explodelayout: Layout a network using the ExplodeLayout Algorithm.

In DIVA-Lab-UTMB/epl: ExplodeLayout algorithm for network visulization

Layout a network using the ExplodeLayout Algorithm.

Description

The explodelayout function applies the ExplodeLayout Algorithm to help user visuallize and analyze their network data.

Usage

explodelayout(nodelistFile, networkFile, labelOn = "no",
  data_name = "data")

Arguments

nodelistFile	a nodelist, which is a dataframe with first column as the name of nodes
networkFile	a network, which is a matrix or dataframe with first column as the outcome
labelOn	labels of nodes on or not. should be 'yes' or 'no'
data_name	name your data so that we know how to name your results files

Details

The explodelayout function takes as input two main files: 1. A nodelist file which containing the lables, original coordinates, outcome (like case or control), entity, and cluster membership for all nodes in the network. 2. A network file which represents the network.

Data Format: The nodelist file must be a dataframe with 8 columns, named as 'Label', 'FRX', 'FRY' 'KKX', 'KKY', 'Outcome', 'Cluster', 'Entity' The network file must be a dataframe or matrix. When it's a bipartite network, the column names should be the variable names and row names should be the subjects names. When it's unipartite, column names and row names are the same. The first column of the network file should be the outcome of the nodes.

Please note: the names in the network files MUST be consistent with the names in the network files.

Output: the function will export the layout of network with different radius into 21 png files, which includes 1 original layout graph and 20 layout graphs with radius of 0.1, 0.2, ... 1.9, 2.0. The corresponding CCS scores of different radius will be saved into a 'statList.dat' file, which would enable user to pick the best CCS score graph.

References

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543384/

Examples

explodelayout(nl1, net1, 'no','sample_data')

DIVA-Lab-UTMB/epl documentation built on April 11, 2022, 3:46 p.m.