text: user3183: 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?
  
  user73: 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
  
original_text: 3183: <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>
   73: <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>
  