<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>

CC BY-SA 4.0
2022-02-03T15:22:19.393Z
Hi, 

I did a umap to better view samples' correlation and it appears that the distance between the samples is proportional to the correlation.

As for the technical batch effect, I am looking into it but I find it strange that the three samples A, B and C, which come from the same dataset and use the same sequencing protocol, are so far apart. 

I will test those method. 
Thanks you very much for your time
Alexis Finkbeiner
CC BY-SA 4.0
2022-02-02T19:12:55.767Z
2022-02-02T19:12:55.767Z
Chris_Rands
<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.sstatic.net/PqEZq.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/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>

CC BY-SA 4.0
2022-02-02T14:20:38.677Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Community
CC BY-SA 4.0
2022-02-02T14:42:20.700Z
Thank you, I am trying to update my comment to get it as clear as possible.
Alexis Finkbeiner
CC BY-SA 4.0
2022-02-02T22:09:22.487Z
Those correlations don't match your description; A looks very different from the others. Can you confirm that the labels are correct for the graph (e.g. show the code snippet that created it)? I've often found that if create heatmap plots in R with labels defined separately from the matrix, the labels appear in the wrong order.
gringer
<rna-seq><scrnaseq><single-cell><phylogenetics><cellranger>
How to solve correlation problems between different samples in scRNA-seq?
# How to solve correlation problems between different samples in scRNA-seq?

(Asked by: username_0 on 2022-02-02)

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.

[![picture](https://i.sstatic.net/PqEZq.png)](https://i.sstatic.net/PqEZq.png)

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

**Tags:** rna-seq, scrnaseq, single-cell, phylogenetics, cellranger
> **Comments:**
> **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
> **username_0**: Thank you, I am trying to update my comment to get it as clear as possible.  
> **username_2**: Those correlations don't match your description; A looks very different from the others. Can you confirm that the labels are correct for the graph (e.g. show the code snippet that created it)? I've often found that if create heatmap plots in R with labels defined separately from the matrix, the labels appear in the wrong order.  

---

## 1 answers

### Answer by username_3 on 2022-02-02. Score: 1

> 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](https://www.nature.com/articles/s41592-021-01336-8) and [2](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9)), 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.

> **Comments:**
> **username_0**: Hi,   I did a umap to better view samples' correlation and it appears that the distance between the samples is proportional to the correlation.  As for the technical batch effect, I am looking into it but I find it strange that the three samples A, B and C, which come from the same dataset and use the same sequencing protocol, are so far apart.   I will test those method.  Thanks you very much for your time  

---

