| xPrioritiser | R Documentation |
xPrioritiser is supposed to prioritise nodes given an input
graph and a list of seed nodes. It implements Random Walk with Restart
(RWR) and calculates the affinity score of all nodes in the graph to
the seeds. The priority score is the affinity score. Parallel computing
is also supported for Linux or Mac operating systems. It returns an
object of class "pNode".
xPrioritiser(seeds, g, normalise = c("laplacian", "row", "column",
"none"),
restart = 0.75, normalise.affinity.matrix = c("none", "quantile"),
parallel = TRUE, multicores = NULL, verbose = T)
seeds |
a named input vector containing a list of seed nodes. For this named vector, the element names are seed/node names (e.g. gene symbols), the element (non-zero) values used to weight the relative importance of seeds. Alternatively, it can be a matrix or data frame with two columns: 1st column for seed/node names, 2nd column for the weight values |
g |
an object of class "igraph" to represent network. It can be a weighted graph with the node attribute 'weight' |
normalise |
the way to normalise the adjacency matrix of the input graph. It can be 'laplacian' for laplacian normalisation, 'row' for row-wise normalisation, 'column' for column-wise normalisation, or 'none' |
restart |
the restart probability used for Random Walk with Restart (RWR). The restart probability takes the value from 0 to 1, controlling the range from the starting nodes/seeds that the walker will explore. The higher the value, the more likely the walker is to visit the nodes centered on the starting nodes. At the extreme when the restart probability is zero, the walker moves freely to the neighbors at each step without restarting from seeds, i.e., following a random walk (RW) |
normalise.affinity.matrix |
the way to normalise the output affinity matrix. It can be 'none' for no normalisation, 'quantile' for quantile normalisation to ensure that columns (if multiple) of the output affinity matrix have the same quantiles |
parallel |
logical to indicate whether parallel computation with
multicores is used. By default, it sets to true, but not necessarily
does so. Partly because parallel backends available will be
system-specific (now only Linux or Mac OS). Also, it will depend on
whether these two packages "foreach" and "doMC" have been installed. It
can be installed via:
|
multicores |
an integer to specify how many cores will be registered as the multicore parallel backend to the 'foreach' package. If NULL, it will use a half of cores available in a user's computer. This option only works when parallel computation is enabled |
verbose |
logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display |
an object of class "pNode", a list with following components:
priority: a matrix of nNode X 4 containing node priority
information, where nNode is the number of nodes in the input graph, and
the 4 columns are "name" (node names), "seed" (1 for seeds, 0 for
non-seeds), "weight" (weight values), "priority" (the priority scores
that are rescaled to the range [0,1]), "rank" (ranks of the priority
scores)
g: an input "igraph" object
call: the call that produced this result
The input graph will treat as an unweighted graph if there is no 'weight' edge attribute associated with
xRDataLoader, xRWR,
xPrioritiserSNPs, xPrioritiserGenes,
xPrioritiserPathways
## Not run: # Load the library library(XGR) library(igraph) library(dnet) # a) provide the input nodes/genes with the significance info ## load human genes org.Hs.eg <- xRDataLoader(RData='org.Hs.eg') sig <- rbeta(500, shape1=0.5, shape2=1) data <- data.frame(symbols=org.Hs.eg$gene_info$Symbol[1:500], sig) # b) provide the network g <- xRDataLoader(RData.customised='org.Hs.string') # c) perform priority analysis pNode <- xPrioritiser(seeds=data, g=g, restart=0.75) ## End(Not run)