visualClusterInBipartite {NIMAA}

Plot the bipartite graph with color coding for different clusters in both parts

Description

The Sankey diagram is used to depict the connections between clusters within each part of the bipartite network. The display is also interactive, and by grouping nodes within each cluster as "summary" nodes, this function emphasizes how clusters of each part are connected together.

Usage

visualClusterInBipartite(
  data,
  community_left,
  community_right,
  name_left = "Left",
  name_right = "Right"
)

Arguments

data

A data frame or matrix object as an edge list.

community_left

An igraph community object, one projection of the bipartite network to be showed on the left side.

community_right

An igraph community object, the other projection of the bipartite network to be showed on the right side.

name_left

A string value, the name of left community.

name_right

A string value, the name of right community.

Value

A customized Sankey plot with a data frame containing the cluster pairwise relationship with the sum of weight values in the weighted bipartite network.

See Also

plot_ly

Examples

# load part of the beatAML data
beatAML_data <- NIMAA::beatAML[1:1000,]

# convert to incidence matrix
beatAML_incidence_matrix <- nominalAsBinet(beatAML_data)

# extract the Recetengular_element_max submatrix
sub_matrices <- extractSubMatrix(beatAML_incidence_matrix,
col.vars = "patient_id", row.vars = "inhibitor",
shape = c("Rectangular_element_max"))

# do clustering analysis
cls1 <- findCluster(sub_matrices$Rectangular_element_max,
part = 1, comparison = FALSE)

cls2 <- findCluster(sub_matrices$Rectangular_element_max,
part = 2, comparison = FALSE)

visualClusterInBipartite(data = beatAML_data,
community_left = cls2$leading_eigen,
community_right = cls1$fast_greedy,
name_left = 'patient_id',
name_right = 'inhibitor')