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?
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$\begingroup$ 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) $\endgroup$– llrsOct 16, 2017 at 14:33
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$\begingroup$ 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. cell.com/molecular-cell/abstract/S1097-2765(17)30049-7 $\endgroup$– Eric A BrennerOct 16, 2017 at 14:54
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4 Answers
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This article uses the freely available R package dropbead for filtering and then Seurat to perform a principal component analysis that groups together affine transcriptomes. It could be what you are looking for.
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$\begingroup$ I've seen that paper and read the abstract, but I didn't know about Dropbead. Thank for the info! $\endgroup$ Oct 17, 2017 at 15:00
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$\begingroup$ 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 :) $\endgroup$– llrsOct 17, 2017 at 15:24
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$\begingroup$ The DESCRIPTION file states GNU GPL3, but yes it would be good to have a LICENSE file $\endgroup$– PeterJun 23, 2018 at 13:26
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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)
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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 for more details.
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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 used Seurat to process both C1 Fluidigm data (>4000 genes per cell) and 10X data (>1000 genes per cell only).
This human limb paper 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.
