Hi Kevin, first of all, thanks for your suggestion. I did follow your suggestion to not filter the gene and remove the control probe from the dataset prior to running WGCNA (as i understand the control probe name start with AFFY). the problem is that during runing WGCNA, i noticed the distribution of data to detect outliers using

gsg <- goodSamplesGenes(norm.counts)
summary(gsg)
gsg$allOK
{  if(sum(!gsg$goodGenes)>0)    
  printFlush(paste("Removing genes:", paste(names(norm.counts)[!gsg$goodGenes], collapse = ", ")));  
  if(sum(!gsg$goodSamples)>0)    
    printFlush(paste("Removing samples:", paste(rownames(norm.counts)[!gsg$goodSamples], collapse = ", ")));  
  norm.counts = norm.counts[gsg$goodSamples, gsg$goodGenes] 
}
sampleTree = hclust(dist(norm.counts), method = "average");
byHist = hist(sampleTree$height,main = "Histogram of Height",xlab = "Height")

The distribution is not a bell shape and reassure to detect outliers

plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="",
 cex.lab = 1.5,cex.axis = 1.5, cex.main = 2)

After that I exclude some samples above the height cut off and then run the pickSoftThreshold and get this result Do you have any suggestion to fix this problem?

P.S. I used ReadAffy() and gcrma(Data) to extract and normalized expresion data and next I mapped the probe using hgu133plus2.db and exclude N/A probe and the control probe. Finally, I used avereps() function to average duplicated gene ID.

Thanks again for helping me.