For well over a decade, scientists have used single-cell omics to better understand biology and disease. By looking at the individual-cell level, researchers can gain visibility into a wide spectrum of cellular states and how they interact with each other. This helps researchers understand gene expressions and identify unique states and rare cell types that may be linked to specific diseases.
Bulk RNA-sequencing approaches typically pool RNA from cells or tissues to analyze in aggregate. Unlike bulk RNA-sequencing, which provides an average of cell expression across a sample, single-cell approaches provide granularity on a cellular level. As a result, single-cell omics provide more precise analysis between what’s happening to individual cells in control and disease samples.
With NVIDIA’s accelerated computing and AI platform for single-cell omics, researchers and developers can:
- Reduce analysis time for processing increasingly large single-cell datasets.
- Accelerate data processing, clustering, dimensionality, reduction, and regression with RAPIDS-single cell.
- Accurately predict gene behavior and disease mechanisms through single-cell foundation models in BioNeMo™.