Statistical Elements of Real-Time qPCR

Why Should You Attend:

• If you work with gene expression data, you should attend.
• Often researchers run the tests and collect data, then are not sure of the best way to test the data for differences and interactions between groups, targets and references, and/or calibrator and test samples.
• Also, there is often a need to control for different researchers or samples in a statistical analysis. Learn how to add and interpret additional factors you may need into your statistical model design.
• Concrete information on sample size, data structure, and interpretation of analysis findings will be presented in examples and Elaine will make some time at the end of the presentation to answer specific questions from the audience.
• Some knowledge of correlation and/or simple linear regression is desired.
• Real-time quantitative PCR (qPCR) includes a set of computations to find counts and fold-differences in gene expression data. qPCR is thus an important aspect in many biomedical fields, when a researcher wants to know the answers to the questions of (1) How many copies in the expression? (absolute quantification) or (2) What is the fold-difference between gene expressions (relative quantification).
• The literature on statistical testing of qPCR data is a bit ambiguous, and there are numerous tests that can be used depending on the question you are asking.

In this webinar, Elaine Eisenbeisz will show you how to use data to estimate a standard curve, how to perform computations for absolute and relative quantification. Also, you will learn a few decision-making criteria and statistical tests that can be used with qPCR data.

Learning Objectives:

• Gain an understanding of the basic principles of qPCR analysis
• Learn how to design a standard curve
• Learn how to compute qPCR data in absolute quantification.
• Learn how to compute qPCR data with three relative quantification techniques.
• Learn about statistical testing and decision making using qPCR data

Areas Covered in the Webinar:

• qPCR What is it?
• Designing a standard curve
• Basics of qPCR analysis
• Absolute quantification methods
• Relative quantification methods
• Normalization against a reference unit mass
• Normalization against a reference gene
• Livak or ∆∆CT method
• ∆CT method using a reference gene
• Pfaffl method
• Statistical assumptions and tests for investigating qPCR data.

Who Will Benefit:

• Study Investigators
• Laboratory personnel
• Data managers
• Data processors
• Statisticians
• Clinical Research Associates
• Clinical Project Managers/Leaders
• Study Sponsors
• Professionals in pharmaceutical, medical device, clinical and biotechnology research who work with gene expression.
• Process and quality control personnel who work with gene expression.
• Other staff in the above fields who work with gene expression data collection and analysis
• Compliance auditors and regulatory professionals who require a knowledge qPCR techniques and analyses for assessment of study protocols and reports

Elaine Eisenbeisz
Owner, Omega Statistics

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California.

Elaine earned her B.S. in Statistics at UC Riverside and received her Master’s Certification in Applied Statistics from Texas A&M.

Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology and analyzes data for numerous studies in the clinical, biotech, and health care fields. Elaine has also works as a contract statistician with private researchers and biotech start-ups as well as with larger companies such as Allergan, Nutrisystem and Rio Tinto Minerals. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals.

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