Gene GEM Correlation Analysis (GGCA)

CI

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.

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 | Rust Crate

Index

Usage

There are a few examples in examples folder for both languages.

Python

  1. Install: pip install ggca
  2. Configure and call the correlate method:
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 }
  2. Create an analysis and run it:
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 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 to import the compiled library in your Python script.
  • Build for Python (uses Maturin) and it's generated by CI maturin-actions

Tests

All the correlation, p-values and adjusted p-values were taken from cor.test and p.adjust functions from the R programming language and 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 or this direct link.

All the correlations results were compared directly with R-Multiomics output (old version of multiomix.org only available for R lang).

Performance

We use criterion.rs 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}
}

Project links

Data verified by PyPI on Sep 12, 2024
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for jware from gravatar.com jware

Credits

Author: JWare Solutions <jware.organization@gmail.com>, Sergio Leandro Calderón <bs.sergiocalderon@gmail.com>

GitHub Statistics

Data verified by PyPI on Sep 12, 2024
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.6

Tags

mRNA expression modulation correlation p-value

Classifiers

Report project as malware

Metadata

Project links

Data verified by PyPI on Sep 12, 2024
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for jware from gravatar.com jware

Credits

Author: JWare Solutions <jware.organization@gmail.com>, Sergio Leandro Calderón <bs.sergiocalderon@gmail.com>

GitHub Statistics

Data verified by PyPI on Sep 12, 2024
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.6

Tags

mRNA expression modulation correlation p-value

Classifiers

Report project as malware

Release files for ggca 1.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ggca 1.0.1
File Size Uploaded
ggca-1.0.1.tar.gz 2.2 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for ggca 1.0.1
File
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ggca-1.0.1-pp310-pypy310_pp73-musllinux_1_2_i686.whl 1.2 MB PyPy 3.10 PyPy 3.10 7.3 Linux musl 1.2+ x86-32 Details
ggca-1.0.1-pp310-pypy310_pp73-musllinux_1_2_armv7l.whl 1.3 MB PyPy 3.10 PyPy 3.10 7.3 Linux musl 1.2+ ARMv7l Details
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ggca-1.0.1-cp38-none-win_amd64.whl 837.1 kB CPython 3.8 none Windows x86-64 Details
ggca-1.0.1-cp38-none-win32.whl 748.9 kB CPython 3.8 none Windows x86-32 Details
ggca-1.0.1-cp38-cp38-musllinux_1_2_x86_64.whl 1.2 MB CPython 3.8 CPython 3.8 Linux musl 1.2+ x86-64 Details
ggca-1.0.1-cp38-cp38-musllinux_1_2_i686.whl 1.2 MB CPython 3.8 CPython 3.8 Linux musl 1.2+ x86-32 Details
ggca-1.0.1-cp38-cp38-musllinux_1_2_armv7l.whl 1.3 MB CPython 3.8 CPython 3.8 Linux musl 1.2+ ARMv7l Details
ggca-1.0.1-cp38-cp38-musllinux_1_2_aarch64.whl 1.2 MB CPython 3.8 CPython 3.8 Linux musl 1.2+ ARM64 Details
ggca-1.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1.1 MB CPython 3.8 CPython 3.8 Linux glibc 2.17+ x86-64 Details
ggca-1.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl 1.2 MB CPython 3.8 CPython 3.8 Linux glibc 2.17+ IBM System/390x Details
ggca-1.0.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl 1.1 MB CPython 3.8 CPython 3.8 Linux glibc 2.17+ PowerPC 64-le Details
ggca-1.0.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl 1.1 MB CPython 3.8 CPython 3.8 Linux glibc 2.17+ x86-32 Details
ggca-1.0.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl 1.0 MB CPython 3.8 CPython 3.8 Linux glibc 2.17+ ARMv7l Details
ggca-1.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1.0 MB CPython 3.8 CPython 3.8 Linux glibc 2.17+ ARM64 Details
ggca-1.0.1-cp37-none-win_amd64.whl 837.2 kB CPython 3.7 none Windows x86-64 Details
ggca-1.0.1-cp37-none-win32.whl 748.9 kB CPython 3.7 none Windows x86-32 Details
ggca-1.0.1-cp37-cp37m-musllinux_1_2_x86_64.whl 1.2 MB CPython 3.7 CPython 3.7 pymalloc Linux musl 1.2+ x86-64 Details
ggca-1.0.1-cp37-cp37m-musllinux_1_2_i686.whl 1.2 MB CPython 3.7 CPython 3.7 pymalloc Linux musl 1.2+ x86-32 Details
ggca-1.0.1-cp37-cp37m-musllinux_1_2_armv7l.whl 1.3 MB CPython 3.7 CPython 3.7 pymalloc Linux musl 1.2+ ARMv7l Details
ggca-1.0.1-cp37-cp37m-musllinux_1_2_aarch64.whl 1.2 MB CPython 3.7 CPython 3.7 pymalloc Linux musl 1.2+ ARM64 Details
ggca-1.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1.1 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64 Details
ggca-1.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl 1.2 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ IBM System/390x Details
ggca-1.0.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl 1.1 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ PowerPC 64-le Details
ggca-1.0.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl 1.1 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-32 Details
ggca-1.0.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl 1.0 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ ARMv7l Details
ggca-1.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1.0 MB CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ ARM64 Details

Total release size: 127.7 MB

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Release files / ggca-1.0.1-cp39-none-win_amd64.whl

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Release history Release notifications | RSS feed

This release

1.0.1 This release

115 release files

1.0.0

115 release files

0.4.1

6 release files

0.4.0

6 release files

Yanked

0.3.0

4 release files

Yanked reason: This version contains bugs in the computation of the adjusted p-value.

Yanked

0.1.11

4 release files

Yanked reason: It's slow

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