Pvclust: an R package for assessing the uncertainty in hierarchical clustering

By Ryota Suzuki, Hidetoshi Shimodaira. Bioinform., 2006

2,414 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/41ec6663f08d5b9d0ca52dae62e7924f3ebda09a

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In Molecular Biology

Citations in the corpus

In vitro generation of human pluripotent stem cell derived lung organoids (2015, methodology): “Analyses were conducted using R package ‘pvclust’ (Suzuki and Shimodaira, 2006).”
Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice (2011, methodology): “Cluster reliability was assessed using the R package pvclust (Suzuki and Shimodaira, 2006).”
Human embryonic stem cells with biological and epigenetic characteristics similar to those of mouse ESCs (2010, background): “An unbiased cross-species hierarchical clustering (35) was performed to assess whether the naïve andprimed state of pluripotency in human cells globally corresponded to those characterized in mouse cells.”
dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering (2015, background): “, 2008), as well as of ‘significant’ clusters based on the pvclust package (Suzuki and Shimodaira, 2006).”
Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw (2011, methodology): “Hierarchical clustering was performed using the R package pvclus”
Insights into Ciliary Genes and Evolution from Multi-Level Phylogenetic Profiling (2017, methodology): “Bootstrapped hierarchical clustering was realized in R, using the package pvclust (Suzuki and Shimodaira 2006) with binary distances, the Ward algorithm (Ward 1963) and 10,000 bootstrap replicates.”
Food in a row: urban trees offer valuable floral resources to pollinating insects (2016, methodology): “We estimated the uncertainty in hierarchical cluster analysis with p-values calculated using multiscale bootstrap resampling (R-package pvclust; Suzuki and Shimodaira, 2006).”
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, methodology): “Finally we clustered the 69 plus 16 metabolic networks according to their similarity in terms of shared metabolic genes using unsupervised hierarchical clustering with average linkage and multiscale bootstrap resampling [27] (10,000 repetitions) implemented in the R statistical software (see Figure ”
Food‐web structure varies along environmental gradients in a high‐latitude marine ecosystem (2018, methodology): “The multivariate analyses were performed with the statistical packages vegan and Pvclust (Suzuki and Shimodaira 2006, Oksanen et al.”