Code to reproduce Tumour-intrinsic features shape T-cell differentiation through myeloma disease evolution

Single-cell data from Sklavenitis-Pistofidis et al. and extended clinical data from Maura et al. (i.e. paraprotein) are omitted (and must be acquired through direct contact with these authors), this repository will not perfectly recreate figures seen in the manuscript. However, the underlying analysis and plotting code is unchanged, so acquiring and integrating this data will generate manuscript figures.

1. /environments

• Rpackages.html, install correct R package versions.
• .yml files used to construct conda env for analysis:

conda env create --name integration_env --file=integration_env.yml
conda env create --name preprocessing --file=preprocessing.yml
conda env create --name tcrdist3_env --file=tcrdist3_env.yml

2. /integration

• Overview of methods vs directly implementable.
• /environments/integration_env.yml can be used to construct conda environment used for integration.
• raw_data.h5ad will be made available at publication or (for reviewers) through communication with editor.

3. /preprocessing

Generates processed and analysed data for constructing figures using data available on Zenodo.

Run:

# First, edit `run_preprocessing.py` to user python (from above conda env) and R locations
# Then, in bash shell, run within /myeloma-singlecell/preprocessing
python3 run_preprocessing.py

Omitted data:

• Single-cell data from Sklavenitis-Pistofidis et al..
• Extended clinical data from Maura et al..
• Granja et al. CITE-seq data, available here.
• CoMMpass data (requires controlled access)

4. /notebooks & /figures

Generate all figures

# bash shell, within /myeloma-singlecell
Rscript make_figures.R