<p><a href="https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0383-5" rel="nofollow noreferrer">This</a> article uses the freely available R package <a href="https://github.com/rajewsky-lab/dropbead" rel="nofollow noreferrer">dropbead</a> for filtering and then <a href="https://www.nature.com/nbt/journal/v33/n5/full/nbt.3192.html" rel="nofollow noreferrer">Seurat</a> to perform a principal component analysis that groups together affine transcriptomes. It could be what you are looking for. </p>

CC BY-SA 3.0
2017-10-17T15:00:11.643Z
I've seen that paper and read the abstract, but I didn't know about Dropbead. Thank for the info!
Eric A Brenner
CC BY-SA 3.0
2017-10-17T15:24:32.433Z
Note that there isn't any license for the package. It might not be safe to use it. (Although I am sure that if you contact the authors they will grant you permission and correct this :)
llrs
CC BY-SA 4.0
2018-06-23T13:26:32.167Z
The DESCRIPTION file states GNU GPL3, but yes it would be good to have a LICENSE file
Peter
CC BY-SA 3.0
2017-10-17T14:17:54.110Z
2017-10-17T14:17:54.110Z
LinuxBlanket
<p>You can try a hierarchical clustering tool HGC (hierarchical graph clustering).</p>
<p>You may get cell population at different hierarchy by choosing multiple cutting heights. See <a href="https://academic.oup.com/bioinformatics/article-abstract/37/21/3964/6294402?redirectedFrom=fulltext" rel="nofollow noreferrer">the paper</a> for more details.</p>

CC BY-SA 4.0
2022-01-18T16:02:07.133Z
2022-01-19T10:02:06.957Z
2022-01-19T10:02:06.957Z
Sijie Chen
<p>SC3 is part of the Bioconductor library for R. It is based on PCA and spectral dimensionality reductions and utilizes k-means. In the introductory paper the authors perform analysis on a Drop-seq dataset of more than 44,000 cells. SC3 outputs several useful plots, including a consensus matrix, expression panel, and a marker genes heatmap. All of these utilize the hierarchical clustering.</p>

<p>I can't speak from personal experience but the package seems very user friendly. Being a Bioconductor package means it has lots of support and excellent documentation. I am getting ready to use it myself for a small Drop-seq dataset of olfactory epithelial cells.</p>

<hr>

<p>SC3: consensus clustering of single-cell RNA-seq data : Nature Methods : Nature Research. Available at: <a href="https://www.nature.com/nmeth/journal/v14/n5/full/nmeth.4236.html" rel="nofollow noreferrer">https://www.nature.com/nmeth/journal/v14/n5/full/nmeth.4236.html</a>. (Accessed: 31st October 2017)</p>

CC BY-SA 3.0
2017-10-31T18:17:50.443Z
2017-10-31T18:17:50.443Z
Kohl Kinning
<p>The current version of Seurat and Scanpy can both handle Drop-seq data. If you want to include more low-reads cells you can loosen the filtering thresholds in either of these packages. These two packages really can handle both low-read-high-cell data and high-read-low-cell data.</p>
<p><a href="https://www.nature.com/articles/s41586-020-2536-x" rel="nofollow noreferrer">This ENCODE paper</a> used Seurat to process both C1 Fluidigm data (&gt;4000 genes per cell) and 10X data (&gt;1000 genes per cell only).</p>
<p><a href="https://www.sciencedirect.com/science/article/pii/S1934590920301569" rel="nofollow noreferrer">This human limb paper</a> used Seurat to process Drop-seq data, although they didn't annotate the cell types well. I used Seurat to reprocess their data and it works much better than what the paper presents.</p>
<p>I guess the key is parameter tuning.</p>

CC BY-SA 4.0
2020-09-04T15:42:38.657Z
2020-09-04T15:42:38.657Z
Code42
<p>I plan on collecting some drop-seq data from brain tissue (from mice and humans). Using only the 75-85% of genes that have 1:1 orthology between mice and humans, I'd like to cluster the cells by transcriptomic similarity and get a dendrogram to use for cell type classification. There are tons of tools for processing and clustering single cell RNA-seq data (<a href="https://www.scrna-tools.org/tools" rel="nofollow noreferrer">https://www.scrna-tools.org/tools</a>), but I get the impression that most of them were not designed with Drop-seq in mind. Anyone have any insight into the pros and cons of the best tools for Drop-seq analysis?</p>

