Answers.Body: <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>
  
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Answers.CreationDate: 2017-10-17T15:00:11.643Z
Answers.Text: I've seen that paper and read the abstract, but I didn't know about Dropbead. Thank for the info!
Answers.UserDisplayName: Eric A Brenner
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Answers.CreationDate: 2017-10-17T15:24:32.433Z
Answers.Text: 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 :)
Answers.UserDisplayName: llrs
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Answers.Text: The DESCRIPTION file states GNU GPL3, but yes it would be good to have a LICENSE file
Answers.UserDisplayName: Peter
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Answers.Body: <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>
  
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Answers.UserDisplayName: Sijie Chen
Answers.Body: <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>
  
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Answers.Body: <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>
  
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Answers.CreationDate: 2020-09-04T15:42:38.657Z
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Answers.UserDisplayName: Code42
Body: <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>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-10-16T14:33:05.257Z
Comments.Text: 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)
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-10-16T14:54:16.273Z
Comments.Text: 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
Comments.UserDisplayName: Eric A Brenner
Tags: <rna-seq><clustering><single-cell>
Title: Getting hierarchy of cell populations with Drop-seq data
ThreadText: # 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  
  
  ---
  
  ## 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  
  
  ---
  
  ### 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.
  
  
  ---
  
  ### 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.
  
  ---
  
  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)
  
  
  ---
  
  ### 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.
  
  
  ---
  
  