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ICG JKU Linz Lab Talk: 'Exploration of large single-cell data [...]', Thomas Höllt
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Abstract:
Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. In this talk we will discuss several hierarchical approaches to the interactive exploration of large single cell data using a combination clustering and t-Distributed Stochastic Neighborhood Embedding (t-SNE) as well as the recently introduced Hierarchical Stochastic Neighborhood Embedding (HSNE). Based on the application to a study on gastrointestinal disorders we show hat HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed. Finally we will discuss CyteGuide, a tool to guide the exploration of HSNE hierarchies.
About the Speaker:
Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. In this talk we will discuss several hierarchical approaches to the interactive exploration of large single cell data using a combination clustering and t-Distributed Stochastic Neighborhood Embedding (t-SNE) as well as the recently introduced Hierarchical Stochastic Neighborhood Embedding (HSNE). Based on the application to a study on gastrointestinal disorders we show hat HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed. Finally we will discuss CyteGuide, a tool to guide the exploration of HSNE hierarchies.
About the Speaker:
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