###########################################
########### EXAMPLE OF THE OMICSCLUSTERING
###########################################
require(STATegRa)
#############################################
## PART 1: CREATING a bioMap CLASS
#############################################
####### This part creates or reads the map between features.
####### In the present example the map is downloaded from a resource.
####### then the class is created.
#load("../data/STATegRa_S2.rda")
data(STATegRa_S2)
MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
metadata = list(type_v1="Gene",type_v2="miRNA",
source_database="targetscan.Hs.eg.db",
data_extraction="July2014"),
map=mapdata)
#############################################
## PART 2: CREATING a bioDist CLASS
#############################################
##### In the second part given a set of main features and surrogate feautres,
##### the profile of the main features is computed through the surrogate features.
# Load Data
data(STATegRa_S1)
#load("../data/STATegRa.S1.Rdata")
## Create ExpressionSets
# source("../R/STATegRa_omicsPCA_classes_and_methods.R")
# Block1 - Expression data
mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
# Block2 - miRNA expression data
miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
# Create Gene-gene distance computed through miRNA data
bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
reference = "Var1",
mapping = MAP.SYMBOL,
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")
require(Biobase)
# Create Gene-gene distance through mRNA data
bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
distance = cor(t(exprs(mRNA.ds)),method="spearman"),
map.name = "id",
map.metadata = list(),
params = list())
#############################################
## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
#############################################
bioDistList<-list(bioDistmRNA,bioDistmiRNA)
weights<-matrix(0,4,2)
weights[,1]<-c(0,0.33,0.67,1)
weights[,2]<-c(1,0.67,0.33,0)#
bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
bioDistList = bioDistList,
weights=weights)
length(bioDistWList)
#############################################
## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
#############################################
bioDistWPlot(referenceFeatures = rownames(Block1) ,
listDistW = bioDistWList,
method.cor="spearman")
#############################################
## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
#############################################
## IDH1
IDH1.F<-bioDistFeature(Feature = "IDH1" ,
listDistW = bioDistWList,
threshold.cor=0.7)
bioDistFeaturePlot(data=IDH1.F)
## PDGFRA
#PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
# listDistW = bioDistWList,
# threshold.cor=0.7)
#bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
## EGFR
#EGFR.F<-bioDistFeature(Feature = "EGFR" ,
# listDistW = bioDistWList,
# threshold.cor=0.7)
#bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
## MGMT
#MGMT.F<-bioDistFeature(Feature = "MGMT" ,
# listDistW = bioDistWList,
# threshold.cor=0.5)
#bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
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