<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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2024-12-10T08:55:00.477Z
2024-12-11T10:00:02.937Z
2024-12-11T10:00:02.937Z
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<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>

<r><scrnaseq><seurat>
Is there a cheat sheet for Seurat data structure?
# 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

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## 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.


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