CC BY-SA 3.0
2017-10-16T14:33:05.257Z
What are the particularities in Drop-seq that would require a different tool/method to analyze it? (It is the first time I heard about drop-seq and I am not totally sure I understood the difference between drop-seq and sc-seq)
llrs
CC BY-SA 3.0
2017-10-16T14:54:16.273Z
Compared with other scRNA-seq techniques (like Smart-seq), Drop-seq sacrifices read counts for cell counts. That's the big difference. It's also very cost-effective and has been increasing in popularity since it came out in 2015. http://www.cell.com/molecular-cell/abstract/S1097-2765(17)30049-7
Eric A Brenner
<rna-seq><clustering><single-cell>
Getting hierarchy of cell populations with Drop-seq data
# Getting hierarchy of cell populations with Drop-seq data

(Asked by: username_0 on 2017-10-15)

I plan on collecting some drop-seq data from brain tissue (from mice and humans). Using only the 75-85% of genes that have 1:1 orthology between mice and humans, I'd like to cluster the cells by transcriptomic similarity and get a dendrogram to use for cell type classification. There are tons of tools for processing and clustering single cell RNA-seq data (<https://www.scrna-tools.org/tools>), but I get the impression that most of them were not designed with Drop-seq in mind. Anyone have any insight into the pros and cons of the best tools for Drop-seq analysis?

**Tags:** rna-seq, clustering, single-cell
> **Comments:**
> **username_1**: What are the particularities in Drop-seq that would require a different tool/method to analyze it? (It is the first time I heard about drop-seq and I am not totally sure I understood the difference between drop-seq and sc-seq)  
> **username_0**: Compared with other scRNA-seq techniques (like Smart-seq), Drop-seq sacrifices read counts for cell counts. That's the big difference. It's also very cost-effective and has been increasing in popularity since it came out in 2015. http://www.cell.com/molecular-cell/abstract/S1097-2765(17)30049-7  

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## 4 answers

### Answer by username_2 on 2017-10-17. Score: 4

[This](https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0383-5) article uses the freely available R package [dropbead](https://github.com/rajewsky-lab/dropbead) for filtering and then [Seurat](https://www.nature.com/nbt/journal/v33/n5/full/nbt.3192.html) to perform a principal component analysis that groups together affine transcriptomes. It could be what you are looking for.

> **Comments:**
> **username_0**: I've seen that paper and read the abstract, but I didn't know about Dropbead. Thank for the info!  
> **username_1**: Note that there isn't any license for the package. It might not be safe to use it. (Although I am sure that if you contact the authors they will grant you permission and correct this :)  
> **username_3**: The DESCRIPTION file states GNU GPL3, but yes it would be good to have a LICENSE file  

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### Answer by username_4 on 2022-01-18. Score: 1

You can try a hierarchical clustering tool HGC (hierarchical graph clustering).

You may get cell population at different hierarchy by choosing multiple cutting heights. See [the paper](https://academic.oup.com/bioinformatics/article-abstract/37/21/3964/6294402?redirectedFrom=fulltext) for more details.


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### Answer by username_5 on 2017-10-31. Score: 1

SC3 is part of the Bioconductor library for R. It is based on PCA and spectral dimensionality reductions and utilizes k-means. In the introductory paper the authors perform analysis on a Drop-seq dataset of more than 44,000 cells. SC3 outputs several useful plots, including a consensus matrix, expression panel, and a marker genes heatmap. All of these utilize the hierarchical clustering.

I can't speak from personal experience but the package seems very user friendly. Being a Bioconductor package means it has lots of support and excellent documentation. I am getting ready to use it myself for a small Drop-seq dataset of olfactory epithelial cells.

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SC3: consensus clustering of single-cell RNA-seq data : Nature Methods : Nature Research. Available at: <https://www.nature.com/nmeth/journal/v14/n5/full/nmeth.4236.html>. (Accessed: 31st October 2017)


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### Answer by username_6 on 2020-09-04. Score: 0

The current version of Seurat and Scanpy can both handle Drop-seq data. If you want to include more low-reads cells you can loosen the filtering thresholds in either of these packages. These two packages really can handle both low-read-high-cell data and high-read-low-cell data.

[This ENCODE paper](https://www.nature.com/articles/s41586-020-2536-x) used Seurat to process both C1 Fluidigm data (>4000 genes per cell) and 10X data (>1000 genes per cell only).

[This human limb paper](https://www.sciencedirect.com/science/article/pii/S1934590920301569) used Seurat to process Drop-seq data, although they didn't annotate the cell types well. I used Seurat to reprocess their data and it works much better than what the paper presents.

I guess the key is parameter tuning.


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