CellEngine Auto-Gate: Accurate Gating in COVID Study

CellCarta’s poster demonstrates the effectiveness of CellEngine’s automatic gate adjustment tool in cytometry analysis for a COVID-19 longitudinal clinical study. The tool uses a machine-learning algorithm trained on manually gated samples to automatically gate large datasets, significantly reducing analysis time while maintaining accuracy.

The study focused on two markers, CCR5 and perforin, known for high variability, and showed that the tool produced results highly similar to manual gating, with a time reduction of 69.3% for perforin and 88.5% for CCR5

View the full poster:

CellEngine’s automatic gate adjustment tool quickly and accurately recapitulates manual gating: assessment in a COVID longitudinal clinical study

As presented in CYTO2023

Autogating

• Closely correlates with manual gating
• Reduces analysis time by 69% to 89%
• Adjusts gates for thousands of files in seconds
• Full deterministic operation based on a few manually gated files