REVEAL Single Cell
REVEAL Single Cell

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.

“I’ve been using Paradigm4 REVEAL: SingleCell from R,  and I’m both happy and impressed with what is there.”

Principal Computational Biologist, Top 10 Pharma

Relevant Publications and Posters
Rapid Single Cell Evaluation of Human Disease and Disorder Targets Using REVEAL SingleCell™ » FULL ARTICLE