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

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