calcGCM: Graphlet Correlation Matrix (GCM)

In stefpeschel/NetCoMi: Network Construction and Comparison for Microbiome Data

Graphlet Correlation Matrix (GCM)

Description

Computes the Graphlet Correlation Matrix (GCM) of a network, given as adjacency matrix.

The GCM of a network is a matrix with Spearman's correlations between the network's node orbits (Hocevar and Demsar, 2016; Yaveroglu et al., 2014).

The function considers only orbits for graphlets with up to four nodes. Orbit counts are determined using the function count4 from orca package.

Unobserved orbits would lead to NAs in the correlation matrix, which is why a row with pseudo counts of 1 is added to the orbit count matrix (ocount).

The function is based on R code provided by Theresa Ullmann (https://orcid.org/0000-0003-1215-8561).

Usage

calcGCM(adja, orbits = c(0, 2, 5, 7, 8, 10, 11, 6, 9, 4, 1))

Arguments

adja	adjacency matrix (numeric) defining the network for which the GCM should be calculated.
orbits	numeric vector with integers from 0 to 14 defining the graphlet orbits to use for GCM calculation. Minimum length is 2. Defaults to c(0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11), thus excluding redundant orbits such as the orbit o3.

Details

By default, only the 11 non-redundant orbits are used. These are grouped according to their role: orbit 0 represents the degree, orbits 2, 5, 7 represent nodes within a chain, orbits 8, 10, 11 represent nodes in a cycle, and orbits 6, 9, 4, 1 represent a terminal node.

Value

A list with the following elements:

gcm	Graphlet Correlation Matrix
ocount	Orbit counts

References

\insertRef

hocevar2016computationNetCoMi

\insertRefyaveroglu2014revealingNetCoMi

See Also

calcGCD, testGCM

Examples

# Load data set from American Gut Project (from SpiecEasi package)
data("amgut1.filt")

# Network construction
net <- netConstruct(amgut1.filt, 
                    filtTax = "highestFreq",
                    filtTaxPar = list(highestFreq = 50),
                    measure = "pearson",
                    normMethod = "clr",
                    zeroMethod = "pseudoZO",
                    sparsMethod = "thresh",
                    thresh = 0.5)

# Get adjacency matrices
adja <- net$adjaMat1

# Network visualization
props <- netAnalyze(net)
plot(props, rmSingles = TRUE, cexLabels = 1.7)

# Calculate Graphlet Correlation Matrix (GCM)
gcm <- calcGCM(adja)

gcm

# Plot heatmap of the GCM
plotHeat(gcm$gcm)
  

stefpeschel/NetCoMi documentation built on Feb. 4, 2024, 8:20 a.m.