Break through the data wrangling and programming challenges analyzing large-scale, longitudinal single-cell datasets.
- Build a multidimensional understanding of disease biology.
- Scale to higher dimensionalities, more cells, more features, and broader coverage1.
- Readily assess key biological hypotheses for target evaluation, disease progression, and precision medicine.
REVEAL Single Cell delivers:
- Agility: Enable cross-study analysis without having to load each group of studies into a single Seurat object or repeatedly open files. Select cells of interest using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes.
- Scalability: Query across single-cell datasets, each with millions of cells. Algorithms like normalization and subpopulation clustering scale automatically in a parallel distributed computing environment.
- Extensibility: Integrate multi-omic cell measurements. Add new data types. Incorporate an unlimited number of studies and reference datasets. Deploy new R, Python library functions.
- Cost-effectiveness: Reduce TCO, including far lower computing costs with Burst Mode™ (elastic) computing. We are driven to optimize algorithm performance to increase performance and reduce costs, year over year.
- Reproducibility: Version raw, QA’d, and processed data as well as algorithm versions and machine-learning models. Guaranteed data integrity and security in a transaction safe and secure multi-user environment.
- Scientific results: Address immediate needs for SARS-CoV-2 research and broader use in precision medicine applications.
