16 Resources
Useful resources for next steps.
16.0.1 Suggested Further Reading Material
- Orchestrating Single Cell Analysis with Bioconductor - this book teaches single cell analysis with the bioconductor ecosystem of packages rather than Seurat. Regardless of your preference for Bioconductor or Seurat, it provides an excellent grounding and further depth and rationale behind each step of a single cell analysis.
- Seurat tutorials for gene expression, spatial & multimodal analysis
- Getting started with Signac - the sibling package to Seurat for scATAC analysis
- Monocle documentationn for trajectories
- Cell Annotation with SingleR
- VDJ analysis with Immcantation
16.0.2 Useful links arising from the discussion during the previous workshop
- 10x Genomics link to ribosomal protein expression
- 10x Genomics link to mitochondrial gene expression
- scRNA Tools, catalogue of tools for scRNA Seq analysis
16.0.2.2 Data tools and visualisation
- scTransform Vignette
- Link to the workflowr library
- iSEE Bioconductor library, interactive explorer
- ShinyCell makes interactive Shiny app from Seurat output
- iCellR interactive data explorer
- Diffusion maps for single cell instead of umaps *Projections of a high-dimensional dataset with an animated scatter-plot
16.1 Help and fruther Resources
Seurat Vignettes
https://satijalab.org/seurat/index.html
There are a good many Seurat vigettes for different aspects of the Seurat package. E.g.
- Guided Clustering tutorial : We’ve just worked through this
- Differential expression : An Exploration of differential expression methods within Seurat
- Data integration : Seurat’s data integration is a popular method to combine different datasets into one joint analysis.
Seurat Cheatsheet
https://satijalab.org/seurat/articles/essential_commands.html
A useful resource for asking; How can I do ‘X’ with my seurat object?
OSCA
https://bioconductor.org/books/release/OSCA/
An comprehensive resource for analysis approaches for single cell data. This uses the SingleCellExperiment bioconductor ecosystem, but alot of the same principle still apply.
This includes a good discussion of useing pseudobulk approaches, worth checking out for differential expression analyses.
MBP training Reading list
https://monashbioinformaticsplatform.github.io/Single-Cell-Workshop/
A workshop page for a previous workshop (upon which this one is based) run by Monash Bioinformatics Platform - down the bottom there is an extensive list of useful single cell links and resources.
Biocommons Single Cell Omics
https://www.biocommons.org.au/single-cell-omics
Join the single cell omics community resources being setup by biocommons.
16.2 Data
Demo 10X data
https://www.10xgenomics.com/resources/datasets
10X genomics have quite a few example datasets availble for download (including PBMC3k). This is a useful resource if you want to see what the ‘raw’ data looks like for a particular technology.
GEO
https://www.ncbi.nlm.nih.gov/geo/
Many papers publish their raw single cell data in GEO. Formats vary, but often you can find the counts matrix. # (PART) Other resources {-}
Seurat data
https://github.com/satijalab/seurat-data
Package for obtaining a few datasets as seurat objects.
16.3 Analysis Tools
A handful of the many tools that might be worth checking out for next steps.
Cyclone
https://pubmed.ncbi.nlm.nih.gov/26142758/
Part of the scran package, cyclone is a(nother) method for determining cell phase. Doco
Harmony
https://portals.broadinstitute.org/harmony/articles/quickstart.html
Method for integration of multiple single cell datasets.
SingleR
http://bioconductor.org/books/release/SingleRBook/
There is extensive documentation for the singleR package in the ‘singleR’ book.
Scrublet
https://github.com/swolock/scrublet
A python based tool for doublet detection. One of many tools in this space.
ScVelo
https://scvelo.readthedocs.io/
A package for single cell RNA velocity analysis, useful for developmental/pseudotime trajectories. Python/scanpy based.
Monocle
https://cole-trapnell-lab.github.io/monocle3/
A package for single cell developmental//pseudotime trajectory analysis.
TidySeurat
https://stemangiola.github.io/tidyseurat/
For fans of tidyverse-everything, there’s tidyseurat. Example workflow here
16.4 Preprocessing Tools
Tooks that process raw sequencing data into counts matricies
Cell Ranger
CellRanger is the 10X tool that takes raw fastq sequence files and produces the counts matricies that are the starting point for today’s analysis. It only works for 10X data.
STARSolo
STAR is an aligner (which is actually used within cell ranger). STARSolo is a tool for producing counts matricies, and is configurable enough for use with multiple technologies.
https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md