Advanced Data Analysis
SATURDAY, 18 MAY, 2013
Course Description
Data analysis is a critical, complex component of flow cytometry experiments. In this course, we demonstrate how data analysis is relevant in nearly every experimental phase. The course begins with a module on experimental design and data acquisition, in which instrument standardization, panel design, and proper controls will be discussed from a data analysis perspective. Next, a module on data visualization will cover population identification (e.g., “gating”), data analysis in the cloud, and troubleshooting based on staining patterns. Following this module, during the lunch break, vendors will be available to demonstrate software packages and answer questions. The afternoon includes sessions on reporting results (fundamental statistics, data aggregation for presentation) and the next generation of R-based tools for automated data analysis. A course packet will be provided with supplementary materials, including review articles and cytometry list discussions that address frequently asked (and complex) questions, like “how many events should be collected?” or “what is the value of isotype controls?” In sum, this course takes a fresh look at the fundamentals of data analysis and introduces cutting-edge tools for the future. Click here to register!
Course Structure
9:30 - 9:45
|
Introduction
Pratip Chattopadhyay, National Institutes of Health, Bethesda, MD, USA |
| 9:45 - 10:45 |
Module 1: Experimental Design and Data Acquisition
Talk 1: Preventing GI/GO (Garbage In/Garbage Out) Syndrome Instrument Standardization and Panel design
Talk 2: Flow Cytometry Controls |
| 10:45 - 11:00 |
Coffee Break |
| 11:00 - 12:30 |
Module 2: Visualizing Data
Talk 1: Visualizing Data and Identifying Populations
Talk 2: Data Analysis in the Cloud
Talk 3: Troubleshooting Based on Staining Patterns |
| 12:30 - 13:15 |
Lunch and Software Show |
| 13:15 - 14:45 |
Module 3: Reporting Results
Talk 1: Fundamental Statistics
Talk 2: Aggregating Data Across Samples (SPICE for Intracellular Cytokines)
Talk 3: Data Presentation Guidelines (MiFlowCyte) |
| 14:45 - 15:45 |
Module 4: Automated Data Analysis
Highlights of R-based Flow Cytometry Packages
From QC to Data Mining for Predictive Subsets |
| 15:45 - 16:00 |
Wrap-up |
Bruce Bagwell
Verity Software House
Greg Finak
Fred Hutchison Cancer Research Center
Jonathan Irish
Vanderbilt University Medical Center
Peng Qiu
MD Anderson Cancer Center
Pratip Chattopadhyay
Vaccine Research Center, NIAID, NIH
Rich Konz
Univeristy of MA Medical School Core Flow Cytometry Lab
Ryan Brinkman
British Columbia Cancer Agency
Steve Perfetto
National Institutes of Health