GenRisk
GenRisk is a package that implements different gene-based scoring schemes to analyze and find significant genes within a phenotype in a population
Citation
Rana Aldisi, Emadeldin Hassanin, Sugirthan Sivalingam, Andreas Buness, Hannah Klinkhammer, Andreas Mayr, Holger Fröhlich, Peter Krawitz, Carlo Maj, GenRisk: a tool for comprehensive genetic risk modeling, Bioinformatics, Volume 38, Issue 9, 1 May 2022, Pages 2651–2653, https://doi.org/10.1093/bioinformatics/btac152
Requirements
- plink >= 1.9 https://www.cog-genomics.org/plink/
- R version >= 3.6.3
Installation
Option 1: The latest release of GenRisk can be installed on python3+ with:
$ pip install genrisk
Option2: you can also install the package with the latest updates directly from GitHub <https://github.com/AldisiRana/GenRisk>_ with:
$ pip install git+https://github.com/AldisiRana/GenRisk.git
Usage
Score genes
This command calculate the gene-based scores for a given dataset.
It requires plink binary files, and an annotations file that contains all information needed for the score computation.
$ genrisk score-genes -a ../toy_example/toy_annotations.tsv -b ../toy_example/toy_data.bim -o toy_genes_scores_test_mod.tsv -t toy_vcf_scoring -v SNP -f gnomadAF -g gene -l ALT -d CADD_raw
- For further CLI options and parameters use --help
Calculate p-values
This function calculates the p-values across the genes between two given groups
$ genrisk find-association -s toy_genes_scores.tsv -i info.pheno -t linear -c quan -a fdr_bh -v sex,age,bmi
- For further CLI options and parameters use --help
Visualize
Visualize manhatten plot and qqplot for the data.
$ genrisk visualize -p logit_assoc_binary.tsv -i genes_info_ref.txt --genescol-1 genes
- For further CLI options and parameters use --help
Create model
Create a prediction model (classifier or regressor) with given dataset
$ genrisk create-model -d toy_dataset_feats.tsv -o quan_regression_model -n quan_regression_model --model-type regressor -l quan --normalize
- For further CLI options and parameters use --help
Test model
Evaluate a prediction model with a given dataset.
$ genrisk test-model --model-path regressor_model.pkl --input-file testing_dataset.tsv --model-type regressor
--labels-col target --samples-col IID
- For further CLI options and parameters use --help
Get PRS scores
This command aquires a PGS file (provided by the user or downloaded from pgscatalog) then calculates the PRS scores for dataset. Note: This command is interactive.
$ genrisk get-prs
- For further CLI options and parameters use --help
Get GBRS
Calculate gene-based risk scores for individuals. If users do not have weights for calculation, they can provide a file with the phenotype and weights will be calculated.
$genrisk get-gbrs --scores-file scores_file.tsv --weights-file weights_file.tsv --weights-col zscore --sum
- For further CLI options and parameters use --help
Contact
If you have any questions or problems with the tool or its installation please feel free to create an issue in the repository or contact me via email: aldisi.rana@gmail.com
Metadata
Release files for GenRisk 0.3.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| GenRisk-0.3.2.tar.gz | 41.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| GenRisk-0.3.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 41.9 MB
Release files / GenRisk-0.3.2.tar.gz
| Download URL | GenRisk-0.3.2.tar.gz |
|---|---|
| Size | 41.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / GenRisk-0.3.2-py3-none-any.whl
| Download URL | GenRisk-0.3.2-py3-none-any.whl |
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| Size | 27.3 kB |
| Tags | Python 3 |
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twine/3.8.0 colorama/0.4.4 importlib-metadata/8.4.0 keyring/23.5.0 pkginfo/1.8.2 readme-renderer/34.0 requests-toolbelt/0.9.1 requests/2.32.3 rfc3986/1.5.0 tqdm/4.66.5 urllib3/1.26.5 CPython/3.10.12
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