Answers.Body: <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>
  
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Answers.CreationDate: 2020-07-30T12:19:42.627Z
Answers.Text: I am working with 25 cells only, is that why? so without the adj p-value significance, the results aren't conclusive?
Answers.UserDisplayName: Najeha Mohamed
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Answers.Text: That is the purpose of statistical tests right ? At least if you plot the boxplots and show that there is a "suggestive" difference between cell-types but did not reach adj p-value thresholds, it might be still OK depending on the reviewers. But with out adj. p-values being significant and without seeing the data, I would assume its just noise.
Answers.UserDisplayName: geek_y
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Answers.Text: I've added the featureplot in here. Is that enough to convince the readers?
Answers.UserDisplayName: Najeha Mohamed
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Answers.Text: For me its convincing, just that you don't have statistical power. Did you use wilcox test ? May be you could try something that is based on linear regression ? though you have very few data points.
Answers.UserDisplayName: geek_y
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Answers.Text: yes i used the wilcox test.. anything else i should look into?
Answers.UserDisplayName: Najeha Mohamed
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Body: <p><a href="https://i.sstatic.net/K14Nx.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-30T03:24:16.370Z
Comments.Text: You need to look at adjusted p values only. I'm a little surprised that the difference is not significant when that gene is expressed in 100% vs 0%, but if everything is right, you should trust the math that the difference is not statically significant. Why do you have so few cells with so many reads? Is this really single cell data?
Comments.UserDisplayName: swbarnes2
Tags: <r><scrnaseq><seurat><p-values>
Title: Seurat FindMarkers() output interpretation
ThreadText: # Seurat FindMarkers() output interpretation
  
  (Asked by: username_0 on 2020-07-29)
  
  [![FeaturePlot of the first four features](https://i.sstatic.net/K14Nx.png)](https://i.sstatic.net/K14Nx.png)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.
  
  **Tags:** r, scrnaseq, seurat, p-values
  
  > **Comments:**
  > **username_1**: You need to look at adjusted p values only. I'm a little surprised that the difference is not significant when that gene is expressed in 100% vs 0%, but if everything is right, you should trust the math that the difference is not statically significant. Why do you have so few cells with so many reads? Is this really single cell data?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-07-30. Score: 1
  
  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.
  
  > **Comments:**
  > **username_0**: I am working with 25 cells only, is that why? so without the adj p-value significance, the results aren't conclusive?  
  > **username_2**: That is the purpose of statistical tests right ? At least if you plot the boxplots and show that there is a "suggestive" difference between cell-types but did not reach adj p-value thresholds, it might be still OK depending on the reviewers. But with out adj. p-values being significant and without seeing the data, I would assume its just noise.  
  > **username_0**: I've added the featureplot in here. Is that enough to convince the readers?  
  > **username_2**: For me its convincing, just that you don't have statistical power. Did you use wilcox test ? May be you could try something that is based on linear regression ? though you have very few data points.  
  > **username_0**: yes i used the wilcox test.. anything else i should look into?  
  
  ---
  
  