text: user2296: I am a PhD student working on developing ideas for my dissertation papers. One of my planned papers will be working with some (human) gene expression data.
  I have had a class on working with gene expression data, but no practical experience. From what I remember from my project in that class, you do a bunch of pre-checks and QC, then you remove batch effects, then you summarize expression across probes and normalize. I thank that part of my class project took me about three days.
  But when I talk to other people about it, they say that cleaning/QCing/normalizing gene expression data is really hard, that it will take forever to get it working, and that if I do this dissertation paper I am going to end up taking extra years to graduate.
  Am I missing something here? Why do the people I talk to seem to think this is so hard and time consuming -- is it really that bad? Should I avoid having a dissertation paper that uses gene expression data if I want to graduate on time?
  (The data will be from Affymetrix microarrays. There are enough samples to use two 96-well plates, so batch effects will be more of a concern than they were in my class project.)
  
  user939: I am not sure how much you know about bioinformatics already, can you use R? For a bioinformatician looking at QC for microarrays should not be a big deal, at least for me it would take maybe a day (or two) to get this done. However, if you never used R and want to start from scratch, it depends on how quickly you learn how to deal with arrays and QC. There are many tutorials and review papers about normalization and QC of arrays. Try there and see if you can easily understand what these papers are telling you.
  Read the limma user's guide, and try to follow the tutorials therein. If you understand that quickly you can try a real experiment of your own. If you don't understand what's described in there, you might want to look for another project for your thesis.
  
original_text: 2296: <p>I am a PhD student working on developing ideas for my dissertation papers. One of my planned papers will be working with some (human) gene expression data.</p>
  
  <p>I have had a class on working with gene expression data, but no practical experience. From what I remember from my project in that class, you do a bunch of pre-checks and QC, then you remove batch effects, then you summarize expression across probes and normalize. I thank that part of my class project took me about three days.</p>
  
  <p>But when I talk to other people about it, they say that cleaning/QCing/normalizing gene expression data is really hard, that it will take forever to get it working, and that if I do this dissertation paper I am going to end up taking extra years to graduate.</p>
  
  <p>Am I missing something here? Why do the people I talk to seem to think this is so hard and time consuming -- is it really that bad? Should I avoid having a dissertation paper that uses gene expression data if I want to graduate on time?</p>
  
  <p>(The data will be from Affymetrix microarrays. There are enough samples to use two 96-well plates, so batch effects will be more of a concern than they were in my class project.)</p>
   939: <p>I am not sure how much you know about bioinformatics already, can you use R? For a bioinformatician looking at QC for microarrays should not be a big deal, at least for me it would take maybe a day (or two) to get this done. However, if you never used R and want to start from scratch, it depends on how quickly you learn how to deal with arrays and QC. There are many tutorials and review papers about normalization and QC of arrays. Try there and see if you can easily understand what these papers are telling you.</p>
  
  <p>Read the limma user's guide, and try to follow the tutorials therein. If you understand that quickly you can try a real experiment of your own. If you don't understand what's described in there, you might want to look for another project for your thesis.</p>
  