Request Whitepaper
This whitepaper is a brief overview of the basis of qPCR data analysis using RealTime StatMiner®.
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Data analysis represents one of the biggest bottlenecks in qPCR experiments and the statistical aspects of the analysis are sometimes considered confusing for the non-expert. In this document we present some of the usual methods used in qPCR data analysis and a practical example using RealTime StatMiner®, the unique software analysis package specialized for qPCR experiments which is compatible with all Applied Biosystems Instruments. RealTime StatMiner uses a simple, step-by-step analysis workflow guide that includes parametric, non-parametric and paired tests for relative quantification of gene expression, as well as 2-way ANOVA for two-factor differential expression analysis. |
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qPCR Data Statistical Analysis
Do you have problems analyzing your RT-qPCR data, find yourself spending a lot of time searching for files, spend hours reformatting and working with Excel worksheets and wonder how reliable are the results?
Read our whitepaper to learn how RealTime StatMiner can help you answer the following questions:
- Do you inspect for technical outliers? How?
- When should a sample be removed from the analysis? Why?
- Do you use GeNorm and Normfinder to select your endogenous controls?
- Do you use multiple endogenous controls?
- When should you use a parametric, non-parametric or paired t-test?
- When working with multiple experimental treatments, do you perform 2-way ANOVA to test for interactions?
- Are your results significant or not?
- Working with miRNA data (panels A and B)?
- How do you deal with undetermined values?
...and more.
Here's how to access to our whitepaper:
Please fill out the form below and you will receive an email with the whitepaper.
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