10 Resources
Useful resources for next steps.
10.1 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.
10.2 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.
Seurat data
https://github.com/satijalab/seurat-data
Package for obtaining a few datasets as seurat objects.
10.3 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
10.4 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