Answers.Body: <p>Are you using the same data from the integration tutorial or using your own data? How many cells/genes are in your dataset?</p>
  <p>Seurat's default integration method (CCA) is known to be runtime/memory intensive. It can handle large datasets but may require lots of CPU cores/memory. I have found that the amount of RAM also seems to increase as more CPU cores are used. That said, I have been able to run the integration tutorial with the included dataset on an 8GB laptop.</p>
  <p>The easiest solution is to simply use more resources when integrating: increase the amount of RAM and consider using multiple cores (there is a separate Seurat tutorial on parallelization). If you are encountering runtime/memory issues and you cannot scale up your resources, I would suggest looking at alternative integration methods:</p>
  <ol>
  <li>Seurat includes another integration approach, <a href="https://satijalab.org/seurat/articles/integration_rpca.html" rel="nofollow noreferrer">RPCA</a> which is supposed to be less memory intensive at the cost of being more conservative with integration</li>
  <li>There are several other integration methods that provide similar results. I have used <a href="https://portals.broadinstitute.org/harmony/articles/quickstart.html" rel="nofollow noreferrer">Harmony</a> and found that it requires significantly less resources than the Seurat integration approaches (both CCA and RPCA). A quick google search of single cell RNA-seq integration methods will turn up other popular methods with benchmarks to compare and contrasts different approaches.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-04-05T17:32:56.390Z
Answers.LastActivityDate: 2022-04-05T17:32:56.390Z
Answers.UserDisplayName: Tomas Bencomo
Body: <p>I'm following the instructions for integration:
  <a href="https://satijalab.org/seurat/articles/integration_introduction.html" rel="nofollow noreferrer">https://satijalab.org/seurat/articles/integration_introduction.html</a></p>
  <p>And it's taking a while to run:</p>
  <pre><code>immune.anchors &lt;- FindIntegrationAnchors(object.list = ifnb.list, normalization.method = &quot;SCT&quot;,
      anchor.features = features)
  </code></pre>
  <p>I'm integrating 6 datasets.</p>
  <p>Is this function designed well for large amount of data?</p>
  
Tags: <seurat><single-cell>
Title: Seurat Integration
ThreadText: # Seurat Integration
  
  (Asked by: username_0 on 2022-03-31)
  
  I'm following the instructions for integration:
  <https://satijalab.org/seurat/articles/integration_introduction.html>
  
  And it's taking a while to run:
  
  ```
  immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, normalization.method = "SCT",
      anchor.features = features)
  
  ```
  
  I'm integrating 6 datasets.
  
  Is this function designed well for large amount of data?
  
  **Tags:** seurat, single-cell
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-04-05. Score: 1 | Accepted answer
  
  Are you using the same data from the integration tutorial or using your own data? How many cells/genes are in your dataset?
  
  Seurat's default integration method (CCA) is known to be runtime/memory intensive. It can handle large datasets but may require lots of CPU cores/memory. I have found that the amount of RAM also seems to increase as more CPU cores are used. That said, I have been able to run the integration tutorial with the included dataset on an 8GB laptop.
  
  The easiest solution is to simply use more resources when integrating: increase the amount of RAM and consider using multiple cores (there is a separate Seurat tutorial on parallelization). If you are encountering runtime/memory issues and you cannot scale up your resources, I would suggest looking at alternative integration methods:
  
  1. Seurat includes another integration approach, [RPCA](https://satijalab.org/seurat/articles/integration_rpca.html) which is supposed to be less memory intensive at the cost of being more conservative with integration
  2. There are several other integration methods that provide similar results. I have used [Harmony](https://portals.broadinstitute.org/harmony/articles/quickstart.html) and found that it requires significantly less resources than the Seurat integration approaches (both CCA and RPCA). A quick google search of single cell RNA-seq integration methods will turn up other popular methods with benchmarks to compare and contrasts different approaches.
  
  
  ---
  
  