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https://github.com/krassowski/meningitis-integration
Discovering biomarkers for meningitis by multivariate integration of multi-omics and clinical data
https://github.com/krassowski/meningitis-integration
meningitis omics omics-data-integration proteomics transcriptomics
Last synced: about 2 months ago
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Discovering biomarkers for meningitis by multivariate integration of multi-omics and clinical data
- Host: GitHub
- URL: https://github.com/krassowski/meningitis-integration
- Owner: krassowski
- License: mit
- Created: 2019-03-25T14:58:28.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T22:21:13.000Z (over 2 years ago)
- Last Synced: 2024-04-13T15:52:16.209Z (9 months ago)
- Topics: meningitis, omics, omics-data-integration, proteomics, transcriptomics
- Language: Jupyter Notebook
- Homepage:
- Size: 103 MB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Meningitis omics integration with PLS, O2-PLS and LASSO
[](https://travis-ci.org/krassowski/meningitis-integration)
### Scope
Data: Transcriptomics (mRNA, gene-level) and proteomics (SOMAScan) integration + clinical data including survival, meningitis subtype and HIV status.
For the list of analyses, please see the [Research_plan.md](Research_plan.md)
### Code organisation overview
Code is organised into:
- Jupyter Notebooks (`.ipynb`) containing high-level analysis code, table summaries and visualisations.
The notebooks can be viewed in the browser on the GitHub page of this repository.
- Implementation details are stored in:
- Python modules (`.py`)
- R scripts (`.R`)
Most of the notebooks use the Python kernel, but some also contain R code. The R code in Python notebooks is marked by the `%%R` at the beginning of the cell (thanks to [rpy2](https://github.com/rpy2/rpy2)). An exclamation mark indicates a bash command, e.g. `!ls`.
The generic utilities and helper functions are stored in the [helpers](helpers) directory.
### Automation for reproducibility
The order of notebooks execution is described by the workflow rules in the [pipeline.py](pipeline.py) file.
Following the installation with:
```bash
# download the code
git clone [email protected]:krassowski/meningitis-integration.git
# enter the directory (if you use conda/virtualenv, activate it now)
cd meningitis-integration
# install Python and R dependencies
./setup.sh
# download the data
./download.sh
```
execute [nbpipeline](https://github.com/krassowski/nbpipeline) to reproduce all results of our study with:
```bash
# -i will generate an interactive graph featuring reproducibility reports
PYTHONPATH=$(pwd):$PYTHONPATH nbpipeline -i
```
#### The analyses plot
For interactive plot use `-i` (as shown above), for static use `-g`:
```bash
nbpipline pipeline.py -g
```
Append `-n` switch to skip execution of the pipeline and generate minimal plots (without reproducibility reports and code analysis):
```bash
nbpipline pipeline.py -i -n
```
### Version compatibility
Developed and tested with Python 3.7.6, R 3.6.3 and Ubuntu 19.10.
All dependencies are declared in:
- `requirements.txt` for Python,
- `install.R` for R
### About
The code in this repository was written as a part of MRes research project at Imperial College London.