# Example: first generate simulated data.
set.seed(100)
ME.A = sample(1:100,50); ME.B = sample(1:100,50)
ME.C = sample(1:100,50); ME.D = sample(1:100,50)
ME.E = sample(1:100,50); ME.F = sample(1:100,50)
ME.G = sample(1:100,50); ME.H = sample(1:100,50)
ME1 = data.frame(ME.A, ME.B, ME.C, ME.D, ME.E)
ME2 = data.frame(ME.A, ME.C, ME.D, ME.E, ME.F, ME.G, ME.H)
simDat1 = simulateDatExpr(ME1,1000,c(0.2,0.1,0.08,0.05,0.04,0.3), signed=TRUE)
simDat2 = simulateDatExpr(ME2,1000,c(0.2,0.1,0.08,0.05,0.04,0.03,0.02,0.3),
signed=TRUE)
# Now run the function using assigned genes
results = overlapTableUsingKME(simDat1$datExpr, simDat2$datExpr,
labels2colors(simDat1$allLabels), labels2colors(simDat2$allLabels),
cutoffMethod="assigned")
results$PvaluesHypergeo
# Now run the function using a p-value cutoff, and inputting the original MEs
colnames(ME1) = standardColors(5); colnames(ME2) = standardColors(7)
results = overlapTableUsingKME(simDat1$datExpr, simDat2$datExpr,
labels2colors(simDat1$allLabels),
labels2colors(simDat2$allLabels),
ME1, ME2, cutoffMethod="pvalue", cutoff=0.05)
results$PvaluesHypergeo
# Check which genes are in common between the black modules from set 1 and
# the green module from set 2
results$OverlappingGenes$MM1_green_MM2_black
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