text: user1628: 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?
  
  user181: 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)
  
  user2471: 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.
  
  user1637: 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. 
  
original_text: 1628: <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>
   181: <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>
   2471: <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>
   1637: <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>
  