SLAS2017 Short Courses
Multi parametric Analysis of High Content Screening Data (Laptop Required)
We offer a hands-on course for analyzing multiparametric data typically extracted from high content images using the open source platform KNIME. The course will show how to transform complex datasets into biological insights using advanced data mining techniques without programming.
Who Should Attend:
- Scientists generating multiparametric data wishing to analyze the data but having no or limited programming experience.
- The course will discuss advanced aspects of data mining and participants should already have experience in obtaining multi parametric data.
- The course is natural continuation of SLAS' Digital Image Processing course and will specifically analyse data from images of the course.
- Participants will need to be equipped with a laptop computer and will receive instructions how to install the required software. No programming skills will be necessary in this course.
How You Will Benefit From This Course:
- Participants will learn how to use KNIME to analyse their data.
- Thanks to the open source nature of the software used, participants will be able to directly apply what they have learned in their own laboratory.
- Participants will have an overview of issues and solutions at all stages of the analysis of multiparametric data.
- Participants will join a growing community of data miners using KNIME
- Data inspection and annotation: completeness, data type, annotation and inspection of biological controls. This section will make participants familiar with the software (data input, data manipulation and first plotting tools).
- Parameter selection: High Content Analysis is complicated because of the plethora of parameters that can be extracted from images. Strategies for parameter choosing will be discussed and applied.
- Clustering, machine learning: multiparametric analysis offers the possibility to classify phenotypes by similarity indicating possible common mechanisms of action.
- Hit calling: the aim of all screens is to identify active conditions from inactive ones, the course will discuss various ways of identifying hits in multiparametric space.
- Others: depending on interest and time several other common techniques used for plotting or analyzing multiparametric data will be presented such as population analysis, dimension reduction strategies or dose-response curves.
Marc Bickle obtained his PhD at the Biozentrum in Basel, Switzerland, studying the immunosuppressive drug Rapamycin using yeast. He then went to the LMB in Cambridge, UK, to study behavior in C. elegans. He then participated in the creation of Aptanomics, a drug discovery Biotech in Lyon, France. He is currently heading the High Throughput Technology Development Studio at the MPG-MPI in Dresden, Germany, developing RNAi and chemical high content screens.
Antje Janosch works as research assistant at the Technology Development Studio (TDS) of the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany.
Her work mainly focusses on image and data analysis as well as on software development (KNIME nodes) to facilitate internal data analysis flows.
As computer scientist she supports the work of the TDS with her expertise since 12 years.