Usage
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)
Arguments
data
an input domain-sample data matrix used for seeds. Each
value in input domain-sample matrix does not necessarily have to be
binary (non-zeros will be used as a weight, but should be non-negative
for easy interpretation).
method
the method used to calculate RWR. It can be 'direct' for
directly applying RWR, 'indirect' for indirectly applying RWR (first
pre-compute 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
row-wise normalisation, 'column' for column-wise 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
degree-preserving 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 p-values. 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 family-wise 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
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:
source("http://bioconductor.org/biocLite.R");
biocLite(c("foreach","doMC")). If not yet installed, this option will
be disabled
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