This pipeline has been adapted from https://github.com/gustaveroussy/EaCoN. It leverages the EaCoN R package to conduct preprocessing and normalization, segmentation and copy number estimation from raw Cytoscan HD .CEL files. The EaCoN package also supports copy number estimation from Oncoscan, SNP6 arrays as well as WES data, but these features have not yet been implemented.

This pipeline leverages the snakemake Python package for workflow management. As a result the pipeline and its dependencies are easily installed from this repository, allowing quick setup, configuration and deployment.

For more information on Snakemake, please see: https://snakemake.readthedocs.io/en/stable/.

Dependency management for this pipeline is handled via conda for Python and renv for R. To get started with setup you can install miniconda3 using the instructions available here: https://docs.conda.io/en/latest/miniconda.html. If you do not currently have R installed, you can install it via conda using the command: conda -c conda-forge r-base==3.6. Please note that the EaCoN package has not been updated to work with R >= 4.0.

Alternatively you can install it directly from CRAN as described here: https://cran.r-project.org/.

The first step to deploying an analysis pipeline is to install the various software packages it depends on. We have included the envs/affymetrix.yml and renv.lock files here to easily accomplish this.

All commands should be executed from the top level directory of this repository.

Conda can be used to install all Python and most OS system dependencies using:

conda env create --file envs/affymetrix.yml

This will take some time to run as it gathers and installs the correct package versions. The environent it creates should be called affymetrix.

If it is not automatically activated after installation please run conda activate affymetrix before proceeding to the next step.

The renv package can be used to install all R dependencies (both CRAN and Bioconductor). R version 3.6.3 and renv are included as dependencies in the environment.yml file and should be installed automatically when setting up your conda environment.

To initialize this project with renv run:

Rscript -e 'library(renv); renv::init()'

If you wish to isolate the R dependencies from your Conda environment R libraries, you can use this command instead:

Rscript -e 'library(renv); renv::isolate(); renv::init(bare=TRUE)'

If intialization doesn't trigger dependency installation, you can do so manually using:

Rscript -e 'renv::restore()'

For more information on renv and how it can be used to manage dependencies in your project, please see: https://rstudio.github.io/renv/articles/renv.html.

This pipeline assumes the following directory structure:

.
├── envs
├── metadata
├── procdata
├── rawdata
├── renv
├── results
└── scripts

Please at minimum create the rawdata and metadata directories, as they are assumed to hold the raw Cytoscan HD plate data (.CEL) and the pairs file, respectively. For more information on the correct formatting for your pairs file, please see https://github.com/gustaveroussy/EaCoN. The remaining missing directories will be created automatically as the pipeline runs.

This file hold the relevant pipeline documentation. Here you can specify the paths to all the parameters for your current pipeline use case. Documentation is provided in the config.yaml file on what each field should contain.

snakemake --cores 2 batch_process_rawdata

snakemake --cores 2 segment_processed_data

`snakemake --cores 2