Package Recipe 'r-rspectral'

recipe r-rspectral

Implements the network clustering algorithm described in Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity.

Homepage:: https://github.com/cmclean5/rSpectral
License:: GPL2 / GPL-2
Recipe:: /r-rspectral/meta.yaml

package r-rspectral¶

Versions:

1.0.0.14-1, 1.0.0.14-0, 1.0.0.10-3, 1.0.0.10-2, 1.0.0.10-1, 1.0.0.10-0, 1.0.0.9-0

Depends:

on bioconductor-graph >=1.88.1,<1.89.0a0
on libgcc >=14
on libstdcxx >=14
on r-base >=4.5,<4.6.0a0
on r-igraph
on r-rcpp >=1.0.8.3
on r-rcpparmadillo >=0.11.2.0.0
on r-rdpack

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install r-rspectral

to add into an existing workspace instead, run:

pixi add r-rspectral

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install r-rspectral

Alternatively, to install into a new environment, run:

conda create -n envname r-rspectral

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/r-rspectral:<tag>

(see r-rspectral/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

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