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difconet  

Differential Coexpressed Networks
View on CRAN: Click here


Download and install difconet package within the R console
Install from CRAN:
install.packages("difconet")

Install from Github:
library("remotes")
install_github("cran/difconet")

Install by package version:
library("remotes")
install_version("difconet", "1.0-4")



Attach the package and use:
library("difconet")
Maintained by
Victor Trevino
[Scholar Profile | Author Map]
First Published: 2017-05-31
Latest Update: 2017-05-31
Description:
Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see for further information.
How to cite:
Victor Trevino (2017). difconet: Differential Coexpressed Networks. R package version 1.0-4, https://cran.r-project.org/web/packages/difconet. Accessed 09 May. 2025.
Previous versions and publish date:
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Complete documentation for difconet
Functions, R codes and Examples using the difconet R package
Some associated functions: difconet.build.controlled.dataset . difconet.noise.inspection . difconet.plot.gene.correlations . difconet.plot.histograms.heatmap2 . difconet.run . 
Some associated R codes: difconet.R .  Full difconet package functions and examples
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