A novel computational method to quantify cell cycle and cell volume variability.


An entire tumor can be composed of thousands of cells, and using bulk approaches to study its composition greatly masks its variability. With single-cell approaches such as mass cytometry and our CellCycleTRACER computational method, we can now pinpoint the proteins with unprecedented resolution down to a single cell.

Currently in beta, CellCycleTRACER is a supervised machine-learning algorithm that classifies and sorts single-cell mass cytometry data according to their cell cycle, which allows us to correct for cell-cycle-state and cell-volume heterogeneity. It is essentially a tool to find the proverbial needle in the haystack.

The algorithm is implemented as a simple and intuitive web application and can be applied to any mass cytometry dataset. We are currently bringing it to the cloud, where scientists throughout the world will be able to upload and analyze their datasets for free.

Schematic of the cell cycle.

Web service

Infer trajectories of cell cycle evolution from mass cytometry data.

Log in or register to use the web app.

Download the instructions here.




Related projects

Quantifying biological heterogeneity from single-cell data.png

Quantifying biological heterogeneity from single-cell data

Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.
  • Machine Learning
  • Healthcare
  • Accelerated Discovery
AI methods for precision therapies

AI for single-cell research

Understanding spatiotemporal heterogeneity across different scales of biological organization.
  • Machine Learning
  • Healthcare
  • Accelerated Discovery