Answers.Body: <p>The github repo has a <a href="https://github.com/satijalab/seurat/wiki" rel="nofollow noreferrer">section</a> for it, but it not up to date.
  You can look at it to have a general understanding on how things are stored and then reference back to the updated functions and workflow to see what is put where.</p>
  
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Answers.CreationDate: 2024-12-10T08:55:00.477Z
Answers.LastActivityDate: 2024-12-11T10:00:02.937Z
Answers.LastEditDate: 2024-12-11T10:00:02.937Z
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Body: <p>I'm working on a Seurat data object that has gone through common processing, including <code>NormalizeData()</code>, <code>ScaleData()</code>, and <code>SCTransform()</code> etc.</p>
  <p>I find that as the processing steps increase, the internal data structure of Seurat becomes more and more complex.</p>
  <pre><code>seurat_object
  An object of class Seurat 
  79355 features across 44566 samples within 3 assays 
  Active assay: integrated (25226 features, 25226 variable features)
   2 layers present: data, scale.data
   2 other assays present: RNA, SCT
   3 dimensional reductions calculated: pca, umap, tsne
  </code></pre>
  <p>For example, after integrating data, I have an <code>integrated</code> assay, but this confuses me. I'm not clear about its differences with data, scale.data, and also, if I need to get scaled data for further analysis, should I use <code>integrated</code> assay or others, or I need to do this process again?</p>
  <p>I wonder if there are some concise documents that explain the function of each data?</p>
  
Tags: <r><scrnaseq><seurat>
Title: Is there a cheat sheet for Seurat data structure?
ThreadText: # Is there a cheat sheet for Seurat data structure?
  
  (Asked by: username_0 on 2024-12-02)
  
  I'm working on a Seurat data object that has gone through common processing, including `NormalizeData()`, `ScaleData()`, and `SCTransform()` etc.
  
  I find that as the processing steps increase, the internal data structure of Seurat becomes more and more complex.
  
  ```
  seurat_object
  An object of class Seurat 
  79355 features across 44566 samples within 3 assays 
  Active assay: integrated (25226 features, 25226 variable features)
   2 layers present: data, scale.data
   2 other assays present: RNA, SCT
   3 dimensional reductions calculated: pca, umap, tsne
  
  ```
  
  For example, after integrating data, I have an `integrated` assay, but this confuses me. I'm not clear about its differences with data, scale.data, and also, if I need to get scaled data for further analysis, should I use `integrated` assay or others, or I need to do this process again?
  
  I wonder if there are some concise documents that explain the function of each data?
  
  **Tags:** r, scrnaseq, seurat
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-10. Score: 1
  
  The github repo has a [section](https://github.com/satijalab/seurat/wiki) for it, but it not up to date.
  You can look at it to have a general understanding on how things are stored and then reference back to the updated functions and workflow to see what is put where.
  
  
  ---
  
  