depmap-nextgen

Contents

• Setup
• Running the code

Setup

Python

We use poetry (version 2.1.3) for python package management. Documentation can be found at https://python-poetry.org/docs, with intructions for installing poetry and using some common commands. A human-readable version of package requirements for the environment used in this study can be found in pyproject.toml.

To recreate the python environment, navigate to the folder containing poetry.lock and pyproject.toml, and run poetry install. Poetry will install all package dependencies, as specified by their exact versions in the .lock file, into an environment which can either be activated for interactive use with poetry shell (documentation) or by prefixing python commands with poetry run. The latter case is used in the run instructions below.

R

The R environment used in Section 3 uses R version 4.4.3, with the following additional packages (and their versions) installed:

• scales_1.4.0
• gplots_3.2.0
• RColorBrewer_1.1_3
• dendextend_1.19.0
• dplyr_1.2.0
• readr-2.2.0

Running the code

Setup and data acquisition:

Clone this repository, and download the contents of the raw data that will be released in the DepMap Portal into the data directory.

The below sections will reproduce the files deposited in processed.

Section 1 (python)

Main Figures 1b-g, 1i, 2a-h, 3a, 4a-f, 5a-b, 5d-h; Extended Figures 1-c, 6f, 10e, 11a-c, 12a-d

To reproduce analytical results for the above figure panels, run the following code (runtime ~20 minutes): poetry run python src/generate.py

Subsequently, run the remaining scripts, in any order, to generate these figure panels (runtime ~5 minutes total):

poetry run python src/achilles_qc.py
poetry run python src/celligner.py
poetry run python src/experimental_validation.py
poetry run python src/expression_addiction.py
poetry run python src/figure_legends.py
poetry run python src/metaprogram_expression.py
poetry run python src/minipool.py
poetry run python src/omics_summaries.py
poetry run python src/onc_tsg_associations.py
poetry run python src/oncoplot.py
poetry run python src/organoid_vs_adherent.py
poetry run python src/paralog_dependencies.py
poetry run python src/pdac_classical_dependencies.py

The generation step will populate the processed directory with .csv files, while the subsequent steps will simultaneously populate the processed directory with .csv files and figures directory with .pdf files.

Section 2 (R)

Main Figures 3b-d; Extended Figure 6 To reproduce analytical results for the above figure panels, run the following R scripts:

Rscript Fig3bc_EDFig6ab_GBM_celligner_transcriptional_analysis.R
Rscript Fig3d_EDFig6c_GBM_GSEA.R

Section 3 (R)

Main Figures 3d, 3f; Extended Figures 5a, 5d, 6b, 8e-g, 11a, 11c, 12a-c, R9a, R13d

To reproduce analytical results for the above figure panels, run the following R scripts:

Rscript EDFig5d_cds2d3d_expression_volcano.R
Rscript EDFig6b_cnsorg2d3d_dependency_volcano.R
Rscript EDFig8e_glial_nextgen_diff_dependency.R
Rscript EDFig8f_cdk6dep_mut_association.R
Rscript EDFig11a_pdacc_associated_dep_wnt3a.R
Rscript Fig3d_glial_mesenchymal_diff_dependency.R

To produce figures, run the following scripts

Rscript EDFig5a_cns2d3d_expression_dendrogram.R
Rscript EDFig8g_cdk6dep_cn_association_glial.R
Rscript EDFig11c_wnt_dependency_mut_association.R
Rscript EDFig12ac_2d3d_onctsg_dependency.R
Rscript Fig3f_cdk6dep_cn_association.R
Rscript FigR9a_depmap_han_2d3d_screens.R
Rscript FigR13d_cdk6dep_cdkn2acn_per_lineage.R