Functions for labeling the clusters in network graph plots with their cluster IDs. The user can specify a cluster-level property by which to rank the clusters, labeling only those clusters above a specified rank.
labelClusters(
net,
plots = NULL,
top_n_clusters = 20,
cluster_id_col = "cluster_id",
criterion = "node_count",
size = 5, color = "black",
greatest_values = TRUE
)addClusterLabels(
plot,
net,
top_n_clusters = 20,
cluster_id_col = "cluster_id",
criterion = "node_count",
size = 5,
color = "black",
greatest_values = TRUE
)
labelClusters()
returns a copy of net
with the specified plots
annotated.
addClusterLabels()
returns an annotated copy of plot
.
A list
of network objects conforming to the output of
buildRepSeqNetwork()
or
generateNetworkObjects()
.
See details.
Specifies which plots in net$plots
to annotate.
Accepts a character vector of element names or a numeric vector of element
position indices.
The default NULL
annotates all plots.
A ggraph
object containing the network graph plot.
A positive integer specifying the number of clusters to label. Those with the
highest rank according to the criterion
argument will be labeled.
Specifies the column of net$node_data
containing
the variable for cluster membership.
Accepts a character string containing the column name.
Can be used to specify a cluster-level network property by which to rank the
clusters. Non-default values are ignored unless net$cluster_data
exists and
corresponds to the cluster membership variable specified by cluster_id_col
.
Accepts a character string containing a column name of net$cluster_data
.
The property must be quantitative for the ranking to be meaningful.
By default, clusters are ranked by node count, which is computed based on the
cluster membership values if necessary.
The font size of the cluster ID labels. Passed to the size
argument of
geom_node_text()
.
The color of the cluster ID labels. Passed to the color
argument of
geom_node_text()
.
Logical. Controls whether clusters are ranked according to the greatest or
least values of the property specified by the criterion
argument. If
TRUE
, clusters with greater values will be ranked above those with
lower values, thereby receiving a higher priority to be labeled.
Brian Neal (Brian.Neal@ucsf.edu)
The list net
must contain the named elements
igraph
(of class igraph
),
adjacency_matrix
(a matrix
or
dgCMatrix
encoding edge connections),
and node_data
(a data.frame
containing node metadata),
all corresponding to the same network. The lists returned by
buildRepSeqNetwork()
and
generateNetworkObjects()
are examples of valid inputs for the net
argument.
Hai Yang, Jason Cham, Brian Neal, Zenghua Fan, Tao He and Li Zhang. (2023). NAIR: Network Analysis of Immune Repertoire. Frontiers in Immunology, vol. 14. doi: 10.3389/fimmu.2023.1181825
addClusterMembership()
,
getClusterStats()
,
generateNetworkGraphPlots()
set.seed(42)
toy_data <- simulateToyData()
network <- buildRepSeqNetwork(
toy_data, "CloneSeq",
cluster_stats = TRUE,
color_nodes_by = "cluster_id",
color_scheme = "turbo",
color_legend = FALSE,
plot_title = NULL,
plot_subtitle = NULL,
size_nodes_by = 1
)
network <- labelClusters(network)
network$plots$cluster_id
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