Resources & Software

We develop open-source software to support our own research and the wider community.
The software reflects our research focus and has typically been extensively used in applied research. All code is freely available, most often through GitLab, and where relevant via Conda and PyPI.

Onboarding

The lab has access to CPU and GPU nodes on high-performance computing clusters. Onboarding documentation is provided to describe access and usage procedures for both locations (UCL and AUMC).

The onboarding document also includes tutorials on coding using Bash, Git, as well as set-up of Linux/Unix environments.

Templates and guidelines are provided for scientific manuscripts, data presentation, and visualisations.

GitLab

plot-misc

Plotting archetypes for biomedical research.

This package provides a matplotlib-first Python API to generate highly customisable illustrations for biomedical research.

Included plot types: forest plots, survival plots, annotated heatmaps, bubble charts, calibration plots, volcano plots, and many others.

GitLab Docs Conda PyPI

clinvar-build

Build a SQLite database from publicly available ClinVar XML files.

This package provides fully customisable command-line tools to parse ClinVar XML data into a SQLite database. ClinVar provides curated information on the potential pathogenicity of human genetic variants. Once built, the package also provides command-line tools to query these data and store results in tabular files.

GitLab Docs Conda PyPI

ECGProcess

Process and standardise ECG data.

This Python package provides processing pipelines to extract user-defined metadata, waveforms, and/or median beat data from standard ECG file formats such as XML or DICOM. The extracted data can be written to disk in a range of formats suitable for storage or analysis in machine and deep learning projects.

GitLab Docs

clean-data

Clean and standardise tabular data.

A package to clean, format, and standardise tabular data and strings. Includes functions for statistical pruning, handling time data, and exploring missingness, as well as functions to create supplementary Excel files. The package also includes a baseline module for creating baseline tables, allowing bespoke testing of differences across groups.

GitLab Docs

stats-misc

Collection of statistical functions

A Python module collecting statistical procedures not found in existing libraries such as SciPy or statsmodels. The package includes functions for interval estimation, hypothesis testing, meta-analysis, resampling, and machine learning.

GitLab Docs Conda