Coutput is an S4 class to store output by
dcRWRpipeline.
Class Coutput
ratioA symmetrix matrix, containing ratio
zscoreA symmetrix matrix, containing z-scores
pvalueA symmetrix matrix, containing p-values
adjpA symmetrix matrix, containing adjusted p-values
cnetworkAn object of S4 class Cnetwork,
storing contact network.
An object of this class can be created via: new("Coutput", ratio,
zscore, pvalue, adjp, cnetwork)
Class-specific methods:
ratio(): retrieve the slot 'ratio' in the object
zscore(): retrieve the slot 'zscore' in the object
pvalue(): retrieve the slot 'pvalue' in the object
adjp(): retrieve the slot 'adjp' in the object
cnetwork(): retrieve the slot 'cnetwork' in the object
write(): write the object into a local file
Standard generic methods:
str(): compact display of the content in the object
show(): abbreviated display of the object
Ways to access information on this class:
showClass("Coutput"): show the class definition
showMethods(classes="Coutput"): show the method
definition upon this class
getSlots("Coutput"): get the name and class of each slot
in this class
slotNames("Coutput"): get the name of each slot in this
class
selectMethod(f, signature="Coutput"): retrieve the
definition code for the method 'f' defined in this class
# 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 12:25:03 First, extract all annotatable domains (2015-07-23 12:25:03)... there are 10 input domains amongst 1083 annotatable domains Second, pre-compute semantic similarity between 209 terms (forced to be the most specific for each domain) using Resnik method (2015-07-23 12:25:11)... Last, calculate pair-wise semantic similarity between 10 domains using BM.average method (2015-07-23 12:25:14)... 1 out of 10 (2015-07-23 12:25:14) 2 out of 10 (2015-07-23 12:25:14) 3 out of 10 (2015-07-23 12:25:14) 4 out of 10 (2015-07-23 12:25:14) 5 out of 10 (2015-07-23 12:25:14) 6 out of 10 (2015-07-23 12:25:14) 7 out of 10 (2015-07-23 12:25:14) 8 out of 10 (2015-07-23 12:25:14) 9 out of 10 (2015-07-23 12:25:14) Finish at 2015-07-23 12:25:14 Runtime in total is: 11 secsdnetworkAn object of S4 class 'Dnetwork' @adjMatrix: a weighted symmetric matrix of 10 domains X 10 domains @nodeInfo (InfoDataFrame) nodeNames: 90250 47862 82549 ... 82861 49899 (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 12:25:14 First, RWR on input graph (10 nodes and 34 edges) using input matrix (5 rows and 2 columns) as seeds (2015-07-23 12:25:14)... using 'indirect' method to do RWR (2015-07-23 12:25:14)... Second, calculate contact strength (2015-07-23 12:25:14)... Third, generate the distribution of contact strength based on 100 permutations on nodes respecting 'random' (2015-07-23 12:25:14)... 1 out of 100 (2015-07-23 12:25:14) 10 out of 100 (2015-07-23 12:25:14) 20 out of 100 (2015-07-23 12:25:14) 30 out of 100 (2015-07-23 12:25:14) 40 out of 100 (2015-07-23 12:25:14) 50 out of 100 (2015-07-23 12:25:14) 60 out of 100 (2015-07-23 12:25:14) 70 out of 100 (2015-07-23 12:25:14) 80 out of 100 (2015-07-23 12:25:14) 90 out of 100 (2015-07-23 12:25:14) 100 out of 100 (2015-07-23 12:25:14) Last, estimate the significance of contact strength: zscore, pvalue, and BH adjusted-pvalue (2015-07-23 12:25:14)... Also, construct the contact graph under the cutoff 5.0e-02 of adjusted-pvalue (2015-07-23 12:25:14)... Your input object 'icontact' of class 'igraph' has been converted into an object of class 'Cnetwork'. Finish at 2015-07-23 12:25:14 Runtime in total is: 0 secscoutputAn 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# 6) write into the file 'Coutput.txt' in your local directory write(coutput, file='Coutput.txt', saveBy="adjp")A file ('Coutput.txt') has been written into your local directory ('/Users/hfang/Sites/SUPERFAMILY/dcGO/dcGOR')# 7) retrieve several slots directly ratio(coutput)aSeeds bSeeds aSeeds 0.9941237 0.990302 bSeeds 0.9903020 0.993314zscore(coutput)aSeeds bSeeds aSeeds -0.2683189 -0.2513369 bSeeds -0.2513369 -0.1625679pvalue(coutput)aSeeds bSeeds aSeeds 0.56 0.66 bSeeds 0.66 0.52adjp(coutput)aSeeds bSeeds aSeeds 0.56 0.66 bSeeds 0.66 0.52cnetwork(coutput)An object of S4 class 'Cnetwork' @adjMatrix: a weighted symmetric matrix of 0 samples/terms X 0 samples/terms nodeInfo (NULL)
Coutput-method