text: user9234: I am using FindMarkers() between 2 groups of cells, my results are listed but i'm having hard time in choosing the right markers. Do I choose according to both the p-values or just one of them? If one of them is good enough, which one should I prefer? I am interested in the marker-genes that are differentiating the groups, so what are the parameters i should look for?
               p_val avg_logFC pct.1 pct.2  p_val_adj
  UBD   3.080608e-06 1.7755753     1 0.000 0.07683959
  CFB   1.262305e-05 1.3989233     1 0.067 0.31485675
  RGS13 1.548593e-05 0.9009480     1 0.200 0.38626551
  PYDC1 1.896309e-05 0.9622537     1 0.133 0.47299636
  BIRC3 4.099414e-05 1.0129472     1 1.000 1.00000000
  ICAM1 4.379895e-05 0.8219610     1 0.200 1.00000000
  
  as you can see, p-value seems significant, however the adjusted p-value is not. and when i performed the test i got this warning In wilcox.test.default(x = c(BC03LN_05 = 0.249819542916203,  ... : cannot compute exact p-value with ties
  I am completely new to this field, and more importantly to mathematics. Please help me understand in an easy way.
  
  user4017: The p-values are not very very significant, so the adj. p-value. You need to plot the gene counts and see why it is the case. It could be because they are captured/expressed only in very very few cells. VlnPlot or FeaturePlot functions should help. You haven't shown the TSNE/UMAP plots of the two clusters, so its hard to comment more.
  
original_text: 9234: <p><a href="https://i.stack.imgur.com/K14Nx.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/K14Nx.png" alt="FeaturePlot of the first four features" /></a>I am using FindMarkers() between 2 groups of cells, my results are listed but i'm having hard time in choosing the right markers. Do I choose according to both the p-values or just one of them? If one of them is good enough, which one should I prefer? I am interested in the marker-genes that are differentiating the groups, so what are the parameters i should look for?</p>
  <pre><code>             p_val avg_logFC pct.1 pct.2  p_val_adj
  UBD   3.080608e-06 1.7755753     1 0.000 0.07683959
  CFB   1.262305e-05 1.3989233     1 0.067 0.31485675
  RGS13 1.548593e-05 0.9009480     1 0.200 0.38626551
  PYDC1 1.896309e-05 0.9622537     1 0.133 0.47299636
  BIRC3 4.099414e-05 1.0129472     1 1.000 1.00000000
  ICAM1 4.379895e-05 0.8219610     1 0.200 1.00000000
  </code></pre>
  <p>as you can see, p-value seems significant, however the adjusted p-value is not. and when i performed the test i got this warning <code>In wilcox.test.default(x = c(BC03LN_05 = 0.249819542916203,  ... : cannot compute exact p-value with ties</code>
  I am completely new to this field, and more importantly to mathematics. Please help me understand in an easy way.</p>
   4017: <p>The p-values are not very very significant, so the adj. p-value. You need to plot the gene counts and see why it is the case. It could be because they are captured/expressed only in very very few cells. <code>VlnPlot</code> or <code>FeaturePlot</code> functions should help. You haven't shown the TSNE/UMAP plots of the two clusters, so its hard to comment more.</p>
  