# scRNA.seq.course
**Repository Path**: guocheng2020/scRNA.seq.course
## Basic Information
- **Project Name**: scRNA.seq.course
- **Description**: Analysis of single cell RNA-seq data course
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-07
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# About the course
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.
In this course we will discuss some of the questions that can be addressed using scRNA-seq as well as the available computational and statistical methods avialable. The course is taught through the University of Cambridge Bioinformatics training unit, but the material found on these pages is meant to be used for anyone interested in learning about computational analysis of scRNA-seq data. The course is taught twice per year and the material here is updated prior to each event.
The number of computational tools is increasing rapidly and we are doing our best to keep up to date with what is available. One of the main constraints for this course is that we would like to use tools that are implemented in R and that run reasonably fast. Moreover, we will also confess to being somewhat biased towards methods that have been developed either by us or by our friends and colleagues.
## Web page
__[https://scrnaseq-course.cog.sanger.ac.uk/website/index.html](https://scrnaseq-course.cog.sanger.ac.uk/website/index.html)__
## Video
This video was recorded in November 2017, at that time the course contained less chapters than the current version.
__[https://www.youtube.com/embed/IrlNcJwPClQ?list=PLEyKDyF1qdObdFBc3JncwXAnMUHlcd0ap](https://www.youtube.com/embed/IrlNcJwPClQ?list=PLEyKDyF1qdObdFBc3JncwXAnMUHlcd0ap)__
## Registration
Please follow this link and register for the __"Analysis of single cell RNA-seq data"__ course:
http://training.csx.cam.ac.uk/bioinformatics/search
## GitHub
https://github.com/hemberg-lab/scRNA.seq.course
## Docker image
[](https://quay.io/repository/hemberg-group/scrna-seq-course)
The course can be reproduced without any package installation by running the course docker image which contains all the required packages.
### Run the image
Make sure Docker is installed on your system. If not, please follow [these instructions](https://docs.docker.com/engine/installation/). To run the course docker image (use [the latest version](https://quay.io/repository/hemberg-group/scrna-seq-course?tab=tags) of the course instead of v3.13):
```
docker run -p 8888:8888 -e PASSWORD="jupyter" quay.io/hemberg-group/scrna-seq-course:v3.13
```
Then follow the instructions provided, e.g.:
```
To access the notebook, open this file in a browser:
file:///home/jovyan/.local/share/jupyter/runtime/nbserver-6-open.html
Or copy and paste one of these URLs:
http://(a9ee1aad5398 or 127.0.0.1):8888/?token=22debff49d9aae3c50e6b0b5241b47eefb1b8f883fcb7e6d
```
A Jupyter session will be open in a web browser (we recommend Chrome).
#### Windows users
On Windows operating system the IP address of the container can be different from `127.0.0.1` (`localhost`). To find the IP address please run:
```
docker-machine ip default
```
### Download data/other files
Please click on `New -> Terminal`. In the new terminal window please run:
```
./poststart.sh
```
If you want to download data files outside of Docker image you can still use the same `poststart.sh` script but you will need to install [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/install-bundle.html) on your computer.
### RStudio
Now go back to Jupyter browser tab and change word `tree` in the url to `rstudio`. RStudio server will open with all of the course files, software and the data folder available.
## Manual installation
If you are not using a docker image of the course, then to be able to run all code chunks of the course you need to clone or download the [course GitHub repository](https://github.com/hemberg-lab/scRNA.seq.course) and start an R session in the `course_files` folder. You will also need to install all required packages manually.
Alternatively, you can just install packages listed in a chapter of interest.
## License
All of the course material is licensed under GPL-3. Anyone is welcome to go through the material in order to learn about analysis of scRNA-seq data. If you plan to use the material for your own teaching, we would appreciate if you tell us about it in addition to providing a suitable citation.
## Prerequisites
The course is intended for those who have basic familiarity with Unix and the R scripting language.
We will also assume that you are familiar with mapping and analysing bulk RNA-seq data as well as with the commonly available computational tools.
We recommend attending the [Introduction to RNA-seq and ChIP-seq data analysis](http://training.csx.cam.ac.uk/bioinformatics/search) or the [Analysis of high-throughput sequencing data with Bioconductor](http://training.csx.cam.ac.uk/bioinformatics/search) before attending this course.
## Contact
If you have any __comments__, __questions__ or __suggestions__ about the material, please contact Vladimir Kiselev.