text: user285: So I have expression data set with 4 healthy sample and 4 disease, to determine the number of K I used mclust which i ran on the list of differentially expressed genes so I got around 7 cluster from Mclust which im using as an input for my K-mean .
  Now my data set is like all the differentially expressed genes it contains both UP and down regulated genes so basically it may be UP in my healthy and Down in patient and vice-versa ,4 likely possibilities.
  So now I have this doubt that from that I'm getting 7 cluster.Is that result is correct ? 
  
  user77: 7 clusters may be correct in that it's the optimal number according to the Bayesian information criterion (or whatever value you had mclust optimize), but that doesn't make it necessarily the most sensible value. Have a look at your data and visually look at what might make sense. It's likely that the clusters are subdividing up and down regulated genes into various subgroups, which may or may not be useful, depending on your goals.
  
  user174: There is generally no such thing as correct or incorrect when it comes to clustering. When you specify $K$, the program did it's statistical algorithms. Unless the the program had a bug, your results were statistically valid.
  You will need to check the clustered labels visually. Only your biological knowledge can tell you the optimal $K$.
  
original_text: 285: <p>So I have expression data set with 4 healthy sample and 4 disease, to determine the number of K I used mclust which i ran on the list of differentially expressed genes so I got around 7 cluster from Mclust which im using as an input for my K-mean .</p>
  
  <p>Now my data set is like all the differentially expressed genes it contains both UP and down regulated genes so basically it may be UP in my healthy and Down in patient and vice-versa ,4 likely possibilities.</p>
  
  <p>So now I have this doubt that from that I'm getting 7 cluster.Is that result is correct ? </p>
   77: <p>7 clusters may be correct in that it's the optimal number according to the Bayesian information criterion (or whatever value you had mclust optimize), but that doesn't make it necessarily the most sensible value. Have a look at your data and visually look at what might make sense. It's likely that the clusters are subdividing up and down regulated genes into various subgroups, which may or may not be useful, depending on your goals.</p>
   174: <p>There is generally no such thing as correct or incorrect when it comes to clustering. When you specify $K$, the program did it's statistical algorithms. Unless the the program had a bug, your results were statistically valid.</p>
  
  <p>You will need to check the clustered labels visually. Only your biological knowledge can tell you the optimal $K$.</p>
  