# Gene GEM Correlation Analysis (GGCA)
[](https://github.com/jware-solutions/ggca/actions/workflows/ci.yml)
Computes efficiently the correlation (Pearson, Spearman or Kendall) and the p-value (two-sided) between all the pairs from two datasets. It also supports [CpG Site IDs][cpg-site].
**IMPORTANT**: GGCA is the heart of a platform called Multiomix. On the official website you will be able to use this library in a fast and agile way through a friendly graphical interface (along with many extra features!). Go to https://multiomix.org/ to get started now!
[Python PyPi][pypi-site] | [Rust Crate][crate-site]
## Index
- [Requirements](#requirements)
- [Usage](#usage)
- [Python](#python)
- [Rust](#rust)
- [Contributing](#contributing)
- [Considerations](#considerations)
## Usage
There are a few examples in `examples` folder for both languages.
### Python
1. Install: `pip install ggca`
1. Configure and call the `correlate` method:
```python
import ggca
mrna_file_path = "mrna.csv"
gem_file_path = "mirna.csv"
try:
(result_combinations, evaluated_combinations) = ggca.correlate(
mrna_file_path,
gem_file_path,
correlation_method=ggca.CorrelationMethod.Pearson,
correlation_threshold=0.5,
sort_buf_size=2_000_000,
adjustment_method=ggca.AdjustmentMethod.BenjaminiHochberg,
all_vs_all=True,
gem_contains_cpg=False,
collect_gem_dataset=None,
keep_top_n=2 # Keeps only top 2 elements
)
print(f'Number of resulting combinations: {len(result_combinations)} of {evaluated_combinations} evaluated combinations')
for combination in result_combinations:
print(
combination.gene,
combination.gem,
combination.correlation,
combination.p_value,
combination.adjusted_p_value
)
except ggca.GGCADiffSamplesLength as ex:
print('Raised GGCADiffSamplesLength:', ex)
except ggca.GGCADiffSamples as ex:
print('Raised GGCADiffSamples:', ex)
except ggca.InvalidCorrelationMethod as ex:
print('Raised InvalidCorrelationMethod:', ex)
except ggca.InvalidAdjustmentMethod as ex:
print('Raised InvalidAdjustmentMethod:', ex)
except ggca.GGCAError as ex:
print('Raised GGCAError:', ex)
```
### Rust
1. Add crate to `Cargo.toml`: `ggca = { version = "1.0.1", default-features = false }`
1. Create an analysis and run it:
```rust
use ggca::adjustment::AdjustmentMethod;
use ggca::analysis::Analysis;
use ggca::correlation::CorrelationMethod;
// File's paths
let df1_path = "mrna.csv";
let df2_path = "mirna.csv";
// Some parameters
let gem_contains_cpg = false;
let is_all_vs_all = true;
let keep_top_n = Some(10); // Keeps the top 10 of correlation (sorting by abs values)
let collect_gem_dataset = None; // Better performance. Keep small GEM files in memory
let analysis = Analysis::new_from_files(df1_path.to_string(), df2_path.to_string(), false);
let (result, number_of_elements_evaluated) = analysis.compute(
CorrelationMethod::Pearson,
0.7,
2_000_000,
AdjustmentMethod::BenjaminiHochberg,
is_all_vs_all,
collect_gem_dataset,
keep_top_n,
)?;
println!("Number of elements -> {} of {} combinations evaluated", result.len(), number_of_elements_evaluated);
for cor_p_value in result.iter() {
println!("{}", cor_p_value);
}
```
Note that [env_logger][env-logger] crate is used to provide some warning in case some mRNA/GEM combinations produce NaN values (for instance, because the input array has 0 std). In that case, you can add RUST_LOG=warn to your command to produce warnings in the stderr. E.g:
`RUST_LOG=warn cargo test --tests`
or
`RUST_LOG=warn cargo run --example basic`
### Development and contributions
All kind of help is welcome! Feel free o submit an issue or a PR.
- Build for rust: `cargo build [--release]` or run an example in the `examples` folder with `cargo run --example [name of the example]`
- Build and run in Python: run `cargo build [--release]` and follow [the official instructions][pyo3-python-import] to import the compiled library in your Python script.
- Build for Python (uses Maturin) and it's generated by CI [maturin-actions][maturin-actions]
### Tests
All the correlation, p-values and adjusted p-values were taken from [cor.test][r-cor-test] and [p.adjust][r-p-adjust] functions from the R programming language and [statsmodels][statsmodels] package for Python language.
Data in `small_files` folder was retrieved with random sampling from the *Colorectal Adenocarcinoma (TCGA, Nature 2012)* dataset. This dataset can be downloaded from [cBioPortal datasets page][cbioportal-datasets-page] or [this direct link][colorectal-dataset].
