text: user14979: I am trying to align and merge different samples from NCBI.
  I end up having correlation problem with these sample. The picture below shows an heatmap of the R² by doing a linear regression between 2 samples' count matrices.
  
  Samples A, B and C are from the same project and sample D from another. A, B and C are sequenced using the 10X Genomics for Single Cell V(D)J + 5' Gene Expression, and Chromium Next GEM Single Cell 3ʹ v3.1 for sample D.
  All samples have been recovered from NCBI using fastq-dump — internaly threaded to be more efficient — and aligned using cellranger with the default human genome given by 10x.
  QC shows some GC content small shifts along side some huge duplication level — even for scRNA-seq.
  Am I missing something important when it comes to merge datasets?
  Thank you very much
  
  user104: 
  The picture below shows an heatmap of the R² by doing a linear
  regression between 2 samples' count matrices.
  
  In addition to doing a heatmap, I'd do a conventional UMAP/t-SNE plot and see how much seperation of the samples there are across all the different clusters/cell types.
  
  Am I missing something important when it comes to merge datasets?
  
  Certainly there are 'technology batch effects' when comparing 3' and 5' data from the same tissue type. You can try to correct for these when merging the dataset by using a batch correction/integration tool (see reviews 1 and 2), but will likely be imperfect.
  
  QC shows some GC content small shifts along side some huge duplication
  level — even for scRNA-seq.
  
  If you're using a tool like fastQC then note that high duplication levels are expected for (single cell) RNAseq from highly expressed genes.
  
original_text: 14979: <p>I am trying to align and merge different samples from NCBI.
  I end up having correlation problem with these sample. The picture below shows an heatmap of the <em>R²</em> by doing a linear regression between 2 samples' count matrices.</p>
  <p><a href="https://i.stack.imgur.com/PqEZq.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/PqEZq.png" alt="picture" /></a></p>
  <p>Samples <strong>A</strong>, <strong>B</strong> and <strong>C</strong> are from the same project and sample <strong>D</strong> from another. A, B and C are sequenced using the <em>10X Genomics for Single Cell V(D)J + 5' Gene Expression</em>, and <em>Chromium Next GEM Single Cell 3ʹ v3.1</em> for sample D.</p>
  <p>All samples have been recovered from NCBI using <code>fastq-dump</code> — internaly threaded to be more efficient — and aligned using <code>cellranger</code> with the default human genome given by 10x.</p>
  <p>QC shows some GC content small shifts along side some huge duplication level — even for scRNA-seq.</p>
  <p>Am I missing something important when it comes to merge datasets?</p>
  <p>Thank you very much</p>
   104: <blockquote>
  <p>The picture below shows an heatmap of the R² by doing a linear
  regression between 2 samples' count matrices.</p>
  </blockquote>
  <p>In addition to doing a heatmap, I'd do a conventional UMAP/t-SNE plot and see how much seperation of the samples there are across all the different clusters/cell types.</p>
  <blockquote>
  <p>Am I missing something important when it comes to merge datasets?</p>
  </blockquote>
  <p>Certainly there are 'technology batch effects' when comparing 3' and 5' data from the same tissue type. You can try to correct for these when merging the dataset by using a batch correction/integration tool (see reviews <a href="https://www.nature.com/articles/s41592-021-01336-8" rel="nofollow noreferrer">1</a> and <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9" rel="nofollow noreferrer">2</a>), but will likely be imperfect.</p>
  <blockquote>
  <p>QC shows some GC content small shifts along side some huge duplication
  level — even for scRNA-seq.</p>
  </blockquote>
  <p>If you're using a tool like fastQC then note that high duplication levels are expected for (single cell) RNAseq from highly expressed genes.</p>
  