Overview
Though Spectre was not designed explicitly to work with genomic data, a number of Spectre's processing, analysis, or plotting functions can be helpful in analysing scRNAseq data. Here we provide analysis options and tools to support scRNAseq analysis, in conjunction with existing tools such as Seurat and SingleCellExperiment.
Citation
If you use Spectre in your work, please consider citing Ashhurst TM, Marsh-Wakefield F, Putri GH et al. (2020). bioRxiv. 2020.10.22.349563. To continue providing open-source tools such as Spectre, it helps us if we can demonstrate that our efforts are contributing to analysis efforts in the community. Please also consider citing the authors of the individual packages or tools (e.g. CytoNorm, FlowSOM, tSNE, UMAP, etc) that are critical elements of your analysis work. We have provided some generic text that you can use for your methods section with each protocol and on the 'about' page.
Common scRNAseq analysis tools
Spectre is not specifically designed to work with scRNAseq data, but provides functions that may aid in analysis and plotting. A variety of popular tools are available for the primary analysis of your scRNAseq data. Specifically, Seurat and SingleCellExperiment objects can be used in Spectre.
| Analysis using Seurat or the SingleCellExperiment framework | Bioconductor/SingleCellExperiment | Monocle |
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| Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data. | This is the website for “Orchestrating Single-Cell Analysis with Bioconductor”, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq). | Build single-cell trajectories with the software that introduced pseudotime. Find cell fate decisions and the genes regulated as they're made. Group and classify your cells based on gene expression. Identify new cell types and states and the genes that distinguish them. |
SingleCellExperiment objects compatible with Spectre | SingleCellExperiment objects compatible with Spectre | Monocle's CellDataSet objects not yet compatible with Spectre |
Spectre tutorials for scRNAseq data
Here we provide a number of tutorials for specific scRNAseq analysis options.
| Converting from Seurat or SingleCellExperiment objects to a data.table | Using Spectre's plotting and aggregation tools on scRNAseq data | Using cytometry clustering tools on scRNAseq data |
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| To facilitate analysis or plotting using Spectre, or other generic tools (e.g. ggplot2, etc), Spectre provides a function for converting Seurat or SingleCellExperiment objects into a simple data.table. | Here we provide a brief tutorial on using Spectre's plotting, aggregation, and summary data functions on scRNAseq data. | Though not specifically designed for genomics, Spectre can be used on any form of 'single cell' data, including single cell RNA sequencing (scRNAseq). Here we provide a brief tutorial for analysing scRNAseq after initial preparation in CellRanger/Seurat. |
| COMING SOON | COMING SOON |
Pseudotime analysis tools
| Monocle | PAGA | Slingshot |
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Single-cell trajectory analysis how cells choose between one of several possible end states. The new reconstruction algorithms introduced in Monocle 2 can robustly reveal branching trajectories, along with the genes that cells use to navigate these decisions. | PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow | Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. |
Other links: https://broadinstitute.github.io/2019_scWorkshop/pseudotime-cell-trajectories.html |
