Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data.
Saskia Freytag,Saskia Freytag,Luyi Tian,Luyi Tian,Ingrid Lönnstedt,Milica Ng,Melanie Bahlo,Melanie Bahlo +7 more
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TLDR
It is found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system, and the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics.Abstract:
Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.read more
Citations
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Single-cell transcriptomics of 20 mouse organs creates a "Tabula Muris"
Overall coordination,Logistical coordination,Library preparation,Cell type annotation,Principal investigators +4 more
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References
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STAR: ultrafast universal RNA-seq aligner
Alexander Dobin,Carrie A. Davis,Felix Schlesinger,Jorg Drenkow,Chris Zaleski,Sonali Jha,Philippe Batut,Mark Chaisson,Thomas R. Gingeras +8 more
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TL;DR: A droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample is described and sequence variation in the transcriptome data is used to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Cole Trapnell,Davide Cacchiarelli,Davide Cacchiarelli,Jonna Grimsby,Prapti Pokharel,Shuqiang Li,Michael A. Morse,Michael A. Morse,Niall J. Lennon,Kenneth J. Livak,Tarjei S. Mikkelsen,Tarjei S. Mikkelsen,John L. Rinn,John L. Rinn,John L. Rinn +14 more
TL;DR: Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.
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An overlap invariant entropy measure of 3D medical image alignment
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