<p>Single-cell tools were developed to address some specific factors unique to scRNA-seq, such as:</p>

<ul>
<li>a lot of dropouts, so hundreds or thousands of detected genes, not tens of thousands (for mammalian genomes)</li>
<li>hundreds or thousands of replicates</li>
<li>not necessarily well-defined groups</li>
</ul>

<p>Thus, I would say your experiment is a lot closer to bulk RNA-seq than scRNA-seq.</p>

CC BY-SA 3.0
2017-12-13T18:51:29.993Z
2017-12-13T18:51:29.993Z
burger
<p>We have RNA-seq data from libraries prepared using the Smartseq2 single-cell protocol on <strong>500 cells</strong> (mini-bulk) / library. The complexity is much better than with single-cells (~14k genes for 1.5M reads).</p>

<p>6 cell types / biological replicates were collected with flow cytometry, and there are 4 control and 5 diseased biological replicates (patients).</p>

<p>We are mostly interested in the differences between control and disease either overall or within each cell type.</p>

<p>Can single-cell tools (e.g. Seurat) be used on this dataset? It is neither bulk nor single-cell data. How would you approach this? </p>

CC BY-SA 3.0
2017-12-06T11:31:18.570Z
I would process it both ways and check if they are comparable. If not then I would need look into the methods more in detail (To see if they are applicable in this data)
llrs
<rna-seq><sequence-analysis><single-cell>
Mini-bulk RNA-seq and scRNA-seq analysis differences?
# Mini-bulk RNA-seq and scRNA-seq analysis differences?

(Asked by: username_0 on 2017-12-06)

We have RNA-seq data from libraries prepared using the Smartseq2 single-cell protocol on **500 cells** (mini-bulk) / library. The complexity is much better than with single-cells (~14k genes for 1.5M reads).

6 cell types / biological replicates were collected with flow cytometry, and there are 4 control and 5 diseased biological replicates (patients).

We are mostly interested in the differences between control and disease either overall or within each cell type.

Can single-cell tools (e.g. Seurat) be used on this dataset? It is neither bulk nor single-cell data. How would you approach this?

**Tags:** rna-seq, sequence-analysis, single-cell
> **Comments:**
> **username_1**: I would process it both ways and check if they are comparable. If not then I would need look into the methods more in detail (To see if they are applicable in this data)  

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## 1 answers

### Answer by username_2 on 2017-12-13. Score: 2

Single-cell tools were developed to address some specific factors unique to scRNA-seq, such as:

* a lot of dropouts, so hundreds or thousands of detected genes, not tens of thousands (for mammalian genomes)
* hundreds or thousands of replicates
* not necessarily well-defined groups

Thus, I would say your experiment is a lot closer to bulk RNA-seq than scRNA-seq.


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