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README.md

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### Data Preparation
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After setting up the environment, you'll need to download the cancer datasets from the Curated Cancer Cell Atlas (CCCA). These datasets form the foundation for the benchmarking analysis.
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For detailed instructions on downloading and processing the CCCA datasets, see the [CCCA Data Download Guide](ccca.md).
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For detailed instructions on downloading and processing the CCCA datasets, see the [CCCA Data Download Guide](https://boevalab.github.io/CanSig-benchmark/ccca.html).
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Quick start for downloading a test dataset:
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docs/source/getting_started.md

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**Signature Analysis Pipeline:**
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```bash
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snakemake --configfile config/signatures/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s signatures.smk
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snakemake --configfile configs/signatures/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s signatures.smk
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```
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**Integration Analysis Pipeline:**
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```bash
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snakemake --configfile config/integration/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s integration.smk
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snakemake --configfile configs/integration/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s integration.smk
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```
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Replace `<config>` with the specific configuration file name you want to use. The `--nvccli` argument enables NVIDIA container support for GPU-accelerated methods.

docs/source/tutorials/data.md

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snakemake --configfile your_config.yml --sdm apptainer -c <number-of-cores> -s integration.smk
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```
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**For both (complete pipeline):**
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```bash
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snakemake --configfile your_config.yml --sdm apptainer -c <number-of-cores>
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```
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Replace `<number-of-cores>` with the number of CPU cores you want to allocate to the pipeline.
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## Output Structure
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Results will be organized in your specified directory as follows:
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```
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# For -s signatures.smk
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results/your_analysis/
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├── benchmarks/ # Performance benchmarks for each pipeline step
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├── corrs/ # Correlation analysis results
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├── data/ # Processed data files
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├── integration/ # Integrated datasets for each method
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├── logs/ # Pipeline execution logs
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├── metasigs/ # Generated meta-signatures
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├── signatures/ # Generated signatures
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├── overlap/ # overlap score for each signature
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└── scores/ # Evaluation scores and metrics
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# For -s integration.smk
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results/your_analysis/
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├── benchmarks/ # Performance benchmarks for each pipeline step
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├── data/ # Processed data files
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├── integration/ # Integrated datasets for each method
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├── logs/ # Pipeline execution logs
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├── eval/ # scib metrics for the latent spaces
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└── plotting/ # Umaps of the latent representations
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```
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Each directory contains the intermediate and final results for that stage of the pipeline.
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docs/source/tutorials/reproducing.md

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The signature analysis pipeline evaluates different gene signature methods across cancer datasets:
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```bash
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snakemake --configfile config/signatures/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s signatures.smk
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snakemake --configfile configs/signatures/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s signatures.smk
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```
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Available signature configuration files can be found at `configs/signatures`.
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The integration analysis pipeline benchmarks batch integration methods:
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```bash
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snakemake --configfile config/integration/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s integration.smk
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snakemake --configfile configs/integration/<config>.yaml --sdm apptainer --apptainer-args "\\-\\-nvccli" -c <number_of_cores> -s integration.smk
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```
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Available signature configuration files can be found at `configs/integration`.

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