<p>I'm aware that you've tagged this as python, but unfortunately I'm most familiar with doing single cell analysis using R. Perhaps there's a python way to do something similar.</p>
<p>Seurat has recently included a workflow for reciprocal PCA integration, which the authors claim should work well when the combined datasets are more different than usual:</p>
<blockquote>
<p>RPCA-based integration runs significantly faster, and also represents a more conservative approach where cells in different biological states are less likely to ‘align’ after integration. We therefore, recommend RPCA during integrative analysis where:</p>
<ul>
<li>A substantial fraction of cells in one dataset have no matching type in the other</li>
<li>Datasets originate from the same platform (i.e. multiple lanes of 10x genomics)</li>
<li>There are a large number of datasets or cells to integrate (see INSERT LINK for more tips on integrating large datasets)</li>
</ul>
</blockquote>
<p>A tutorial workflow can be found here:</p>
<p><a href="https://satijalab.org/seurat/articles/integration_rpca.html" rel="nofollow noreferrer">https://satijalab.org/seurat/articles/integration_rpca.html</a></p>

CC BY-SA 4.0
2022-10-17T06:53:03.310Z
Indeed, I am a python user, but this is already helpful. Thanks :)
Divyanshu Srivastava
CC BY-SA 4.0
2022-10-15T08:58:58.750Z
2022-10-15T08:58:58.750Z
gringer
<p>While merging datasets from similar biological conditions, but different experimental conditions is a well-studied topic, and there are many batch correction techniques available. What I want to understand is the right way to merge scRNA seq counts data of healthy and tumor cells.</p>
<p>Does individually log-normalising the counts, followed by a simple concatenation of the objects works, or there is a better way to do this?</p>

<python><scrnaseq>
Correct way of merging scRNAseq datasets of healthy and tumor cell?
# Correct way of merging scRNAseq datasets of healthy and tumor cell?

(Asked by: username_0 on 2022-10-14)

While merging datasets from similar biological conditions, but different experimental conditions is a well-studied topic, and there are many batch correction techniques available. What I want to understand is the right way to merge scRNA seq counts data of healthy and tumor cells.

Does individually log-normalising the counts, followed by a simple concatenation of the objects works, or there is a better way to do this?

**Tags:** python, scrnaseq

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## 1 answers

### Answer by username_1 on 2022-10-15. Score: 1

I'm aware that you've tagged this as python, but unfortunately I'm most familiar with doing single cell analysis using R. Perhaps there's a python way to do something similar.

Seurat has recently included a workflow for reciprocal PCA integration, which the authors claim should work well when the combined datasets are more different than usual:

> RPCA-based integration runs significantly faster, and also represents a more conservative approach where cells in different biological states are less likely to ‘align’ after integration. We therefore, recommend RPCA during integrative analysis where:
>
> * A substantial fraction of cells in one dataset have no matching type in the other
> * Datasets originate from the same platform (i.e. multiple lanes of 10x genomics)
> * There are a large number of datasets or cells to integrate (see INSERT LINK for more tips on integrating large datasets)

A tutorial workflow can be found here:

<https://satijalab.org/seurat/articles/integration_rpca.html>

> **Comments:**
> **username_0**: Indeed, I am a python user, but this is already helpful. Thanks :)  

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