Description
Microscopy experiments have proven to be a powerful means of generating information-rich data for biological applications. From small-scale microscopy experiments to time-lapse movies and high-throughput screens, automatic image analysis is more objective and quantitative and less tedious than visual inspection.
This course will introduce users to the free open-source image analysis program CellProfiler and its companion data exploration program CellProfiler Analyst. We will show how CellProfiler can be used to analyse a variety of types of imaging experiments. We will also briefly discuss the basic principles of supervised machine learning with CellProfiler Analyst in order to score complex and subtle phenotypes.
Trainers
Beth Cimini, Broad Institute
Audience and Prerequisites
- Researchers who want to extract quantitative information from microscopy images
- Basic knowledge of fluorescence microscopy and digital image acquisition assumed.
- No computational expertise is required.
- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
Syllabus, Tools and Resources
During this course you will learn about:
- Basics of image analysis
- Methods of distinguishing objects of interest from the image background
- Methods of separating clusters of touching objects
- Identification of subcellular compartments
- Obtaining measurements from the cell and subcellular compartments
- Basics of machine learning for phenotype identification
- Utilization of pixel classification tools to help in object detection and measurement
Learning Objectives
After this course you should be able to:
- Set up a CellProfiler pipeline for image analysis
- Troubleshoot and optimize a pipeline for a particular data set
- Analyze CellProfiler data with CellProfiler Analyst