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Cholesteryl Ester Transfer Protein (CETP) as a Drug Target for Cardiovascular Disease

dataset
posted on 04.05.2021, 15:15 by Floriaan Schmidt
Contains data and computer scripts to generate the figures underlying the manuscript: Cholesteryl Ester Transfer Protein (CETP) as a Drug Target for Cardiovascular Disease.

The README.md:
---------------------------------------------------------------------------------------------------------
# Scripts and deposited data

This directory contains R and python scripts along with the data necessary to
generate the results described in the manuscript: 'cholesteryl ester transfer
protein (CETP) as a drug target for cardiovascular disease'.

## Directory content

.
├── README.md
├── scripts
├── figures
├── MR_data
├── TRIAL_data
└── PICKLED_data

The `scripts` directory contains the computer codes necessary to
generate the figures which get saved to the `figures` directory.

The script source data from the `MR_data` and `TRIAL_data` directories.
The `PICKLED_data` directory contains the python pickled files from
which we extracted the .csv files in `MR_data` -- these pickled files
can be ignored.

## Computer scripts

The directory root contains various R (.R) and python (.py) scripts that
will process the data files.

It also contains a `cetp_conda_env.yml` file to conda install the
necessary R and python modules:

```
cd scripts
conda env create -f cetp_conda_env.yml
```

This creates the `cetp` conda environment.

To generate the figures, please run the following `bash` script:

```
# run from the scripts directory
./run_scripts.sh
```

### Input and output

* scripts/MR_heatmap.py
: Takes the MR_data .csv files and returns Figures 2 and 5.
* scripts/MR_metabolites.R
: Takes the MR_data .csv files and returns Figure 4.
* scripts/MR_forestplots.R
: Takes the MR_data .csv files and returns Figure 3.
* scripts/RCT_forestplots.R
: Takes the TRIAL_data .xlsx files and returns Figure 1.


## Contact

Please contact Floriaan Schmidt for any queries: amand.schmidt@ucl.ac.uk .

Funding

BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223

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