Package 'INetTool'

adj_rename

Description

This function constructs a list of adjacency matrices with the same row and column names for all the matrices. The output is the object needed for consensusNet function.

Usage

adj_rename(adjL)

Arguments

adjL	list of adjacency matrices

Value

a list of adjacency matrices with the same rows and columns name.

Examples

data("tryL_data")
adj_rename(tryL_data)

---

Adjacency Data

Description

List of 2 adjacency matrices data type.

Usage

adjL_data

Format

## 'adjL_data' A list of 2 objects:

AdjMatrix1

Adjacency matrix;

AdjMatrix2

Adjacency matrix.

---

consensusNet

Description

This function computes the INet Algorithm for the construction of a **Consensus Network**.

Usage

consensusNet(
  adjL,
  threshold = 0.5,
  tolerance = 0.1,
  theta = 0.04,
  nitermax = 50,
  ncores = 2,
  verbose = TRUE
)

Arguments

adjL	list of weighted adjacency matrix with weights in [0,1]. Same name in rows and columns for all the matrices.
threshold	threshold for the construction of the Consensus (default 0.5). Used in the last step on the similar graphs.
tolerance	the tolerance of differences between similar graphs for the construction of the Consensus (default 0.1).
theta	importance to give to the neighbourhood part of the weight (default 0.04).
nitermax	maximum number of iteration before stopping the algorithm (default 50).
ncores	number of CPU cores to use (default is 2). We suggest to use ncores equal to the number of graphs to integrate.
verbose	flag for verbose output (default as TRUE).

Value

a list of 3 types: $graphConsensus the Consensus Network, $Comparison the Jaccard weighted distances between the graphs calculated in each iteration, $similarGraphs the similar graphs before the Thresholding

Examples

data("adjL_data")
consensusNet(adjL_data)

---

constructionGraph

Description

This function constructs graphs from data with pearson correlation and proportional thresholding (the data should be with the same names (the nodes) in columns for all the matrices).

Usage

constructionGraph(data, perc = 0.95)

Arguments

data	a list of datasets
perc	percentile (default 0.95 it takes the 5 percent of the highest weights)

Value

Threshold information (highest weight, number of edges, number of nodes, modularity with louvain method), graphs in a list for each layer and weighted adjacency matrices in a list for each layer.

Examples

data("exampleL_data")
constructionGraph(exampleL_data)

---

densityNet

Description

This function creates a density plot of the different graphs mean weights. It can be used to search the final Threshold for the Consensus Network starting from similar networks.

Usage

densityNet(graphL)

Arguments

graphL

the list of weighted graphs in igraph format.

Value

the quantile of the mean density distribution, the quantile of the mean density distribution without the zeros, plot density distribution without the zeros

Examples

data("graphL_data")
densityNet(graphL_data)

---

Example Data

Description

3 data types: Gene_Expression, Methy_Expression and Mirna_Expression data from patients with Glioblastoma

Usage

exampleL_data

Format

## 'exampleL_data' A list of 3 objects:

Gene_Expression

subset of Gene expression data;

Methy_Expression

subset of Methylation data;

Mirna_Expression

subset of Mirna data.

Source

<https://portal.gdc.cancer.gov/>

---

Graph Data

Description

List of 2 graphs of igraph class type.

Usage

graphL_data

Format

## 'graphL_data' A list of 2 objects:

Graph1

Graph firt layer;

Graph2

Graph second layer.

---

JWmatrix

Description

This function computes the Jaccard weighted matrix distance between all the pairs of graphs.

Usage

JWmatrix(graphL)

Arguments

graphL

list of graphs as igraph objects with the same nodes.

Value

weighted Jaccard distance matrix

Examples

data("graphL_data")
JWmatrix(graphL_data)

---

JWmean

Description

This function computes the Mean Weighted Jaccard Distance for Multilayer Networks.

Usage

JWmean(graphL)

Arguments

graphL

list of different graphs in igraph format with same nodes.

Value

a number: the mean distance

Examples

data("graphL_data")
JWmean(graphL_data)

---

measuresNet

Description

This function computes graphs and nodes measures to analyse all the layers in one shot.

Usage

measuresNet(graphL, nodes.measures = TRUE)

Arguments

graphL	a list of graphs as igraphs objects.
nodes.measures	logical, if falso it computes only graph measures, if true it computes also nodes measures (default TRUE).

Value

list of measure for each layer.

Examples

data("graphL_data")
measuresNet(graphL_data)

---

plotC

Description

The function plots the network without isolated nodes.

Usage

plotC(graph, ...)

Arguments

graph	a graph
...	other parameter

Value

plot

Examples

data("graphL_data")
plotC(graphL_data[[1]])

---

plotINet

Description

The function plots a beginning network and the consensus in one graph with different edge colors: red edges represent edges of the consensus already present in the beginning one, while light blue edges represent new edges constructed by the consensus procedure.

Usage

plotINet(
  adj,
  graph.consensus,
  edge.width = 3,
  vertex.label.cex = 0.5,
  vertex.size = 10,
  edge.curved = 0.2,
  method = "NA",
  ...
)

Arguments

adj	one of the beginning adjacency matrices
graph.consensus	consensus network, output of the consensusNet function
edge.width	the edge width (default 3)
vertex.label.cex	the size of the vertex label (default 0.8)
vertex.size	the size of the vertex (default 10)
edge.curved	to make the edge curved (default 0.2)
method	community detection method to color the nodes one of "walktrap", "edgeBetweenness", "fastGreedy", "louvain", "spinglass", "leadingEigen", "labelProp", "infomap", "optimal" and "leiden" (default no method)
...	other parameter

Value

Union graph with beginning and consensus edge. Red edges represent edges of the consensus already present in the beginning one, while light blue edges represent new edges constructed by the consensus procedure. Community detection of the beginning graph if added.

Examples

data("adjL_data")
con <- consensusNet(adjL_data)
plotINet(adjL_data[[1]], con$graphConsensus)

---

plotL

Description

This function plots all the layers in one plot(wrapper of plot.multinet).

Usage

plotL(graphL, ...)

Arguments

graphL	List of graphs
...	other parameter

Value

plot of graphs

Examples

data("graphL_data")
plotL(graphL_data)

---

specificNet

Description

The function creates Case Specific Networks one for each layer to give information of the peculiar layer not present in the Consensus.

Usage

specificNet(graphL, graph.consensus)

Arguments

graphL	a list of graphs as igraphs objects.
graph.consensus	graphConsensus output of the consensusNet function.

Value

Case Specific Networks one for each layer and percentage of specificity.

Examples

data("graphL_data")
data("adjL_data")
myConsensus <- consensusNet(adjL_data)
specificNet(graphL_data, myConsensus$graphConsensus)

---

thresholdNet

Description

The function reconstructs the Consensus Network with different thresholding after the consensusNet function starting from similar graphs.

Usage

thresholdNet(sim.graphL, threshold = 0.5)

Arguments

sim.graphL	a list of similarGraphs output of the consensusNet function.
threshold	different threshold to compute.

Value

a new consensus network igraph object.

Examples

data("adjL_data")
myConsensus <- consensusNet(adjL_data)
thresholdNet(myConsensus$similarGraphs)

---

try Data

Description

Random data with different nodes name in a list of 2 adjacency matrices.

Usage

tryL_data

Format

## 'tryL_data' A list of 2 objects:

AdjMatrix1

Adjacency matrix;

AdjMatrix2

Adjacency matrix.

---