All the correlations results were compared directly with R-Multiomics output (old version of [multiomix.org][multiomix] only available for R lang).
### Performance
We use [criterion.rs][criterion] to perform benchmarks. In case you have made a contribution you can check that no regression was added to the project. Just run `cargo bench` before and after your changes to perform a statistical analysis of performance.
## Considerations
If you use any part of our code, or the tool itself is useful for your research, please consider citing:
```
@article{camele2022multiomix,
title={Multiomix: a cloud-based platform to infer cancer genomic and epigenomic events associated with gene expression modulation},
author={Camele, Genaro and Menazzi, Sebastian and Chanfreau, Hern{\'a}n and Marraco, Agustin and Hasperu{\'e}, Waldo and Butti, Matias D and Abba, Martin C},
journal={Bioinformatics},
volume={38},
number={3},
pages={866--868},
year={2022},
publisher={Oxford University Press}
}
```
[pypi-site]: https://pypi.org/project/ggca/
[crate-site]: https://crates.io/crates/ggca
[cpg-site]: https://en.wikipedia.org/wiki/CpG_site
[pyo3-issue]: https://github.com/PyO3/pyo3/issues/1084
[r-cor-test]: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/cor.test
[r-p-adjust]: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/p.adjust
[statsmodels]: https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html
[cbioportal-datasets-page]: https://www.cbioportal.org/datasets
[colorectal-dataset]: https://cbioportal-datahub.s3.amazonaws.com/coadread_tcga_pub.tar.gz
[multiomix]: https://www.multiomix.org
[env-logger]: https://docs.rs/env_logger/latest/env_logger/
[maturin-actions]: https://github.com/PyO3/maturin-action
[criterion]: https://github.com/bheisler/criterion.rs
[pyo3-python-import]: https://pyo3.rs/v0.22.1/building-and-distribution#manual-builds
Raw data
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"description": "# Gene GEM Correlation Analysis (GGCA)\n\n[](https://github.com/jware-solutions/ggca/actions/workflows/ci.yml)\n\nComputes efficiently the correlation (Pearson, Spearman or Kendall) and the p-value (two-sided) between all the pairs from two datasets. It also supports [CpG Site IDs][cpg-site].\n\n**IMPORTANT**: GGCA is the heart of a platform called Multiomix. On the official website you will be able to use this library in a fast and agile way through a friendly graphical interface (along with many extra features!). Go to https://multiomix.org/ to get started now!\n\n[Python PyPi][pypi-site] | [Rust Crate][crate-site]\n\n\n## Index\n\n- [Requirements](#requirements)\n- [Usage](#usage)\n\t- [Python](#python)\n\t- [Rust](#rust)\n- [Contributing](#contributing)\n- [Considerations](#considerations)\n\n\n## Usage\n\nThere are a few examples in `examples` folder for both languages.\n\n\n### Python\n\n1. Install: `pip install ggca`\n1. Configure and call the `correlate` method:\n\n```python\nimport ggca\n\n\nmrna_file_path = \"mrna.csv\"\ngem_file_path = \"mirna.csv\"\n\ntry:\n\t(result_combinations, evaluated_combinations) = ggca.correlate(\n\t\tmrna_file_path,\n\t\tgem_file_path,\n\t\tcorrelation_method=ggca.CorrelationMethod.Pearson,\n\t\tcorrelation_threshold=0.5,\n\t\tsort_buf_size=2_000_000,\n\t\tadjustment_method=ggca.AdjustmentMethod.BenjaminiHochberg,\n\t\tall_vs_all=True,\n\t\tgem_contains_cpg=False,\n\t\tcollect_gem_dataset=None,\n\t\tkeep_top_n=2 # Keeps only top 2 elements\n\t)\n\n\tprint(f'Number of resulting combinations: {len(result_combinations)} of {evaluated_combinations} evaluated combinations')\n\tfor combination in result_combinations:\n\t\tprint(\n\t\t\tcombination.gene,\n\t\t\tcombination.gem,\n\t\t\tcombination.correlation,\n\t\t\tcombination.p_value,\n\t\t\tcombination.adjusted_p_value\n\t\t)\nexcept ggca.GGCADiffSamplesLength as ex:\n\tprint('Raised GGCADiffSamplesLength:', ex)\nexcept ggca.GGCADiffSamples as ex:\n\tprint('Raised GGCADiffSamples:', ex)\nexcept ggca.InvalidCorrelationMethod as ex:\n\tprint('Raised InvalidCorrelationMethod:', ex)\nexcept ggca.InvalidAdjustmentMethod as ex:\n\tprint('Raised InvalidAdjustmentMethod:', ex)\nexcept ggca.GGCAError as ex:\n\tprint('Raised GGCAError:', ex)\n```\n\n\n### Rust\n\n1. Add crate to `Cargo.toml`: `ggca = { version = \"1.0.1\", default-features = false }`\n1. Create an analysis and run