text: user6470: I have two populations of mice, say RED and GREEN; there are 5 pairs treatment/control in each.
  The table is the following:
  gene_name  p-value(treatVSctrl) RED1_ctrl RED1_treat RED2_ctrl RED2_treat ... GRN1_ctrl GRN1_treat etc... 
  gene1      1.3                  4         10         5         10             6.2       9.75
  gene2      2.8                  3         6          6         12             3         6  
  gene3      4.4                  5         12         4         8              7         10
  
  I need to compare two groups to have an idea of the expression of different genes after treatment.
  I also need to find a difference between RED and GREEN group in terms of genes expression
  The thing is I cannot compare mice one by one just measuring the fold change, I need to consider p-value. What is the most correct way to perform this analysis?
  
  user3848: I don't really understand the table, particularly the 'p-value' in the header. More generally I don't really understand the experiment nor the data.
  As a general rule the Wilcoxon would be a reasonable guess of the test using raw data. This is non-parametric version of the T-test without the rigid assumptions of parametrics. Control mice go in one column and subjects in the other. Scipy.stats has the test, its certainly in R (which I don't really use).
  print (scipy.stats.wilcoxon(x = [1,2,3,4,5], y=[1,3,5,6,7], zero_method='wilcox', correction=False, alternative='two-sided', mode='auto', *, axis=0, nan_policy='propagate'))
  
  There you go, filled out with some example data.
  
original_text: 6470: <p>I have two populations of mice, say RED and GREEN; there are 5 pairs treatment/control in each.</p>
  <p>The table is the following:</p>
  <pre><code>gene_name  p-value(treatVSctrl) RED1_ctrl RED1_treat RED2_ctrl RED2_treat ... GRN1_ctrl GRN1_treat etc... 
  gene1      1.3                  4         10         5         10             6.2       9.75
  gene2      2.8                  3         6          6         12             3         6  
  gene3      4.4                  5         12         4         8              7         10
  </code></pre>
  <p>I need to compare two groups to have an idea of the expression of different genes after treatment.
  I also need to find a difference between RED and GREEN group in terms of genes expression</p>
  <p>The thing is I cannot compare mice one by one just measuring the fold change, I need to consider p-value. What is the most correct way to perform this analysis?</p>
   3848: <p>I don't really understand the table, particularly the 'p-value' in the header. More generally I don't really understand the experiment nor the data.</p>
  <p>As a general rule the Wilcoxon would be a reasonable guess of the test using raw data. This is non-parametric version of the T-test without the rigid assumptions of parametrics. Control mice go in one column and subjects in the other. Scipy.stats has the test, its certainly in R (which I don't really use).</p>
  <pre><code>print (scipy.stats.wilcoxon(x = [1,2,3,4,5], y=[1,3,5,6,7], zero_method='wilcox', correction=False, alternative='two-sided', mode='auto', *, axis=0, nan_policy='propagate'))
  </code></pre>
  <p>There you go, filled out with some example data.</p>
  