tni.permutation {RTN} | R Documentation |
This function takes a TNI object and returns a transcriptional network inferred by mutual information (with multiple hypothesis testing corrections).
tni.permutation(object, pValueCutoff=0.01, pAdjustMethod="BH", globalAdjustment=TRUE, estimator="pearson",nPermutations=1000, pooledNullDistribution=TRUE, parChunks=50, verbose=TRUE)
object |
a preprocessed object of class 'TNI' |
pValueCutoff |
a single numeric value specifying the cutoff for p-values considered significant. |
pAdjustMethod |
a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details). |
globalAdjustment |
a single logical value specifying to run global p.value adjustments (when globalAdjustment=TRUE) or not (when globalAdjustment=FALSE). |
estimator |
a character string indicating which estimator to be used for mutual information computation. One of "pearson" (default), "kendall", or "spearman", can be abbreviated. |
nPermutations |
a single integer value specifying the number of permutations for deriving TF-target p-values in the mutual information analysis. If running in parallel, nPermutations should be greater and multiple of parChunks. |
pooledNullDistribution |
a single logical value specifying to run the permutation analysis with pooled regulons (when pooledNullDistribution=TRUE) or not (when pooledNullDistribution=FALSE). |
parChunks |
an optional single integer value specifying the number of permutation chunks to be used in the parallel analysis (effective only for "pooledNullDistribution = TRUE"). |
verbose |
a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE) |
a mutual information matrix in the slot "results" containing a reference transcriptional network,
see 'tn.ref' option in tni.get
.
Mauro Castro
data(dt4rtn) # select 5 regulatoryElements for a quick demonstration! tfs4test <- dt4rtn$tfs[c("PTTG1","E2F2","FOXM1","E2F3","RUNX2")] ## Not run: # preprocessing rtni <- tni.constructor(expData=dt4rtn$gexp, regulatoryElements=tfs4test, rowAnnotation=dt4rtn$gexpIDs) # linear version! rtni<-tni.permutation(rtni) # parallel version with SNOW package! library(snow) options(cluster=makeCluster(3, "SOCK")) rtni<-tni.permutation(rtni) stopCluster(getOption("cluster")) ## End(Not run)