ShinyDataSHIELD

ShinyDataSHIELD enables federated, non-disclosive analysis of distributed multicohort datasets using the DataSHIELD infrastructure for privacy-preserving statistical and omic investigations.

Key Features:

• Federated non-disclosive analysis: Performs computations across distributed multicohort datasets without sharing individual-level raw data to preserve privacy.
• DataSHIELD integration: Integrates with the DataSHIELD infrastructure to execute remote, privacy-preserving analyses on distributed resources.
• Descriptive statistics: Computes descriptive summary statistics for distributed datasets.
• Visualizations: Generates visual outputs including scatter plots, histograms, heatmaps, and boxplots from distributed summaries.
• Statistical modelling: Supports generalized linear fixed- and mixed-effects models for hypothesis testing and inference.
• Survival analysis: Implements survival analysis using Cox regression on distributed data.
• Genome-wide association studies (GWAS): Provides modules for conducting GWAS across distributed genetic datasets.
• Omic analysis: Supports transcriptomics, epigenomics, and multi-omic integration for distributed omic datasets.

Scientific Applications:

• Multicohort federated studies: Privacy-preserving analysis of multicohort datasets where pooling of individual-level data is not possible.
• High-throughput genomic studies: GWAS and other large-scale genomic analyses across distributed cohorts.
• Transcriptomics and epigenomics research: Analysis and integration of transcriptomic and epigenomic data in a federated setting.
• Biosciences and social sciences: Analyses in domains requiring secure handling of sensitive or restricted data.

Methodology:

Computations are executed via the DataSHIELD framework using federated non-disclosive algorithms to produce descriptive summary statistics and visualizations, fit generalized linear fixed- and mixed-effects models, perform Cox regression survival analyses, conduct GWAS, and support transcriptomics, epigenomics, and multi-omic integration on distributed datasets.