text: user285: I have been looking ways to use data from various micro-array platform such as agilient, rosetta/merk. Some of the established method I came across is combat, but I'm not sure if it can be done for cross platform. 
  A similar question here where the answer is kind of can't be or shouldn't be done.
  Any suggestion how to take account of batch effects across multiple platforms.
  
  user3828: Usually with microarrays you want to make a case/control comparison, so I am going to assume that.
  Data from different array platforms is generally difficult to compare:  each platform is measuring potentially a different part of the expression of the gene (different exons or different regions), each platform is likely to need different normalisation approaches, and have different dynamic range.
  If you have case/control data from several different platforms, then the most robust thing to do is to analyse each platform separately, and produce a list of up and down-regulated genes according to each platform.
  Then, it would be reasonably safe to combine the lists of genes in a 'meta-analysis' approach.  I would expect that in a lot of cases, 'up' and 'down' regulation would be correlated across platforms, but I would not necessarily expect very strong correlation in the magnitude of changes between platforms for individual genes.
  
original_text: 285: <p>I have been looking ways to use data from various micro-array platform such as agilient, rosetta/merk. Some of the established method I came across is combat, but I'm not sure if it can be done for cross platform. </p>
  
  <p>A similar question <a href="https://support.bioconductor.org/p/77363/" rel="nofollow noreferrer">here</a> where the answer is kind of can't be or shouldn't be done.</p>
  
  <p>Any suggestion how to take account of batch effects across multiple platforms.</p>
   3828: <p>Usually with microarrays you want to make a case/control comparison, so I am going to assume that.</p>
  
  <p>Data from different array platforms is generally difficult to compare:  each platform is measuring potentially a different part of the expression of the gene (different exons or different regions), each platform is likely to need different normalisation approaches, and have different dynamic range.</p>
  
  <p>If you have case/control data from several different platforms, then the most robust thing to do is to analyse each platform separately, and produce a list of up and down-regulated genes according to each platform.</p>
  
  <p>Then, it would be reasonably safe to combine the lists of genes in a 'meta-analysis' approach.  I would expect that in a lot of cases, 'up' and 'down' regulation would be correlated across platforms, but I would not necessarily expect very strong correlation in the magnitude of changes between platforms for individual genes.</p>
  