samplePaths {NetPathMiner} | R Documentation |
Randomly traverses paths of increasing lengths within a set network to create an empirical pathway distribution for more accurate determination of path significance.
samplePaths(graph, max.path.length, num.samples = 1000, num.warmup = 10, verbose = TRUE)
graph |
A weighted igraph object. Weights must be in |
max.path.length |
The maxmimum path length. |
num.samples |
The numner of paths to sample |
num.warmup |
The number of warm up paths to sample. |
verbose |
Whether to display the progress of the function. |
Can take a bit of time.
A matrix where each row is a path length and each column is the number of paths sampled.
Timothy Hancock
Ahmed Mohamed
Other Path ranking methods: extractPathNetwork
,
getPathsAsEIDs
, pathRanker
## Prepare a weighted reaction network. ## Conver a metabolic network to a reaction network. data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism. rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE) ## Assign edge weights based on Affymetrix attributes and microarray dataset. # Calculate Pearson's correlation. data(ex_microarray) # Part of ALL dataset. rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph, weight.method = "cor", use.attr="miriam.uniprot", y=factor(colnames(ex_microarray)), bootstrap = FALSE) ## Get significantly correlated paths using "p-valvue" method. ## First, establish path score distribution by calling "samplePaths" pathsample <- samplePaths(rgraph, max.path.length=10, num.samples=100, num.warmup=10) ## Get all significant paths with p<0.1 significant.p <- pathRanker(rgraph, method = "pvalue", sampledpaths = pathsample ,alpha=0.1)