text: user285: I have tried different combination of tree cut heights but im getting the exprSize as one sample as full the other one as empty .
  # Choose the "base" cut height for the female data set
  baseHeight = 135
  # Adjust the cut height for the male data set for the number of samples
  cutHeights = c(135, 145*exprSize$nSamples[2]/exprSize$nSamples[1]);
  # Re-plot the dendrograms including the cut lines
  pdf(file = "SampleClustering.pdf", width = 12, height = 12);
  par(mfrow=c(2,1))
  par(mar = c(0, 4, 2, 0))
  for (set in 1:nSets)
  {
    plot(sampleTrees[[set]], main = paste("Sample clustering on all genes in", setLabels[set]),
         xlab="", sub="", cex = 0.7);
    abline(h=cutHeights[set], col = "red");
  }
  dev.off();
  
  My figure 
  When i see the cutHeights and exprSize i do see its higher than 120 but still i get only one set of sample which is 55 whereas the my other set which is 
  exprSize
  $nSets
  [1] 2
  
  $nGenes
  [1] 8213
  
  $nSamples
  [1] 55 47
  
  $structureOK
  [1] TRUE
  
  This is after tree cut 
  exprSize
  $nSets
  [1] 2
  
  $nGenes
  [1] 8213
  
  $nSamples
  [1] 55  0
  
  $structureOK
  [1] TRUE
  
  > cutHeights
  [1] 135.0000 123.9091
  
  Im not able to figure out what is wrong 
  
  user851: Your data don't have obvious outliers so you don't have to worry about removing them. The problem is that both sets show prominent sample clusters. I would try to figure out what drives those clusters (is it technical or biological), and whether the variation should be removed. See the comments in WGCNA FAQ under point 5 (My data are heterogeneous. Can I still use WGCNA?). This looks like data from GEO, so look into the sample annotation for these data sets and see if any of the recorded sample characteristics align with the clusters you see in the sample trees.
  
original_text: 285: <p>I have tried different combination of tree cut heights but im getting the exprSize as one sample as full the other one as empty .</p>
  
  <pre><code># Choose the "base" cut height for the female data set
  baseHeight = 135
  # Adjust the cut height for the male data set for the number of samples
  cutHeights = c(135, 145*exprSize<span class="math-container">$nSamples[2]/exprSize$</span>nSamples[1]);
  # Re-plot the dendrograms including the cut lines
  pdf(file = "SampleClustering.pdf", width = 12, height = 12);
  par(mfrow=c(2,1))
  par(mar = c(0, 4, 2, 0))
  for (set in 1:nSets)
  {
    plot(sampleTrees[[set]], main = paste("Sample clustering on all genes in", setLabels[set]),
         xlab="", sub="", cex = 0.7);
    abline(h=cutHeights[set], col = "red");
  }
  dev.off();
  </code></pre>
  
  <p>My figure <a href="https://i.stack.imgur.com/Pi7QD.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Pi7QD.jpg" alt="tree cut"></a></p>
  
  <p>When i see the cutHeights and exprSize i do see its higher than 120 but still i get only one set of sample which is 55 whereas the my other set which is </p>
  
  <pre><code>exprSize
  $nSets
  [1] 2
  
  $nGenes
  [1] 8213
  
  $nSamples
  [1] 55 47
  
  $structureOK
  [1] TRUE
  </code></pre>
  
  <p>This is after tree cut </p>
  
  <pre><code>exprSize
  $nSets
  [1] 2
  
  $nGenes
  [1] 8213
  
  $nSamples
  [1] 55  0
  
  $structureOK
  [1] TRUE
  
  &gt; cutHeights
  [1] 135.0000 123.9091
  </code></pre>
  
  <p>Im not able to figure out what is wrong </p>
   851: <p>Your data don't have obvious outliers so you don't have to worry about removing them. The problem is that both sets show prominent sample clusters. I would try to figure out what drives those clusters (is it technical or biological), and whether the variation should be removed. See the comments in <a href="https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html" rel="nofollow noreferrer">WGCNA FAQ</a> under point 5 (My data are heterogeneous. Can I still use WGCNA?). This looks like data from GEO, so look into the sample annotation for these data sets and see if any of the recorded sample characteristics align with the clusters you see in the sample trees.</p>
  