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.
“CellCycleTRACER accounts for cell cycle and volume in mass cytometry data,”
Maria Anna Rapsomaniki et al.
Nature Communications 9(632) 2018.
“Computational biologists find inspiration for machine learning cell cycle sorting method in an unlikely place,”
IBM Research blog, 2018.
Infer trajectories of cell cycle evolution from mass cytometry data.
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IBM Research Scientist