Validation and Automation of Phenotypic Profiling Across Multiple Cell Lines

Data management, image processing, multivariate statistical analysis, and profiling at both individual cell and aggregated well level are increasingly becoming a bottleneck in HCS analysis.

In this Webinar we will present an autophagy assay across three cancer cell lines to validate and automate a phenotypic HCS analysis workflow by PerkinElmer’s Columbus and Spotfire High Content Profiler. We will start with our instrument-agnostic HCS platform Columbus which stores, manages and analyzes images coming from all common HCS instruments. Even complex analysis sequences can be easily created by using our famous “Building Block” approach. The results of that image analysis can be selected directly within Spotfire High Content Profiler (HCP), our solution for the analysis and visualization of multiparametric HCS data. Spotfire HCP creates different graphics and analysis in an automated workflow starting with QC overviews on single cell and well-level data but also statistical analysis like Principle Component Analysis and unsupervised machine learning algorithm. All these tools help the end-user to validate their data and find interesting Compounds, RNAis or new classes in their data.

Presenter:
Dr. Christian Schueller
Biology Application Specialist at PerkinElmer Informatics

Christian Schueller is part of the PerkinElmer Informatics team in Europe. Before he joint PerkinElmer he was a scientist at the University of Bonn where he was interested in the uptake of pathogenic bacteria by Macrophages. After that time he worked for Leica Microsystems in the field of microscopic imaging and image analysis. He holds a degree in Biology and a Ph.D. in Cellular Biology. Within PerkinElmer he supports all activities regarding our products Columbus, Spotfire with it’s different biological modules and the E-Notebook.
In this webinar, you will learn
  • Use ‘Building Blocks’ to conduct complex analysis sequences
  • Create different graphics and analysis in an automated workflow starting with QC overviews on single cell and well-level data
  • Leverage statistical analysis techniques such as Principle Component Analysis and unsupervised machine learning algorithms
  • How to validate your data and find interesting Compounds, RNAis or new classes in their data.

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