text: user4062: I am currently looking at microarray data from a bioconductor microarray dataset. Specifically, I have data (a snippet) which looks like the following:
     GSM794885   GSM794886   GSM794887   GSM794888   GSM794889 
     -1.67170290 -0.07803822  0.99990560 -1.94692850 -1.78671360 
  
  I am wondering how to interpret the values in this microarray dataset. Is it generally the case that larger values mean a more significant effect?
  
  user73: Processed microarray data is commonly represented as log_2(expression). This data transformation is used because the data more closely fit a normal distribution in log space. With such a transformation, negative values represent very small amounts, rather than the absence of something.
  Larger values mean that more stuff is being produced (presumably transcripts), but its significance can only be assessed when combined with other data (e.g. through differential expression analysis).
  
original_text: 4062: <p>I am currently looking at microarray data from a bioconductor microarray dataset. Specifically, I have data (a snippet) which looks like the following:</p>
  
  <pre><code>   GSM794885   GSM794886   GSM794887   GSM794888   GSM794889 
     -1.67170290 -0.07803822  0.99990560 -1.94692850 -1.78671360 
  </code></pre>
  
  <p>I am wondering how to interpret the values in this microarray dataset. Is it generally the case that larger values mean a more significant effect?</p>
   73: <p>Processed microarray data is commonly represented as log_2(expression). This data transformation is used because the data more closely fit a normal distribution in log space. With such a transformation, negative values represent very small amounts, rather than the absence of something.</p>
  
  <p>Larger values mean that more stuff is being produced (presumably transcripts), but its significance can only be assessed when combined with other data (e.g. through differential expression analysis).</p>
  