suggest that current models of PCa biology do not adequately represent genetic heterogeneity of PCa related to age, and future clinical trials would benefit from stratification based on age.

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Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

Cited by 514 publications

References 8 publications

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

Water End Use Disaggregation Based on Soft Computing Techniques

High-Throughput Transcriptomic Analysis Nominates Proteasomal Genes As Age Specific Biomarkers and Therapeutic Targets in Prostate Cancer

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