https://github.com/tgbrooks/ukbb

UK Biobank Temperature Biorhythm Anlaysis
https://github.com/tgbrooks/ukbb

Last synced: 4 months ago
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UK Biobank Temperature Biorhythm Anlaysis

• Host: GitHub
• URL: https://github.com/tgbrooks/ukbb
• Owner: tgbrooks
• License: mit
• Created: 2019-07-25T18:31:53.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-10-06T20:57:40.000Z (9 months ago)
• Last Synced: 2023-10-06T21:36:25.518Z (9 months ago)
• Language: Jupyter Notebook
• Homepage: http://bioinf.itmat.upenn.edu/biorhythm_atlas/
• Size: 11.9 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-uk-biobank - snake-ukb-accel

README

        
# UK Biobank Actigraphy Anlaysis - Biorhythm Atlas

A study associating actigraphy (wrist worn accelerometry and associated sensors) data to disease phenotypes in the UK Biobank.

This repository contains a Snakemake pipeline to process and aggregate the actigraphy data in the UK Biobank.

It then contains scripts (longitudinal_analysis.py) to generate figures and results tables for our [Temperature Biorhythm Atlas](http://bioinf.itmat.upenn.edu/biorhythm_atlas/) phenome-wide scan of predictive power of temperature rhythms for disease diagnoses.

Temperature rhythms were assessed from the included temperature sensor embedded in the actigraphy device (an Axivity AX3).

These temperature rhythms capture the expected phenotype of wrist temperature in the extremities (higher at night, with an approximately 6 degrees C difference from peak to trough).

A cosinor fit was performed to determine the amplitude (difference from peak to mid-level of the cosine fit) of the amplitude. 

Phenotypes were associated via the [PheCODE](https://phewascatalog.org/phecodes) map, which maps ICD9 and ICD10 codes to phenotypes.

In-patient hospital records were used to determine diagnoses that occured after the actigraphy recording.

A Cox proportional hazards model was then used to determine the associations of the temperature amplitude with these diagnoses.

Individuals with low temperature amplitude were more likely to develop diseases across a wide range of conditions.

## Example Data

The repository contains example data in the `synthetic_data` folder.

This is a small, randomized dataset appropriate to demonstrate the code and particularly statistical and figure/table generation features without any real patient information.

To run the example data run after cloning this repository using python 3.10 and R 4.2:

``` shell

python -m venv venv

source venv/bin/activate #on Windows use instead: venv\Scripts\activate

pip install -r requirements.txt

python longitudinal_analysis.py --cohort 0 --input_directory synthetic_data/ --output_directory synthetic_results/

```

The output files are then in the `synthetic_results` directory.

Installation of dependencies may take a few minutes, and the main computation should take a minute or two.