https://github.com/idinsmore1/polars-mas

Polars-backed library for fast multiple testing
https://github.com/idinsmore1/polars-mas

Last synced: about 1 month ago
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Polars-backed library for fast multiple testing

• Host: GitHub
• URL: https://github.com/idinsmore1/polars-mas
• Owner: idinsmore1
• Created: 2024-10-10T19:20:09.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-03-20T15:16:54.000Z (about 1 month ago)
• Last Synced: 2025-03-20T15:42:25.392Z (about 1 month ago)
• Language: Python
• Size: 253 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• trackawesomelist - polars-mas (⭐0) - A CLI tool and python library meant to perform large scale multiple association tests, primarily seen in academic research by [@idinsmore1](https://github.com/idinsmore1). (Recently Updated / [Mar 21, 2025](/content/2025/03/21/README.md))
• awesome-polars - polars-mas - A CLI tool and python library meant to perform large scale multiple association tests, primarily seen in academic research by [@idinsmore1](https://github.com/idinsmore1). (Tools build with Polars / Ruby)

README

        
# Polars-MAS: Multiple Association Studies

`polars-mas` is a python library and CLI tool meant to perform large scale multiple association tests, primarily seen in academic research. Currently this tool only supports Firth's logistic regression. Will run as a stand in replacement for PheWAS R package analysis, especially for Phecodes. `polars-mas` is built to leverage the speed and memory efficiency of the `polars` dataframe library and it's interoperability with the `sklearn` and `statsmodels` libraries.  

## Installation

```python

pip install polars-mas

```

## Running the CLI

```text

polars-mas --help

Polars-MAS: A Python package for multiple association analysis.

options:

  -h, --help            show this help message and exit

  -i INPUT, --input INPUT

                        Input file path.

  -o OUTPUT, --output OUTPUT

                        Output file prefix. Will be suffixed with '{predictor}.csv'.

  -p PREDICTORS [PREDICTORS ...], --predictors PREDICTORS [PREDICTORS ...]

                        Predictor column names. These will be tested independently

  -s SEPARATOR, --separator SEPARATOR

                        Column separator. Default is ","

  -d DEPENDENTS [DEPENDENTS ...], --dependents DEPENDENTS [DEPENDENTS ...]

                        Dependent variable column names.

  -di DEPENDENTS_INDICES, --dependents-indices DEPENDENTS_INDICES

                        Dependent variable column indicies. Ignored if --dependents is used. Accepts comma separated list of

                        indices/indicies ranges. E.g. 2, 2-5, 2-, 2,3 , 2,5-8, 2,8- are all valid. Range follows python

                        slicing conventions - includes start, excludes end.

  -c COVARIATES [COVARIATES ...], --covariates COVARIATES [COVARIATES ...]

                        Covariate column names.

  -ci COVARIATES_INDICIES, --covariates-indicies COVARIATES_INDICIES

                        Covariate column indicies. Ignored if --covariates is used. Accepts comma separated list of

                        indices/indicies ranges. E.g. 2, 2-5, 2-, 2,3 , 2,5-8, 2,8- are all valid. Range follows python

                        slicing conventions - includes start, excludes end.

  -cc CATEGORICAL_COVARIATES [CATEGORICAL_COVARIATES ...], --categorical-covariates CATEGORICAL_COVARIATES [CATEGORICAL_COVARIATES ...]

                        Categorical covariate column names.

  -nv NULL_VALUES [NULL_VALUES ...], --null-values NULL_VALUES [NULL_VALUES ...]

                        List of values to be treated as missing values. Default is None (normal polars option).

  -qt, --quantitative   Dependent variables are quantitative traits.

  -mi {drop,forward,backward,min,max,mean,zero,one}, --missing {drop,forward,backward,min,max,mean,zero,one}

                        Method to handle missing values in covariates and predictor variables. If not specified, rows with

                        missing values in the predictor and covariate columns will be dropped.

  -t {standard,min-max}, --transform {standard,min-max}

                        Transform continuous covariates/predictor variables. Default is no transformation.

  -mc MIN_CASES, --min-cases MIN_CASES

                        Minimum number of cases for each dependent variable. Only applied when not --quantitative. Default is

                        20.

  -m {firth,linear}, --model {firth,linear}

                        Type of model to fit. Default is firth logistic regression.

  --phewas              Input data uses Phecodes for dependent variables.

  --phewas-sex-col PHEWAS_SEX_COL

                        Sex covariate column name for PheWAS analysis. Default = 'sex'. Must be coded as male = 0 and female

                        = 1.

  -th THREADS, --threads THREADS

                        Number of threads for numpy and sklearn to use within each worker.

  -n NUM_WORKERS, --num-workers NUM_WORKERS

                        Number of workers for parallel processing and threads available to Polars. Default is number of CPUs.

  -v, --verbose         have more verbose logging

```

If you have an R environment with the `PheWAS` package installed, you can run the `src/tests/example_data/generate_examples.R` script to create dummy data for this repository. 

**NOTE ON THREADS AND WORKERS**: The total number of threads used by `polars-mas` is the number of workers (`-n`) multiplied by the number of threads (`-th`). So if you have 4 workers with 8 threads, `polars-mas` will use 32 threads on your machine.