16-11-2012, 04:14 PM
The qPCR data statistical analysis
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INTRODUCTION
Since the invention of real-time PCR (qPCR), thousands of
high-impact studies have been conducted and published using
qPCR technique (Heid et al. 1996; Higuchi et al. 1993;
VanGuilder, Vrana, and Freeman 2008). Because it is highly
sensitive, qPCR is the preferred method for microarray data
validation (Canales et al. 2006); however the most exciting
applications have been in the discovery of new biomarkers and
in diagnostic prediction (Gillis et al. 2007). Despite the fact
that this technique has been widely used by researchers, there
are several obstacles to analyzing the vast amounts of data
generated.
Before data can be generated and analyzed, an hypothesis
needs to be formed and the experiment designed. The success
of a project depends on fundamental rules in the
implementation of quality controls (review plates, filter
outliers, removal of incorrect samples and flag genes
undetected), selection of the optimal endogenous controls for
normalization and the correct choice of the correct statistical
method for the analysis. In this document we describe some of
the crucial steps in qPCR data analysis and illustrate statistical
notions with a concrete example using the RealTime StatMiner
software.
FOLD CHANGE IN QPCR
In every well, the qPCR experiment measures the
expression intensity of a certain gene from a sample under
specific biological conditions. This measurement is expressed
in Cycles to Threshold (Ct) of PCR, a relative value that
represents the cycle number at which the amount of amplified
DNA reaches the threshold level. Because of the technical
variability between experiments the Ct needs to be normalized
(see Box 2). Differential expression is done gene by gene by
comparing the normalized Ct values (MCt) of all the biological
replicates between two groups of samples (two biological
conditions).
STATISTICAL SIGNIFICANCE
Having a positive fold change suggests that a certain
miRNA is upregulated but is this extensible to any other mice?
In other words, is the differential expression of this miRNA
statistically significant or was the result achieved by chance?
The statistical test calculates the p-value of every detector
compared in the analysis. According to the gold standard in
statistics, a p-value lower than 0.05 is considered significant
(Fisher 1925), although some authors set the cut-off at 0.01.
Statistics are widely utilized in most of the works published;
even so it is unclear to some qPCR users how to apply these
methods.
PARAMETRIC OR NON-PARAMETRIC TEST
A statistical test can be parametric or non-parametric. To
know which of the two types of tests to choose one question
needs to be answered: does the Ct value of every detector in
the project follow a “normal” distribution? In other words,
would the distribution of the Ct values for a single detector
results in a histogram similar to the plot A in Figure 5 if the
experiment was done with an large number of mice?