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This Wiki contains primers for single-cell data analysis with parameters and settings that we have found work best in practice across a variety of datasets. The primers are in the form of Jupyter Notebooks and rely heavily on the use of scanpy. ArchR is used for single-cell ATAC preprocessing.

Environment set up

conda is the recommended system for running these primers

# Create new conda environtment
conda create --name singlecell python==3.8

# Activate conda environment
conda activate singlecell


# Scanpy installation 
conda install seaborn scikit-learn statsmodels numba pytables
conda install -c conda-forge python-igraph leidenalg
pip install scanpy


# Other package instllation
pip install palantir
pip install seaborn
pip install harmonyTS
pip install cellxgene
pip install harmonypy
pip install pyranges
pip install pytabix

# umap version
pip install umap-learn==0.5.1

# jupyter/ipython installation 
conda install -c conda-forge jupyterlab

Optional for coverage plots

• BEDTools In the Hutch environment, bedtools can be installed using ml BEDTools. Alternatively, install from https://bedtools.readthedocs.io/en/latest/

• bedGraphToBigWigh Can be downloaded from the UCSC utility directory here: http://hgdownload.soe.ucsc.edu/admin/exe/. Make sure the binary is in your path.

Primers for analysis of different data types

• Single-cell RNA-seq
• Single-cell ATAC-seq
  • Single-cell ATAC-seq coverages
• Single-cell Multiome