What TRUST offers
Accelerate your research with TRUST
TRUST is a data intermediary, which aims to facilitate secure data linkages for health analytics. As such, TRUST makes available the datasets based on approved data requests. Depending on the request and needs, data could include population-related datasets (e.g. demographic data), lifestyle (e.g. wearable data), chronic disease screening data, clinical data (e.g. diagnosis data, medication data, and healthcare financing data). In addition, TRUST is also seeking to facilitate access to strategic population and disease research datasets (e.g. genomic data, phenotypic data). We welcome feedback on datasets that can be beneficial to TRUST users.
Note that certain more sensitive datasets can only be accessed via a Micro-Access Laboratory (MAL) that is certified by MOH.
Anonymised Real-world Datasets
Population datasets (e.g. socio-economic, birth and death data)
Clinical data (e.g. diagnosis data, medication data, laboratory results, healthcare financing data and radiology data)
Lifestyle (data from wearables)
Chronic disease screening data
Anonymised Strategic Research Datasets
Genomic data
Longitudinal population cohorts
Longitudinal disease cohorts
TRUST Features
Operating Environment: Windows 10 Desktop.
On-demand Jupyter notebook interfaces and AWS S3 storage.
Provide a range of compute resources via notebook interface, including multicore virtual CPUs and Spark cluster.
Analytical Tools Available
Python, R, PySpark and SparkR. notebook kernels.
Hail Genomic data exploration and analysis framework (e.g. GWAS, gender prediction).
Python, R and Spark libraries for data science (e.g. Scikit-learn, Carat, pyspark. sql).
OMOP-ATLAS to enable UI based clinical data exploration.
Scale of Analyses That Can Be Supported
Analysis requiring average compute resource
e.g. Variant re-classification of hereditary diseases
Data: Longitudinal population cohort genomic and clinical data (e.g. diagnosis, lab tests)
Analysis: Data filtering, aggregation, statistical analyses, clustering
Analytical insights generated: Known and novel clinical association with population specific disease variants, distributions and visualisation
Analysis requiring higher compute resource
e.g. GWAS analysis
Data: Longitudinal population cohort genomic and traits data
Analysis: Running GWAS
Analytical insights generated: Associated genetic factors, significances and visualisation