VIB scientists develop best methods for automated flow cytometry analysis

16 July 2016
Flow cytometry has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. VIB scientists from the team of Yvan Saeys (VIB Inflammation Research Center, UGent) have developed novel computational tools to automate the analysis of flow cytometry data. Using their algorithm they were able to obtain the best performance in the FlowCAP IV challenge, an important benchmark in the recent field of flow cytometry bioinformatics.
“Our methods also revealed unexpected cell types that correlate well with progression to AIDS, leading to novel cell subsets potentially important in HIV to AIDS progression.”
 Yvan Saeys
The Flow Cytometry Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. The most tecent challenge takes these goals one step further and combines automated population identification with statistical  methods to predict clinical phenotypes. The methods developed in the Saeys Lab proved to obtain the best results when predicting HIV to AIDS progression.
Van Gassen et al. Cytometry Part A, 2016
Aghaeepour et al. Cytometry Part A, 2016


Yvan Saeys and Sofie Van Gassen