WGCNA - Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
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cpp
9.88 score 57 stars 38 dependents 6.6k scripts 18k downloadsflashClust - Implementation of Fast Hierarchical Clustering
A fast implementation of hierarchical clustering that incorporates original code from Fionn Murtagh.
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7.22 score 126 dependents 530 scripts 83k downloadsdynamicTreeCut - Methods for Detection of Clusters in Hierarchical Clustering Dendrograms
Contains methods for detection of clusters in hierarchical clustering dendrograms.
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6.93 score 4 stars 65 dependents 650 scripts 17k downloadsrandomGLM - Random General Linear Model Prediction
A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.
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1.53 score 1 stars 34 scripts 296 downloadsmoduleColor - Basic Module Functions
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
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1.30 score 20 scripts 527 downloadsPropClust - Propensity Clustering and Decomposition
Implementation of propensity clustering and decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. Propensity decomposition can be viewed on the one hand as a generalization of the eigenvector-based approximation of correlation networks, and on the other hand as a generalization of random multigraph models and conformity-based decompositions.
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fortran
1.00 score 2 scripts 209 downloads