text: user2247: I have expression data for 100 samples and clinical data for 50 among them. I want to carryout k-means clustering using expression data and then do a survival analysis (Kaplan-Meier analysis) using the labels obtained from k-means.
  Can I used the expression data of all 100 samples to carryout clustering or should I use only 50 samples for clustering?
  
  user3541: In case there is no systematic difference between the samples with or without the clinical data (i.e. one set is microarray and the other is RNA-seq), I would use all of the samples. And if the cluster labels obtained with "clustering 50 samples" and those obtained with "clustering 100 samples" does not differ too much, then doing either of two would not have much effect anyway. If you would like to compare cluster labels in that way and assuming you would want to test a number of ks, it would be better to compare each combination of ks and number of samples.
  
original_text: 2247: <p>I have expression data for 100 samples and clinical data for 50 among them. I want to carryout k-means clustering using expression data and then do a survival analysis (Kaplan-Meier analysis) using the labels obtained from k-means.</p>
  <p>Can I used the expression data of all 100 samples to carryout clustering or should I use only 50 samples for clustering?</p>
   3541: <p>In case there is no systematic difference between the samples with or without the clinical data (i.e. one set is microarray and the other is RNA-seq), I would use all of the samples. And if the cluster labels obtained with &quot;clustering 50 samples&quot; and those obtained with &quot;clustering 100 samples&quot; does not differ too much, then doing either of two would not have much effect anyway. If you would like to compare cluster labels in that way and assuming you would want to test a number of <code>k</code>s, it would be better to compare each combination of <code>k</code>s and number of samples.</p>
  