In alan-turing-institute/network-comparison: An implementation of the NetEMD alignment-free network distance measure

Virus PPI example for Netdis

Load graphs

Use read_simple_graphs to read graph data from all files in a directory that match a specific filename pattern in a format suitable for calculating graphlet-based feature counts using the ORCA package. We use igraph::read_graph to read graph data from files, so support all file formats it supports. See help for igraph::read_graph for a list of supported values for the format parameter and the igraph documentation for descriptions of each of the supported file formats.

The ORCA package we use to efficiently calculate graphlet and orbit counts requires that graphs are undirected, simple (i.e. have no self-loops or multiple edges) and connected (i.e. have no isolated vertices). Therefore, by default, graphs loaded by read_simple_graphs will be coerced to have the above properties. This can be avoided by setting the relevant as_undirected, remove_loops, remove_multiple or remove_isolates parameters to FALSE.

library("netdist")
edge_format = "ncol"
# Load reference graph (used for Netdis. Not required for NetEMD)
ref_path = file.path(system.file(file.path("extdata", "random"),
                                 package = "netdist"),
                     "ER_1250_10_1")
ref_graph <- read_simple_graph(ref_path, format = edge_format)

# Set source directory and file properties for Virus PPI graph edge files
source_dir <- system.file(file.path("extdata", "VRPINS"),
                          package = "netdist")
edge_format <- "ncol"
file_pattern <- "*"

# Load all graphs in the source folder matching the filename pattern
query_graphs <- read_simple_graphs(source_dir,
                                   format = edge_format, 
                                   pattern = file_pattern)
print(names(query_graphs))

In this example we will use counts of graphlets containing up to 4 nodes and consider ego-network neighbourhoods of size 2 (i.e. the immediate neighbours of each node plus their immediate neighbours).

# Set the maximum graphlet size to compute counts for
max_graphlet_size <- 4
neighbourhood_size <- 2

Generate NetDis measures between each pair of query graphs

# Calculate netdis measure for graphlets up to size max_graphlet_size
netdis_result <- netdis_many_to_many(query_graphs,
                                     ref_graph,
                                     max_graphlet_size = max_graphlet_size,
                                     neighbourhood_size = neighbourhood_size)

# Netdis measure for graphlets of size 3
res3 <- netdis_result$netdis["netdis3", ]
netdis3_mat <- cross_comp_to_matrix(res3, netdis_result$comp_spec)

print("Netdis: graphlet size = 3")
print(netdis3_mat)

# Netdis measure for graphlets of size 4
res4 <- netdis_result$netdis["netdis4", ]
netdis4_mat <- cross_comp_to_matrix(res4, netdis_result$comp_spec)

print("Netdis: graphlet size = 4")
print(netdis4_mat)

Generate dendrograms

graphdists <- as.dist(netdis4_mat)
par(mfrow = c(1, 2))
cex <- 1

# Dendrogram based on Netdis measure for graphlets of size 3
title <- paste("Netdis: graphlet size = ", 3, sep = "")
plot(phangorn::upgma(as.dist(netdis3_mat), method = "average"),
     use.edge.length = FALSE, 
     edge.width = cex*2,
     main = title,
     cex.lab = cex, cex.axis = cex,
     cex.main = cex, cex.sub = cex,
     cex = cex)

# Dendrogram based on Netdis measure for graphlets of size 4
title = paste("Netdis: graphlet size = ", 4, sep = "")
plot(phangorn::upgma(as.dist(netdis4_mat), method = "average"),
     use.edge.length = FALSE, 
     edge.width = cex*2,
     main = title,
     cex.lab = cex, cex.axis = cex,
     cex.main = cex, cex.sub = cex,
     cex = cex)

cex <- 1.5
col <- colorRampPalette(colors = c("blue","white"))(100)
title <- paste("Netdis: graphlet size = ", 3, sep = "")
heatmap(netdis3_mat, Rowv = NULL, Colv = NULL, col = col, main = title,
        cexRow = cex, cexCol = cex, symm = TRUE)

cex <- 1.5
col <- colorRampPalette(colors = c("blue","white"))(100)
title <- paste("Netdis: graphlet size = ", 4, sep = "")
heatmap(netdis4_mat, Rowv = NULL, Colv = NULL, col = col, main = title,
        cexRow = cex, cexCol = cex, symm = TRUE)

alan-turing-institute/network-comparison documentation built on June 7, 2022, 10:41 p.m.