data(STATegRa_S1)
data(STATegRa_S2)
require(Biobase)
# Truncate data for brevity
Block1 <- Block1[1:100,]
Block2 <- Block2[1:100,]
## Create ExpressionSets
mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
## Create the bioMap
map.gene.miRNA<-bioMap(name = "Symbol-miRNA",
metadata = list(type_v1="Gene",type_v2="miRNA",
source_database="targetscan.Hs.eg.db",
data_extraction="July2014"),
map=mapdata)
# Create Gene-gene distance computed through miRNA data
bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
reference = "Var1",
mapping = map.gene.miRNA,
surrogateData = miRNA.ds, ### miRNA data
referenceData = mRNA.ds, ### mRNA data
maxitems=2,
selectionRule="sd",
expfac=NULL,
aggregation = "sum",
distance = "spearman",
noMappingDist = 0,
filtering = NULL,
name = "mRNAbymiRNA")
# Create Gene-gene distance through mRNA data
bioDistmRNA<-new("bioDistclass",
name = "mRNAbymRNA",
distance = cor(t(exprs(mRNA.ds)),method="spearman"),
map.name = "id",
map.metadata = list(),
params = list())
###### Generation of the list of Surrogated distances.
bioDistList<-list(bioDistmRNA,bioDistmiRNA)
sample.weights<-matrix(0,4,2)
sample.weights[,1]<-c(0,0.33,0.67,1)
sample.weights[,2]<-c(1,0.67,0.33,0)
###### Generation of the list of bioDistWclass objects.
bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
bioDistList = bioDistList,
weights=sample.weights)
###### Plot of distances.
bioDistWPlot(referenceFeatures = rownames(Block1) ,
listDistW = bioDistWList,
method.cor="spearman")
###### Computing the matrix of features/distances associated.
fm<-bioDistFeature(Feature = rownames(Block1)[1] ,
listDistW = bioDistWList,
threshold.cor=0.7)
bioDistFeaturePlot(data=fm)
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