neatmap: Explore Multi-Network Data
In neatmaps: Heatmaps for Multiple Network Data

Description Usage Arguments Details Value Author(s) References Examples

neatmap produces a heatmap of multi-network data and identifies stable clusters in its variables.

neatmap(df, scale_df, link_method = "average",
  dist_method = "euclidean", max_k = 10, reps = 1000, p_var = 1,
  p_net = 0.8, cc_seed = 100, main_title = "", xlab, ylab,
  xlab_cex = 1, ylab_cex = 1, heatmap_margins = c(50, 50, 50, 100))

`df`	a dataframe of network attributes containing only numeric values.
`scale_df`	A string indicating whether the columns of the data frame should be scaled, and, if so, which method should be used. The options are "none", "ecdf", "normalize" and "percentize". If "none" is selected, then the columns are not scaled. If "ecdf" is selected, then the columns are transformed into their empirical cumulative distribution. If "normalize" is selected, each column is centered to have a mean of 0 and scaled to have a standard deviation of 1. If "percentize" is selected, column values are transformed into percentiles.
`link_method`	The agglomeration method to be used for hierarchical clustering. Defaults to the average linkage method. See other methods in `hclust`.
`dist_method`	The distance measure to be used between columns and between rows of the dataframe. Distance is used as a measure of similarity. Defaults to euclidean distance. See other options in `dist`.
`max_k`	The maximum number of clusters to consider in the consensus clustering step. Consensus clustering will be performed for max_k-1 iterations, i.e. for 2, 3, ..., max_k clusters. Defaults to 10.
`reps`	The number of subsamples taken at each iteration of the consensus cluster algorithm. Defaults to 1000.
`p_var`	The proportion of network variables to be subsampled during consensus clustering. Defaults to 1.
`p_net`	The proportion of networks to be subsampled during consensus clustering. Defaults to 0.8.
`cc_seed`	The seed used to ensure the reproducibility of the consensus clustering. Defaults to 1.
`main_title`	The title of the heatmap.
`xlab`	The x axis label of the heatmap.
`ylab`	The y axis label of the heatmap.
`xlab_cex`	The font size of the elements on the x axis.
`ylab_cex`	The font size of the elements on the y axis.
`heatmap_margins`	The size of the margins for the heatmap. See `heatmaply`.

This function allows users to efficiently explore their multi-network data by visualizing their data with a heatmap and assessing the stability of the associations presented within it. neatmap requires that the data frame be processed into an appropriate format prior to use. Data is then scaled (if necessary) using of the built in methods. See (list functions) for further details on how to prepare multi-network data for use with neatmap. The heatmap is created using heatmaply and the consensus clustering is performed using ConsensusClusterPlus

A named list containing the heatmap of the multi-network data and a list of length max_k-1 where each element is a list containing the consensus matrix, the consensus hierarchical clustering results and the consensus class assignments. The list of results produced by the consensus clustering can be parsed using following functions in the neatmaps package: consClustResTable, consensusECDF and consensusChangeECDF.

Philippe Boileau, philippe_boileau@berkeley.edu

For more information on the consensus clustering, see Monti et al..

# create the data frame using the network, node and edge attributes
df <- netsDataFrame(network_attr_df,
                    node_attr_df,
                    edge_df)

# run the neatmap code on df
neat_res <- neatmap(df, scale_df = "ecdf", max_k = 3, reps = 100, 
                    xlab = "vars", ylab = "nets", xlab_cex = 1, ylab_cex = 1)

# extract the heatmap
heatmap <- neat_res$heatmap

# extract the consensus clustering results
consensus_res <- neat_res$consensus_clust