dcRWRpipeline {dcGOR}  R Documentation 
dcRWRpipeline
is supposed to estimate sample relationships (ie.
contact strength between samples) from an input domainsample matrix
and an input graph (such as a domaindomain semantic network). The
pipeline includes: 1) random walk restart (RWR) of the input graph
using the input matrix as seeds; 2) calculation of contact strength
(inner products of RWRsmoothed columns of input matrix); 3) estimation
of the contact signficance by a randomalisation procedure. It supports
two methods how to use RWR: 'direct' for directly applying RWR in the
given seeds; 'indirectly' for first precomputing affinity matrix of
the input graph, and then deriving the affinity score. Parallel
computing is also supported for Linux or Mac operating systems.
dcRWRpipeline(data, g, method = c("indirect", "direct"), normalise = c("laplacian", "row", "column", "none"), restart = 0.75, normalise.affinity.matrix = c("none", "quantile"), permutation = c("random", "degree"), num.permutation = 100, p.adjust.method = c("BH", "BY", "bonferroni", "holm", "hochberg", "hommel"), adjp.cutoff = 0.05, parallel = TRUE, multicores = NULL, verbose = T)
data 
an input domainsample data matrix used for seeds. Each value in input domainsample matrix does not necessarily have to be binary (nonzeros will be used as a weight, but should be nonnegative for easy interpretation). 
g 
an object of class "igraph" or 
method 
the method used to calculate RWR. It can be 'direct' for directly applying RWR, 'indirect' for indirectly applying RWR (first precompute affinity matrix and then derive the affinity score) 
normalise 
the way to normalise the adjacency matrix of the input graph. It can be 'laplacian' for laplacian normalisation, 'row' for rowwise normalisation, 'column' for columnwise normalisation, or 'none' 
restart 
the restart probability used for 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 
permutation 
how to do permutation. It can be 'degree' for degreepreserving permutation, 'random' for permutation in random 
num.permutation 
the number of permutations used to for generating the distribution of contact strength under randomalisation 
p.adjust.method 
the method used to adjust pvalues. It can be one of "BH", "BY", "bonferroni", "holm", "hochberg" and "hommel". The first two methods "BH" (widely used) and "BY" control the false discovery rate (FDR: the expected proportion of false discoveries amongst the rejected hypotheses); the last four methods "bonferroni", "holm", "hochberg" and "hommel" are designed to give strong control of the familywise error rate (FWER). Notes: FDR is a less stringent condition than FWER 
adjp.cutoff 
the cutoff of adjusted pvalue to construct the contact graph 
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
systemspecific (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 "iContact", a list with following components:
ratio
: a symmetric matrix storing ratio (the observed
against the expected) between pairwise samples
zscore
: a symmetric matrix storing zscore between pairwise
samples
pval
: a symmetric matrix storing pvalue between pairwise
samples
adjpval
: a symmetric matrix storing adjusted pvalue
between pairwise samples
icontact
: the constructed contact graph (as an 'igraph'
object) under the cutoff of adjusted value
Amatrix
: a precomputated affinity matrix when using
'inderect' method; NULL otherwise
call
: the call that produced this result
The choice of which method to use RWR depends on the number of seed sets and the number of permutations for statistical test. If the total product of both numbers are huge, it is better to use 'indrect' method (for a single run).
dcRDataLoader
, dcDAGannotate
,
dcDAGdomainSim
, dcConverter
## Not run: # 1) load onto.GOMF (as 'Onto' object) g < dcRDataLoader('onto.GOMF') # 2) load SCOP superfamilies annotated by GOMF (as 'Anno' object) Anno < dcRDataLoader('SCOP.sf2GOMF') # 3) prepare for ontology appended with annotation information dag < dcDAGannotate(g, annotations=Anno, path.mode="shortest_paths", verbose=TRUE) # 4) calculate pairwise semantic similarity between 10 randomly chosen domains alldomains < unique(unlist(nInfo(dag)$annotations)) domains < sample(alldomains,10) dnetwork < dcDAGdomainSim(g=dag, domains=domains, method.domain="BM.average", method.term="Resnik", parallel=FALSE, verbose=TRUE) dnetwork # 5) estimate RWR dating based sample/term relationships # define sets of seeds as data # each seed with equal weight (i.e. all nonzero entries are '1') data < data.frame(aSeeds=c(1,0,1,0,1), bSeeds=c(0,0,1,0,1)) rownames(data) < id(dnetwork)[1:5] # calcualte their two contact graph coutput < dcRWRpipeline(data=data, g=dnetwork, parallel=FALSE) coutput ## End(Not run)