Function to setup a pipeine to estimate RWR-based contact strength between samples from an input domain-sample data matrix and an input graph


dcRWRpipeline is supposed to estimate sample relationships (ie. contact strength between samples) from an input domain-sample matrix and an input graph (such as a domain-domain 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 RWR-smoothed 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 pre-computing 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)


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).
an object of class "igraph" or Dnetwork
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)
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'
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)
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
how to do permutation. It can be 'degree' for degree-preserving permutation, 'random' for permutation in random
the number of permutations used to for generating the distribution of contact strength under randomalisation
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
the cutoff of adjusted pvalue to construct the contact graph
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(""); biocLite(c("foreach","doMC")). If not yet installed, this option will be disabled
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
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 pre-computated 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).


# 1) load onto.GOMF (as 'Onto' object) g <- dcRDataLoader('onto.GOMF')
'onto.GOMF' (from package 'dcGOR' version 1.0.5) has been loaded into the working environment
# 2) load SCOP superfamilies annotated by GOMF (as 'Anno' object) Anno <- dcRDataLoader('SCOP.sf2GOMF')
'SCOP.sf2GOMF' (from package 'dcGOR' version 1.0.5) has been loaded into the working environment
# 3) prepare for ontology appended with annotation information dag <- dcDAGannotate(g, annotations=Anno, path.mode="shortest_paths", verbose=TRUE)
At level 15, there are 2 nodes, and 3 incoming neighbors. At level 14, there are 6 nodes, and 7 incoming neighbors. At level 13, there are 8 nodes, and 8 incoming neighbors. At level 12, there are 19 nodes, and 22 incoming neighbors. At level 11, there are 24 nodes, and 28 incoming neighbors. At level 10, there are 53 nodes, and 44 incoming neighbors. At level 9, there are 101 nodes, and 81 incoming neighbors. At level 8, there are 198 nodes, and 147 incoming neighbors. At level 7, there are 367 nodes, and 192 incoming neighbors. At level 6, there are 644 nodes, and 221 incoming neighbors. At level 5, there are 489 nodes, and 167 incoming neighbors. At level 4, there are 246 nodes, and 56 incoming neighbors. At level 3, there are 106 nodes, and 13 incoming neighbors. At level 2, there are 20 nodes, and 1 incoming neighbors. At level 1, there are 1 nodes, and 0 incoming neighbors.
# 4) calculate pair-wise 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)
Start at 2015-07-23 12:58:42 First, extract all annotatable domains (2015-07-23 12:58:42)... there are 10 input domains amongst 1083 annotatable domains Second, pre-compute semantic similarity between 188 terms (forced to be the most specific for each domain) using Resnik method (2015-07-23 12:58:52)... Last, calculate pair-wise semantic similarity between 10 domains using BM.average method (2015-07-23 12:58:57)... 1 out of 10 (2015-07-23 12:58:57) 2 out of 10 (2015-07-23 12:58:57) 3 out of 10 (2015-07-23 12:58:57) 4 out of 10 (2015-07-23 12:58:57) 5 out of 10 (2015-07-23 12:58:57) 6 out of 10 (2015-07-23 12:58:57) 7 out of 10 (2015-07-23 12:58:57) 8 out of 10 (2015-07-23 12:58:57) 9 out of 10 (2015-07-23 12:58:57) Finish at 2015-07-23 12:58:57 Runtime in total is: 15 secs
An object of S4 class 'Dnetwork' @adjMatrix: a weighted symmetric matrix of 10 domains X 10 domains @nodeInfo (InfoDataFrame) nodeNames: 48056 48230 57889 ... 52335 57603 (10 total) nodeAttr: id
# 5) estimate RWR dating based sample/term relationships # define sets of seeds as data # each seed with equal weight (i.e. all non-zero 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)
Start at 2015-07-23 12:58:57 First, RWR on input graph (10 nodes and 33 edges) using input matrix (5 rows and 2 columns) as seeds (2015-07-23 12:58:57)... using 'indirect' method to do RWR (2015-07-23 12:58:57)... Second, calculate contact strength (2015-07-23 12:58:57)... Third, generate the distribution of contact strength based on 100 permutations on nodes respecting 'random' (2015-07-23 12:58:57)... 1 out of 100 (2015-07-23 12:58:57) 10 out of 100 (2015-07-23 12:58:57) 20 out of 100 (2015-07-23 12:58:57) 30 out of 100 (2015-07-23 12:58:57) 40 out of 100 (2015-07-23 12:58:57) 50 out of 100 (2015-07-23 12:58:57) 60 out of 100 (2015-07-23 12:58:57) 70 out of 100 (2015-07-23 12:58:57) 80 out of 100 (2015-07-23 12:58:57) 90 out of 100 (2015-07-23 12:58:57) 100 out of 100 (2015-07-23 12:58:57) Last, estimate the significance of contact strength: zscore, pvalue, and BH adjusted-pvalue (2015-07-23 12:58:57)... Also, construct the contact graph under the cutoff 5.0e-02 of adjusted-pvalue (2015-07-23 12:58:57)... Your input object 'icontact' of class 'igraph' has been converted into an object of class 'Cnetwork'. Finish at 2015-07-23 12:58:57 Runtime in total is: 0 secs
An object of S4 class 'Coutput', containing following slots: @ratio: a matrix of 2 X 2, containing ratio @zscore: a matrix of 2 X 2, containing z-scores @pvalue: a matrix of 2 X 2, containing p-values @adjp: a matrix of 2 X 2, containing adjusted p-values @cnetwork: an object of S4 class 'Cnetwork', containing 0 interacting nodes