Answers.Body: <p>What I would personally do is run a Χ<sup>2</sup></p>
  <pre><code>from scipy.stats import chisquare
  import numpy as np
  
  pan = [11, 39, 4 ....]
  core = [29, 50, 7 ... ]
  f_exp = np.array(pan)
  f_obs = np. array(core)
  print (chisquare(f_obs=f_obs, f_exp=f_exp))
  </code></pre>
  <p>This will output the total statistic and whether its above or below the critical probability.</p>
  <p>Fisher's Exact test is essentially Χ<sup>2</sup> for a small number of observations, which is not the case with your data.</p>
  <p>Looking at your data Χ<sup>2</sup> is not likely to be &quot;significant&quot; (which is the result you are looking for), i.e. the pan genome is the core genome across all COGs.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-03-05T05:56:13.493Z
Answers.Text: Thank you very much for all your efforts @M__ ! Hopefully, this works.
Answers.UserDisplayName: K_081
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-03-05T03:20:46.687Z
Answers.LastActivityDate: 2024-03-05T03:26:13.437Z
Answers.LastEditDate: 2024-03-05T03:26:13.437Z
Answers.UserDisplayName: M__
Body: <p>This is a continuation of the following question -</p>
  <p><a href="https://bioinformatics.stackexchange.com/questions/22104/fishers-exact-test-for-cog-categories-in-pan-core-genome-analysis/22108">Fisher&#39;s exact test for COG categories in pan, core genome analysis</a></p>
  <p>Also, the dataset -</p>
  <p><a href="https://i.sstatic.net/7dJra.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7dJra.png" alt="COG categories dataset" /></a></p>
  <p>How do I perform a Fisher's exact test on this data to show that a COG category is significantly more or less in the pan/core/accessory/species specific genomes?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-01T14:11:55.737Z
Comments.Text: I'll post a response late Monday, sorry really busy. Basically you can do spot checks via Fishers Exact test or go the whole hog and do a Chi2 test. The matrix you have is pan/core vs cogs.
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-01T14:29:42.767Z
Comments.Text: Hi @M__, good to know. Thanks a lot!
Comments.UserDisplayName: K_081
Tags: <r><eggnog><pangenome>
Title: Calculating Fisher's exact test for COG categories
ThreadText: # Calculating Fisher's exact test for COG categories
  
  (Asked by: username_0 on 2024-02-23)
  
  This is a continuation of the following question -
  
  [Fisher's exact test for COG categories in pan, core genome analysis](https://bioinformatics.stackexchange.com/questions/22104/fishers-exact-test-for-cog-categories-in-pan-core-genome-analysis/22108)
  
  Also, the dataset -
  
  [![COG categories dataset](https://i.sstatic.net/7dJra.png)](https://i.sstatic.net/7dJra.png)
  
  How do I perform a Fisher's exact test on this data to show that a COG category is significantly more or less in the pan/core/accessory/species specific genomes?
  
  **Tags:** r, eggnog, pangenome
  
  > **Comments:**
  > **username_1**: I'll post a response late Monday, sorry really busy. Basically you can do spot checks via Fishers Exact test or go the whole hog and do a Chi2 test. The matrix you have is pan/core vs cogs.  
  > **username_0**: Hi @username_1, good to know. Thanks a lot!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-03-05. Score: 1 | Accepted answer
  
  What I would personally do is run a Χ2
  
  ```
  from scipy.stats import chisquare
  import numpy as np
  
  pan = [11, 39, 4 ....]
  core = [29, 50, 7 ... ]
  f_exp = np.array(pan)
  f_obs = np. array(core)
  print (chisquare(f_obs=f_obs, f_exp=f_exp))
  
  ```
  
  This will output the total statistic and whether its above or below the critical probability.
  
  Fisher's Exact test is essentially Χ2 for a small number of observations, which is not the case with your data.
  
  Looking at your data Χ2 is not likely to be "significant" (which is the result you are looking for), i.e. the pan genome is the core genome across all COGs.
  
  > **Comments:**
  > **username_0**: Thank you very much for all your efforts @username_1 ! Hopefully, this works.  
  
  ---
  
  