Answers.Body: <p>it depends on the size of your data. If you have enough RAM available, then you may directly compute a correlation-based distance matrix using the <em>cor()</em> function (with method="spearman" or method="pearson"). But you may also give a try to Mutual Information distance using the <strong>Parmigene</strong> (<a href="https://cran.r-project.org/web/packages/parmigene/index.html" rel="nofollow noreferrer">https://cran.r-project.org/web/packages/parmigene/index.html</a>) R package or partial correlations with the <strong>Corpcor</strong> (<a href="https://cran.r-project.org/web/packages/corpcor/index.html" rel="nofollow noreferrer">https://cran.r-project.org/web/packages/corpcor/index.html</a>) R package.
  If you have a large expression matrix, remember to use the excellent <strong>data.table</strong> package and its <em>fread()</em> function.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-05-12T07:10:29.800Z
Answers.LastActivityDate: 2020-05-12T07:10:29.800Z
Answers.UserDisplayName: thomas duge de bernonville
Answers.Body: <p>After looking around, it seems like the <a href="https://camoco.readthedocs.io/en/latest/index.html" rel="nofollow noreferrer">Camoco library</a> is suitable. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-05-12T00:33:24.527Z
Answers.Text: It would be nice to hear from the R crowd
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Answers.CreationDate: 2020-05-11T23:39:56.903Z
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Answers.UserDisplayName: An Ignorant Wanderer
Body: <p>What are some good tools (in Python if possible) for creating gene co-expression networks from RNA seq data?</p>
  
Tags: <python><gene-expression>
Title: Tools for creating gene coexpression networks
ThreadText: # Tools for creating gene coexpression networks
  
  (Asked by: username_0 on 2020-05-11)
  
  What are some good tools (in Python if possible) for creating gene co-expression networks from RNA seq data?
  
  **Tags:** python, gene-expression
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-05-12. Score: 1
  
  it depends on the size of your data. If you have enough RAM available, then you may directly compute a correlation-based distance matrix using the *cor()* function (with method="spearman" or method="pearson"). But you may also give a try to Mutual Information distance using the **Parmigene** (<https://cran.r-project.org/web/packages/parmigene/index.html>) R package or partial correlations with the **Corpcor** (<https://cran.r-project.org/web/packages/corpcor/index.html>) R package.
  If you have a large expression matrix, remember to use the excellent **data.table** package and its *fread()* function.
  
  
  ---
  
  ### Answer by username_0 on 2020-05-11. Score: 1
  
  After looking around, it seems like the [Camoco library](https://camoco.readthedocs.io/en/latest/index.html) is suitable.
  
  > **Comments:**
  > **username_2**: It would be nice to hear from the R crowd  
  
  ---
  
  