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Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations. Existing approaches to sharing of distributed medical data either provide only limited protection of patients' privacy or sacrifice the accuracy of results. Here, the authors propose a federated analytics system, based on multiparty homomorphic encryption (MHE), to overcome these issues. [ABSTRACT FROM AUTHOR]

Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains. den-SNE and densMAP enhance single-cell transcriptomic data visualization by incorporating density information. [ABSTRACT FROM AUTHOR]

Most sequenced genomes are currently stored in strict access-controlled repositories. Free access to these data could improve the power of genome-wide association studies (GWAS) to identify disease-causing genetic variants and aid the discovery of new drug targets. However, concerns over genetic data privacy may deter individuals from contributing their genomes to scientific studies and could prevent researchers from sharing data with the scientific community. Although cryptographic techniques for secure data analysis exist, none scales to computationally intensive analyses, such as GWAS. Here we describe a protocol for large-scale genome-wide analysis that facilitates quality control and population stratification correction in 9K, 13K, and 23K individuals while maintaining the confidentiality of underlying genotypes and phenotypes. We show the protocol could feasibly scale to a million individuals. This approach may help to make currently restricted data available to the scientific community and could potentially enable secure genome crowdsourcing, allowing individuals to contribute their genomes to a study without compromising their privacy. [ABSTRACT FROM AUTHOR]

The original version of this Article omitted a reference to previous work in 'Blatt, M., Gusev, A., Polyakov, Y. & Goldwasser, S. Secure large-scale genome-wide association studies using homomorphic encryption. The original article can be found online at https://doi.org/10.1038/s41467-021-25972-y. [Extracted from the article]