<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>

CC BY-SA 4.0
2022-04-05T17:32:56.390Z
2022-04-05T17:32:56.390Z
Tomas Bencomo
<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>

<seurat><single-cell>
Seurat Integration
# 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

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## 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.


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