text: user4595: I have a Normal esophageal Fibroblasts (NOFs) cultured in DMEM media; The same NOF also have been cultured with a tumor sample from a patient named 005 on DMEM media; I have also Cancer Associated Fibroblasts (CAFs) cultured on DMEM, on DMEM + tumor from a patient named as 005 and DMEM + tumor from a patient named 036 something like this picture;
  
  I want to know if NOF and CAF are different in terms of gene expression but I am not sure about design for DESeq2 or Edger and ANOVA.
  By @Devon's help I have done so
  > mycols
     cell_type co_cultured
  G2       NOF          NO
  G3       NOF         YES
  G4       CAF          NO
  G5       CAF         YES
  G6       CAF         YES
  
  
  dds=DESeqDataSetFromMatrix(countData = NOFCAF,colData = mycols, design        =~ cell_type*co_cultured) 
  
  Now, I am wondering what would be a suitable contrast for results function in DESeq2
  > resultsNames(dds)
  [1] "Intercept"                   "cell_type_NOF_vs_CAF"        "co_cultured_YES_vs_NO"      
  [4] "cell_typeNOF.co_culturedYES"
  
  
  
  > res=results(dds)
  > res
  log2 fold change (MLE): cell typeNOF.co culturedYES 
  Wald test p-value: cell typeNOF.co culturedYES 
  DataFrame with 2545 rows and 6 columns
  
  Thanks a lot for correcting me
  
  user77: You're really running out of degrees of freedom and the only actual replicate is a single sample from another patient, so please take the results with a huge grain of salt.
  You can use a design of ~cellType * coculture where cellType has levels NOF and CAF and coculture has levels yes and no (ideally you'd block by patient, but that's not an option for you).
  
original_text: 4595: <p>I have a Normal esophageal Fibroblasts (NOFs) cultured in DMEM media; The same NOF also have been cultured with a tumor sample from a patient named 005 on DMEM media; I have also Cancer Associated Fibroblasts (CAFs) cultured on DMEM, on DMEM + tumor from a patient named as 005 and DMEM + tumor from a patient named 036 something like this picture;</p>
  
  <p><a href="https://i.stack.imgur.com/TwjbK.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TwjbK.jpg" alt="enter image description here"></a></p>
  
  <p>I want to know if NOF and CAF are different in terms of gene expression but I am not sure about design for DESeq2 or Edger and ANOVA.</p>
  
  <p>By @Devon's help I have done so</p>
  
  <pre><code>&gt; mycols
     cell_type co_cultured
  G2       NOF          NO
  G3       NOF         YES
  G4       CAF          NO
  G5       CAF         YES
  G6       CAF         YES
  
  
  dds=DESeqDataSetFromMatrix(countData = NOFCAF,colData = mycols, design        =~ cell_type*co_cultured) 
  </code></pre>
  
  <p>Now, I am wondering what would be a suitable contrast for results function in DESeq2</p>
  
  <pre><code>&gt; resultsNames(dds)
  [1] "Intercept"                   "cell_type_NOF_vs_CAF"        "co_cultured_YES_vs_NO"      
  [4] "cell_typeNOF.co_culturedYES"
  
  
  
  &gt; res=results(dds)
  &gt; res
  log2 fold change (MLE): cell typeNOF.co culturedYES 
  Wald test p-value: cell typeNOF.co culturedYES 
  DataFrame with 2545 rows and 6 columns
  </code></pre>
  
  <p>Thanks a lot for correcting me</p>
   77: <p>You're really running out of degrees of freedom and the only actual replicate is a single sample from another patient, so please take the results with a huge grain of salt.</p>
  
  <p>You can use a design of <code>~cellType * coculture</code> where <code>cellType</code> has levels <code>NOF</code> and <code>CAF</code> and <code>coculture</code> has levels <code>yes</code> and <code>no</code> (ideally you'd block by patient, but that's not an option for you).</p>
  