it:\n\n```rust\nuse ggca::adjustment::AdjustmentMethod;\nuse ggca::analysis::Analysis;\nuse ggca::correlation::CorrelationMethod;\n\n// File's paths\nlet df1_path = \"mrna.csv\";\nlet df2_path = \"mirna.csv\";\n\n// Some parameters\nlet gem_contains_cpg = false;\nlet is_all_vs_all = true;\nlet keep_top_n = Some(10); // Keeps the top 10 of correlation (sorting by abs values)\nlet collect_gem_dataset = None; // Better performance. Keep small GEM files in memory\n\nlet analysis = Analysis::new_from_files(df1_path.to_string(), df2_path.to_string(), false);\nlet (result, number_of_elements_evaluated) = analysis.compute(\n\tCorrelationMethod::Pearson,\n\t0.7,\n\t2_000_000,\n\tAdjustmentMethod::BenjaminiHochberg,\n\tis_all_vs_all,\n\tcollect_gem_dataset,\n\tkeep_top_n,\n)?;\n\nprintln!(\"Number of elements -> {} of {} combinations evaluated\", result.len(), number_of_elements_evaluated);\n\nfor cor_p_value in result.iter() {\n\tprintln!(\"{}\", cor_p_value);\n}\n```\n\nNote that [env_logger][env-logger] crate is used to provide some warning in case some mRNA/GEM combinations produce NaN values (for instance, because the input array has 0 std). In that case, you can add RUST_LOG=warn to your command to produce warnings in the stderr. E.g:\n\n`RUST_LOG=warn cargo test --tests`\n\nor \n\n`RUST_LOG=warn cargo run --example basic`\n\n\n### Development and contributions\n\nAll kind of help is welcome! Feel free o submit an issue or a PR.\n\n- Build for rust: `cargo build [--release]` or run an example in the `examples` folder with `cargo run --example [name of the example]`\n- Build and run in Python: run `cargo build [--release]` and follow [the official instructions][pyo3-python-import] to import the compiled library in your Python script.\n- Build for Python (uses Maturin) and it's generated by CI [maturin-actions][maturin-actions]\n\n\n### Tests\n\nAll the correlation, p-values and adjusted p-values were taken from [cor.test][r-cor-test] and [p.adjust][r-p-adjust] functions from the R programming language and [statsmodels][statsmodels] package for Python language.\n\nData in `small_files` folder was retrieved with random sampling from the *Colorectal Adenocarcinoma (TCGA, Nature 2012)* dataset. This dataset can be downloaded from [cBioPortal datasets page][cbioportal-datasets-page] or [this direct link][colorectal-dataset].\n\nAll the correlations results were compared directly with R-Multiomics output (old version of [multiomix.org][multiomix] only available for R lang).\n\n\n### Performance\n\nWe use [criterion.rs][criterion] to perform benchmarks. In case you have made a contribution you can check that no regression was added to the project. Just run `cargo bench` before and after your changes to perform a statistical analysis of performance.\n\n\n## Considerations\n\nIf you use any part of our code, or the tool itself is useful for your research, please consider citing:\n\n```\n@article{camele2022multiomix,\n title={Multiomix: a cloud-based platform to infer cancer genomic and epigenomic events associated with gene expression modulation},\n author={Camele, Genaro and Menazzi, Sebastian and Chanfreau, Hern{\\'a}n and Marraco, Agustin and Hasperu{\\'e}, Waldo and Butti, Matias D and Abba, Martin C},\n journal={Bioinformatics},\n volume={38},\n number={3},\n pages={866--868},\n year={2022},\n publisher={Oxford University Press}\n}\n```\n\n\n[pypi-site]: https://pypi.org/project/ggca/\n[crate-site]: https://crates.io/crates/ggca\n[cpg-site]: https://en.wikipedia.org/wiki/CpG_site\n[pyo3-issue]: https://github.com/PyO3/pyo3/issues/1084\n[r-cor-test]: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/cor.test\n[r-p-adjust]: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/p.adjust\n[statsmodels]: https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html\n[cbioportal-datasets-page]: https://www.cbioportal.org/datasets\n[colorectal-dataset]: https://cbioportal-datahub.s3.amazonaws.com/coadread_tcga_pub.tar.gz\n[multiomix]: https://www.multiomix.org\n[env-logger]: https://docs.rs/env_logger/latest/env_logger/\n[maturin-actions]: https://github.com/PyO3/maturin-action\n[criterion]: https://github.com/bheisler/criterion.rs\n[pyo3-python-import]: https://pyo3.rs/v0.22.1/building-and-distribution#manual-builds\n\n",
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