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توضیحات

Models nonlinear interactions between covariates and phenotypes

ویژگی	مقدار
سیستم عامل	-
نام فایل	deepnull-0.2.2
نام	deepnull
نسخه کتابخانه	0.2.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Google LLC
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/google-health/genomics-research/tree/main/nonlinear-covariate-gwas
آدرس اینترنتی	https://pypi.org/project/deepnull/
مجوز	-

# DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power This repository contains code implementing nonlinear covariate modeling to increase power in genome-wide association studies, as described in "DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power" ([Hormozdiari et al 2021](https://doi.org/10.1101/2021.05.26.445783)). The code is written using Python 3.7 and TensorFlow 2.4. ## Installation Installation is not required to run DeepNull end-to-end; you can just [open `DeepNull_e2e.ipynb` in colab](https://colab.research.google.com/github/Google-Health/genomics-research/blob/main/nonlinear-covariate-gwas/DeepNull_e2e.ipynb) to try it out. To install DeepNull locally, run ```bash pip install --upgrade pip pip install --upgrade deepnull ``` on a machine with Python 3.7+. This installs a CPU-only version, as there are typically few enough covariates that using accelerators does not provide meaningful speedups. Verify that the installation is working properly by executing all tests: ```bash python -m deepnull.config_test python -m deepnull.data_test python -m deepnull.metrics_test python -m deepnull.main_test python -m deepnull.model_test python -m deepnull.train_eval_test ``` ## How to run DeepNull To run locally, there is a single required input file. This file contains the phenotype of interest and covariates used to predict the phenotype, formatted as a *tab-separated* file suitable for GWAS analysis with [PLINK](https://www.cog-genomics.org/plink/2.0/assoc) or [BOLT-LMM](https://alkesgroup.broadinstitute.org/BOLT-LMM/BOLT-LMM_manual.html). Briefly, the file must contain a single header line. The first two columns must be `FID` and `IID`, and all `IID` values must be unique. An example command to train DeepNull to predict the phenotype `pheno` from covariates `age`, `sex`, and `genotyping_array` is the following: ```bash python -m deepnull.main \ --input_tsv=/input/YOUR_PHENOCOVAR_TSV \ --output_tsv=/output/YOUR_OUTPUT_TSV \ --target=pheno \ --covariates="age,sex,genotyping_array" ``` To see all available flags, run ```bash python -m deepnull.main --help 2> /dev/null ``` Of particular note is the `--model_config` flag. DeepNull uses the [ml_collections](https://github.com/google/ml_collections) library to specify all parameters related to the model and training regimen. The supported configuration code is located in [`config.py`](config.py), and parameters can be modified as described in detail in the [`ml_collections README`](https://github.com/google/ml_collections#parameterising-the-get_config-function). As a brief example, to use the DeepNull architecture with the `elu` activation and train with batch size 4096, the above example command would be modified as follows: ```bash python -m deepnull.main \ --input_tsv=/input/ORIGINAL_PHENOCOVAR_TSV \ --output_tsv=/output/PHENOCOVAR_WITH_DEEPNULL_PREDICTION_TSV \ --target=pheno \ --covariates="age,sex,genotyping_array" \ --model_config=/path/to/config.py:deepnull \ --model_config.model_config.mlp_activation=elu \ --model_config.training_config.batch_size=4096 ``` where `/path/to/config.py` provides the path to [`config.py`](config.py) on your machine. ## Incorporating DeepNull into a GWAS analysis The above section, "How to run DeepNull", shows that the DeepNull software adds a single column to a phenotype+covariate file of interest that represents a nonlinear prediction of the target phenotype of interest. To incorporate this into a GWAS analysis, the single additional covariate should be **added** as an additional covariate. A concrete example with `BOLT-LMM`, using the same file, phenotype `pheno`, and covariates `age`, `sex`, `genotyping_array` as above, is shown below: ### Original example GWAS command ```bash # N.B. Data loading flags are omitted for brevity. bolt \ --phenoFile /input/ORIGINAL_PHENOCOVAR_TSV \ --covarFile /input/ORIGINAL_PHENOCOVAR_TSV \ --qCovarCol age \ --qCovarCol sex \ --qCovarCol genotyping_array \ --phenoCol pheno ``` After running DeepNull on the `/input/ORIGINAL_PHENOCOVAR_TSV` to create the new TSV `/output/PHENOCOVAR_WITH_DEEPNULL_PREDICTION_TSV` that includes the column `pheno_deepnull`, the updated command is given below: ### Updated GWAS command to incorporate DeepNull ```bash # N.B. Data loading flags are omitted for brevity. # Note the addition of the single `--qCovarCol pheno_deepnull` line. bolt \ --phenoFile /output/PHENOCOVAR_WITH_DEEPNULL_PREDICTION_TSV \ --covarFile /output/PHENOCOVAR_WITH_DEEPNULL_PREDICTION_TSV \ --qCovarCol age \ --qCovarCol sex \ --qCovarCol genotyping_array \ --qCovarCol pheno_deepnull \ --phenoCol pheno ``` ## Data Datasets used to reproduce the results from the above publication are available to researchers with approved access to the [UK Biobank](https://www.ukbiobank.ac.uk/). NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis. This is not an officially supported Google product.

نیازمندی

مقدار	نام
>=0.36	wheel
>=0.12	absl-py
>=0.1	ml-collections
>=1.19	numpy
>=1.1	pandas
>=2.4.1	tensorflow
>=0.12	tensorflow-probability
>=1.4	xgboost

زبان مورد نیاز

مقدار	نام
>=3.7, <4	Python

نحوه نصب

نصب پکیج whl deepnull-0.2.2:

pip install deepnull-0.2.2.whl

نصب پکیج tar.gz deepnull-0.2.2:

pip install deepnull-0.2.2.tar.gz