Dynamic Tree Cut statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.


Citations per year

Citations chart

Popular tool citations

chevron_left Gene expression clustering chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases


To access compelling stats and trends, optimize your time and resources and pinpoint new correlations, you will need to subscribe to our premium service.


Dynamic Tree Cut specifications


Unique identifier OMICS_23803
Name Dynamic Tree Cut
Alternative name dynamicTreeCut
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 1.63-1
Stability Stable
Source code URL https://cran.r-project.org/src/contrib/dynamicTreeCut_1.63-1.tar.gz
Maintained No



Add your version



This tool is not available anymore.

Additional information

https://cran.r-project.org/web/packages/dynamicTreeCut/index.html https://cran.r-project.org/web/packages/moduleColor/index.html https://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/BranchCutting/InstallationInstructions.html

Publication for Dynamic Tree Cut

Dynamic Tree Cut in pipelines

PMCID: 5902692
PMID: 29692794
DOI: 10.3389/fpls.2018.00470

[…] co-expression network was visualized using cytoscape v3.4 (), and the network topology parameters were calculated using the networkanalyzer plugin (). co-expression modules were identified using dynamic tree cut software ()., gene ontology (go) enrichment analyses were performed using bingo software (). hypergeometric tests with an fdr of 5% as a cutoff were used to select significantly […]

PMCID: 5404332
PMID: 28438154
DOI: 10.1186/s12931-017-0558-1

[…] genes. to identify modules of highly co-expressed genes, we used average linkage hierarchical clustering to group genes based on the topological overlap of their connectivity, followed by a dynamic tree-cut algorithm to cluster dendrogram branches into gene modules []. each of the resulting modules was assigned a color. for each gene, we calculated a module membership (mm) whose values […]

PMCID: 5457141
PMID: 28489004
DOI: 10.7554/eLife.23253.044

[…] from these processed expression data, we followed the protocols of wgcna () to create a gene co-expression network. modules were defined as branches of a hierarchical cluster tree using the top-down dynamic tree cut method (). for each module, the expression patterns were summarized by the module eigengene (me), defined as the singular vector of the standardized expression patterns. pairs […]

PMCID: 5648782
PMID: 29051502
DOI: 10.1038/s41598-017-13176-8

[…] biological variance between our samples as already indicated by the clustering and mds analyses (fig.  and supplementary fig. ). however, subsequent quantification of module similarity revealed that dynamictreecut might have identified modules which are very similar (supplementary fig. ). therefore modules were merged based on module eigengene correlations of 0.9 (medissthres = 0.1). although […]

PMCID: 4765147
PMID: 26911482
DOI: 10.1186/s12864-016-2476-x

[…] 3500 to save running time, and the threshold for network output was set as 0.5 to achieve more stringent connectivity of nodes in the network. co-expressed genes were clustered by applying tom and dynamictreecut functions to form different co-expression modules. a unique color was assembled to name each module. correlations of ips with modules were calculated using the cor() function in r […]

To access a full list of citations, you will need to upgrade to our premium service.

Dynamic Tree Cut in publications

PMCID: 5958058
PMID: 29773832
DOI: 10.1038/s41467-018-04383-6

[…] distance and ward linkage metrics. clustering of chromatin states regions was performed using hierarchical clustering and the number of clusters was determined using the cutreedynamic function (dynamictreecut r package) with a minimum size module of 500 features., chromhmm was used to perform hidden markov modeling on the five histone marks and, by default, chromatin states were analyzed […]

PMCID: 5943461
PMID: 29743727
DOI: 10.1038/s41598-018-25760-7

[…] beta value satisfying the scale free topology criteria (optimal beta equal to 5 in rpmf group and 8 in rlf group). we set up values 2 and 10 as the parameters of deepsplit and minmodulesize for the dynamic tree cut function. the module eigenotu, which was defined as the first principle component of a module, was used to calculate the pearson correlation between a module and a metabolic trait. […]

PMCID: 5914069
PMID: 29685133
DOI: 10.1186/s12920-018-0358-6

[…] of a topological overlap measure (tom) dissimilarity matrix, which serves as input for average linkage hierarchical clustering. branches from the resulting tree were divided into modules using the dynamictreecut algorithm with the option deepsplit = 0. the choice of the value that specifies the sensitivity of splitting clusters was guided by visual inspection of the tom plot, a color-coded […]

PMCID: 5905132
PMID: 29665773
DOI: 10.1186/s12864-018-4649-2

[…] is the degree of node i. hierarchical clustering with average linkage method was applied using tom as the distance matrix, followed by the dynamic tree cutting procedure implemented in the r package dynamictreecut [], requiring minimal module size as 20. for each identified module, a wilcoxon signed rank test was applied to the expression alteration scores of proteins in the module. modules […]

PMCID: 5899093
PMID: 29654251
DOI: 10.1038/s41598-018-24341-y

[…] r function on the distances described as (1-coinhibition). sub-clustering was identified in the obtained dendrogram using a dynamic branch cutting method by using the cutreedynamic function of the dynamictreecut r package with the non-default parameter settings minimum cluster size = 3 and deepsplit = 4., by4742 s. cerevisiae cells were grown overnight in ypd medium. the day after, 1 × 109 […]

To access a full list of publications, you will need to upgrade to our premium service.

Dynamic Tree Cut institution(s)
Department of Human Genetics, University of California at Los Angeles, CA, USA; Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
Dynamic Tree Cut funding source(s)
Supported by 1U19AI063603-01 and NINDS/NIMH 1U24NS043562-01.

Dynamic Tree Cut reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review Dynamic Tree Cut