This repository contains course materials for the single-cell data analysis workshop hosted at the Weatherall Institute of Molecular Medicine, Oxford on 9 September 2016.
Download or clone the repository so that you have everything you need to participate in the course.
This repository contains data files and RMarkdown documents that can be used for the analysis of the datasets. The data was kindly provided by James Kinchen ("jk" data) and Cynthia Sandor ("cs" data).
Data files:
cs_scater_workshop.RData
- dataset from Cynthia Sandorjk_anon_data.RData
- dataset from James Kinchen
Rmd files:
There are two versions of each RMarkdown document, a "skeleton" version without the R code and a "complete" version with full R code. The idea is to work from the "skeleton" version and consult the "complete" version when you get stuck.
- Cynthia Sandor's data analysis:
cs_surgery_report_skeleton.Rmd
,cs_surgery_report_complete.Rmd
- James Kinchen's data analysis:
jk_surgery_report_skeleton.Rmd
,jk_surgery_report_complete.Rmd
HTML files:
HTML reports produced by running the above ("complete") Rmd files in case you want to check the expected code output and plots.
cs_surgery_report_complete.html
jk_surgery_report_complete.html
The above should give you everything you need to work along with the analyses in the workshop. The Rmd skeleton files should also be a good place to keep your own notes on the workshop and analyses.
You will need to have the following R packages installed: scater
, scran
, WGCNA
, gplots
. To get the most out of scater
it is benefical also to have Rtsne
, mvoutlier
and cowplot
installed. We will use the development versions of the Bioconductor packages to get the
install.packages(c("gplots", "Rtsne", "mvoutlier", "cowplot"))
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite(c("scater", "WGCNA", "scran"))
Optional (but fun): M3Drop
.
install.packages("devtools")
devtools::install_github('tallulandrews/M3Drop')
-
scater
pre-print: http://biorxiv.org/content/early/2016/08/15/069633 -
scater
tutorials with large, open datasets: https://github.com/davismcc/scater_tutorials_open_data -
Hemberg Lab scRNA-seq course: http://hemberg-lab.github.io/scRNA.seq.course/
-
F1000 Research "A step-by-step workflow for low-level analysis of single-cell RNA-seq data": http://f1000research.com/articles/5-2122/v1
-
conquer: a repository for processed, QC'd single-cell datasets. http://imlspenticton.uzh.ch:3838/conquer/
-
(Long!) list of single-cell tools and software: https://github.com/seandavi/awesome-single-cell