text: user4595: I have 2 cell types (NOF and CAF) cultures individually and also have been co-cultured with tumour like this picture.
  https://ibb.co/x8Ty1Wz
  If this is the log cpm of these 4 samples 
  > head(onco[,1:4])
               A10       A11        A12        A2
  A2M    11.755100 12.145143 12.9787977 12.407221
  ABCB11  2.955457  2.278363  0.1229266  1.493507
  ABCC2   6.711643  6.974025  0.1229266  7.628936
  ABCC6   6.443439  6.094670  6.1389496  7.483394
  ABCF1   9.995098 10.045613  9.6627424 10.314842
  ABCG2   7.125483  7.448127  6.8669702  8.492895
  > 
  
  I have used log of count per million (cpm) for Kruskal-Wallis rank sum test that says these samples are significantly difference like below;
  > NOF1=c(NOF1$`onco$A10`)
  > NOF2=c(NOF2$`onco$A11`)
  > CAF1=c(CAF1$`onco$A12`)
  > CAF2=c(CAF2$`onco$A2`)
  > my_data <- data.frame( 
  +     group = rep(c("NOF1", "NOF2","CAF1","CAF2"), each = 719),
  +     cell_type = c(NOF1,NOF2,  CAF1,CAF2)
  + )
  
  > head((my_data))
    group cell_type
  1  NOF1 11.755100
  2  NOF1  2.955457
  3  NOF1  6.711643
  4  NOF1  6.443439
  5  NOF1  9.995098
  6  NOF1  7.125483
  > 
  
  > kruskal.test(cell_type ~ group, data = my_data)
  
      Kruskal-Wallis rank sum test
  
  data:  cell_type by group
  Kruskal-Wallis chi-squared = 22.006, df = 3, p-value = 6.505e-05
  
  > 
  
  I then did Pairwise comparisons using Wilcoxon rank sum test to see which samples are different with each other.
  > pairwise.wilcox.test(my_data$cell_type, my_data$group,
  +                      p.adjust.method = "BH")
  
      Pairwise comparisons using Wilcoxon rank sum test 
  
  data:  my_data$cell_type and my_data$group 
  
       CAF1   CAF2   NOF1  
  CAF2 0.0058 -      -     
  NOF1 0.2809 4e-05  -     
  NOF2 0.6818 0.0016 0.3811
  
  P value adjustment method: BH 
  > 
  
  My Question is; how I can extract the genes behind the difference between each pair of samples? I mean something like differential expression by non parametric test. also I am not sure if I am right in using log cpm values for these test
  If possible give me a hand to correct myself and get intuition
  Thanks 
  
  user77: All of the genes are behind the difference, you're doing a rank test comparing all of them against each other at once. Your results have more to do with samples having slightly different outlier genes than any interesting biology. You don't have enough replicates to do a non-parametric test.
  
original_text: 4595: <p>I have 2 cell types (NOF and CAF) cultures individually and also have been co-cultured with tumour like this picture.</p>
  
  <p><a href="https://ibb.co/x8Ty1Wz" rel="nofollow noreferrer">https://ibb.co/x8Ty1Wz</a></p>
  
  <p>If this is the log cpm of these 4 samples </p>
  
  <pre><code>&gt; head(onco[,1:4])
               A10       A11        A12        A2
  A2M    11.755100 12.145143 12.9787977 12.407221
  ABCB11  2.955457  2.278363  0.1229266  1.493507
  ABCC2   6.711643  6.974025  0.1229266  7.628936
  ABCC6   6.443439  6.094670  6.1389496  7.483394
  ABCF1   9.995098 10.045613  9.6627424 10.314842
  ABCG2   7.125483  7.448127  6.8669702  8.492895
  &gt; 
  </code></pre>
  
  <p>I have used log of count per million (cpm) for Kruskal-Wallis rank sum test that says these samples are significantly difference like below;</p>
  
  <pre><code>&gt; NOF1=c(NOF1<span class="math-container">$`onco$</span>A10`)
  &gt; NOF2=c(NOF2$`onco<span class="math-container">$A11`)
  &gt; CAF1=c(CAF1$</span>`onco$A12`)
  &gt; CAF2=c(CAF2<span class="math-container">$`onco$</span>A2`)
  &gt; my_data &lt;- data.frame( 
  +     group = rep(c("NOF1", "NOF2","CAF1","CAF2"), each = 719),
  +     cell_type = c(NOF1,NOF2,  CAF1,CAF2)
  + )
  
  &gt; head((my_data))
    group cell_type
  1  NOF1 11.755100
  2  NOF1  2.955457
  3  NOF1  6.711643
  4  NOF1  6.443439
  5  NOF1  9.995098
  6  NOF1  7.125483
  &gt; 
  
  &gt; kruskal.test(cell_type ~ group, data = my_data)
  
      Kruskal-Wallis rank sum test
  
  data:  cell_type by group
  Kruskal-Wallis chi-squared = 22.006, df = 3, p-value = 6.505e-05
  
  &gt; 
  </code></pre>
  
  <p>I then did Pairwise comparisons using Wilcoxon rank sum test to see which samples are different with each other.</p>
  
  <pre><code>&gt; pairwise.wilcox.test(my_data<span class="math-container">$cell_type, my_data$</span>group,
  +                      p.adjust.method = "BH")
  
      Pairwise comparisons using Wilcoxon rank sum test 
  
  data:  my_data<span class="math-container">$cell_type and my_data$</span>group 
  
       CAF1   CAF2   NOF1  
  CAF2 0.0058 -      -     
  NOF1 0.2809 4e-05  -     
  NOF2 0.6818 0.0016 0.3811
  
  P value adjustment method: BH 
  &gt; 
  </code></pre>
  
  <p>My Question is; how I can extract the genes behind the difference between each pair of samples? I mean something like differential expression by non parametric test. also I am not sure if I am right in using log cpm values for these test</p>
  
  <p>If possible give me a hand to correct myself and get intuition</p>
  
  <p>Thanks </p>
   77: <p>All of the genes are behind the difference, you're doing a rank test comparing all of them against each other at once. Your results have more to do with samples having slightly different outlier genes than any interesting biology. You don't have enough replicates to do a non-parametric test.</p>
  