## ---- eval=FALSE-------------------------------------------------------------- # library(CoRegNet) # data(CIT_BLCA_EXP,HumanTF,CIT_BLCA_Subgroup) # dim(CIT_BLCA_EXP) # #showing 6 first TF in the gene expression dataset # head(intersect(rownames(CIT_BLCA_EXP),HumanTF)) ## ---- eval=FALSE-------------------------------------------------------------- # grn = hLICORN(CIT_BLCA_EXP, TFlist=HumanTF) ## ---- eval=FALSE-------------------------------------------------------------- # influence = regulatorInfluence(grn,CIT_BLCA_EXP) ## ---- eval=FALSE-------------------------------------------------------------- # coregs= coregulators(grn) ## ---- eval=FALSE-------------------------------------------------------------- # display(grn,CIT_BLCA_EXP,influence,clinicalData=CIT_BLCA_Subgroup) ## ---- eval=FALSE-------------------------------------------------------------- # # An example of how to infer a co-regulation network # grn =hLICORN(CIT_BLCA_EXP, TFlist=HumanTF) # print(grn) ## ---- eval=FALSE-------------------------------------------------------------- # #Default discretization. # #Uses the standard deviation of the whole dataset to set a threshold. # disc1=discretizeExpressionData(CIT_BLCA_EXP) # table(disc1) # boxplot(as.matrix(CIT_BLCA_EXP)~disc1) # # #Discretization with a hard threshold # disc2=discretizeExpressionData(CIT_BLCA_EXP, threshold=1) # table(disc2) # boxplot(as.matrix(CIT_BLCA_EXP)~disc2) # # # more examples here # help(discretizeExpressionData) ## ---- eval=FALSE-------------------------------------------------------------- # # running only on the 200 first gene in the matrix for fast analysis # # Choosing to divide in 4 threads whenever possible # options("mc.cores"=4) # grn =hLICORN(head(CIT_BLCA_EXP,200), TFlist=HumanTF) # print(grn) # options("mc.cores"=2) # grn =hLICORN(head(CIT_BLCA_EXP,200), TFlist=HumanTF) # print(grn) ## ---- eval=FALSE-------------------------------------------------------------- # # ChIP data from the CHEA database # data(CHEA_sub) # # #ChIP data from the ENCODE project # data(ENCODE_sub) # # # Protein protein interactions between TF from the HIPPIE database # data(HIPPIE_sub) # # # Protein protein interactions between TF from the STRING database # data(STRING_sub) # # enrichedGRN = addEvidences(grn,CHEA_sub,ENCODE_sub) # enrichedGRN = addCooperativeEvidences(enrichedGRN,HIPPIE_sub,STRING_sub) ## ---- eval=FALSE-------------------------------------------------------------- # print(enrichedGRN) ## ---- eval=FALSE-------------------------------------------------------------- # # Default unsupervised refinement method # refinedGRN = refine(enrichedGRN) # print(refinedGRN) # # Example of supervised refinement with the CHEA chip data # refinedGRN = refine(enrichedGRN, integration="supervised", # referenceEvidence="CHEA_sub") # print(refinedGRN) ## ---- eval=FALSE-------------------------------------------------------------- # CITinf =regulatorInfluence(grn,CIT_BLCA_EXP) # ## ---- eval=FALSE-------------------------------------------------------------- # # Coregulators of a hLICORN inferred network # head(coregulators(grn)) ## ---- eval=FALSE-------------------------------------------------------------- # data(CIT_BLCA_CNV) # data(CIT_BLCA_Subgroup) ## ---- eval=FALSE-------------------------------------------------------------- # display(grn,expressionData=CIT_BLCA_EXP,TFA=CITinf) ## ---- eval=FALSE-------------------------------------------------------------- # # Visualizing additional regulatory or co-regulatory evidences in the network # display(enrichedGRN,expressionData=CIT_BLCA_EXP,TFA=CITinf) # # # # Visualizing sample classification using a named factor # display(grn,expressionData=CIT_BLCA_EXP,TFA=CITinf,clinicalData=CIT_BLCA_Subgroup) # # # Visualizing copy number alteration of regulators # data(CIT_BLCA_CNV) # display(grn,expressionData=CIT_BLCA_EXP,TFA=CITinf,clinicalData=CIT_BLCA_Subgroup,alterationData=CIT_BLCA_CNV) #