Introduction to single cell analysis concepts

This workshop intends to introduce the basic concepts underlying single-cell data generation, processing and analysis. We will introduce the current state-of-the-art technologies for molecular profiling at the single cell level. The goal is to help participants get familiar with existing tools and understand the differences between them.

Workshop slides

You can download the material covered in the workshop from OneDrive.

Installations

There are two options: Docker and Manual. It is highly recommended to setup the Docker image for the workshop, it will download the data and setup all the required packages. If you want to set up everything on your own you can follow the steps listed in the Manual section.

Using Docker

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1. Download Docker Desktop from their website. If you're using Windows 10 you can read this documentation to set it up.

2. Pull the image

docker pull larisamsoto/micm_singlecell

3. Run the container

To enable jupyter lab - this option will not work on Safari

docker run -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -d larisamsoto/micm_singlecell

If you prefer to use Jupyter Notebook

docker run -p 8888:8888 -d larisamsoto/micm_singlecell

4. To open the Jupyter server in a browser (preferably chrome) just copy and paste one of the following URLs:

http://127.0.0.1:8888/lab

http://localhost:8888/lab

Manual

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1. Make sure you have installed Python 3.8.8, Jupyter and R 4.0.3

2. Install the required Python packages using conda

conda install -c bioconda scvelo 
conda install -c bioconda scanpy 

2. Install the required R packages

From CRAN

install.packages(c('BiocManager','remotes','devtools','Seurat','tidyverse','gprofiler2','data.table','patchwork','viridis','ggsci'))

From Bioconductor

BiocManager::install(c('SingleR','scRNAseq','BiocGenerics', 'slingshot','limma','TENxBrainData')))

From Anaconda

conda install -c bu_cnio r-seuratwrappers 
conda install -c bioconda r-velocyto.r 

From Github

devtools::install_github('satijalab/seurat-data')
devtools::install_github(repo = 'mojaveazure/loomR', ref = 'develop')

3. Clone this repository

git clone https://github.com/larisa-msoto/micm_singlecell.git

4. Download the data using this OneDrive link into the folder data.