REST 2008
version
2.0.7 released 21. June 2008
The
new standalone software versions REST 2008 was programmed and
designed by Matthew Herrmann, David Chiew, and Birgit Speller working at Corbett
Research (Sydney, Australia)
and Michael W. Pfaffl, Technical
University of Munich, Germany.
REST 2008 builds on
its predecessor REST 2005 with significant
improvements to randomization
algorithms.
This new revision introduces alternative data inputs such as single run
efficiency and amplification
takeoff point,
alleviating the
need to set amplification plot thresholds.
Download =>
Manual REST 2008
Download
=>
REST 2008 software (removed)
Slide show REST2008
Please download newest version REST 2009 =>
http://www.REST.de.com
Abstract
REST 2008 is a
standalone software package for analyzing gene expression using
realtime amplification data. The software addresses issues surrounding
the measurement of uncertainty in expression ratios by introducing
randomization and bootstrapping techniques. New confidence intervals
for expression levels also allow measurement of not only the
statistical significance of deviations but also their likely magnitude,
even in the presence of outliers. Whisker box plots provide a visual
representation of variation for each gene, highlighting potential
issues such as distribution skew. REST 2008 builds on its predecessor REST 2005 with significant
improvements to randomization algorithms.
This new revision introduces alternative data inputs such as single
sample efficiency and amplification takeoff point, alleviating the
need to set amplification plot thresholds.
What's NEW since REST 2005 ?
NEW  RESTRG mode
A new method of input
has been introduced, allowing users to copy and paste results from the
RotorGene software's Comparative Quantitation analysis. This is an
alternative to importing standard curve and CT results. See RESTRG
Mode chapter for more details.
NEW 
WhiskerBox Plots Exportable
WhiskerBox plots can
now be exported by rightclicking on the graph.
NEW 
Improved randomisation
Improvements to the
randomisation algorithms have been made, making confidence intervals
much tighter, and pvalues more accurate. In previous versions the
pairwise fixed reallocation was incorrectly matching the gene of
interest CT with the incorrect reference CT, this issue has been
rectified in REST 2008.
NEW 
Handling of standard curve variation
REST 2008 no longer
takes into account the variation of the standard curve and implements
improvements to the calculation of confidence intervals and pvalues.
In previous versions, the software would randomly pick two points from
the standard curve and calculate an efficiency based on that. However
there is a situation when two points are chosen that lie close to each
other on the standard curve, this can cause a bogus efficiency which
adds unnecessary outliers to the random distribution. We now calculate
the efficiency by determining the line of best fit for the standard
curve, this efficiency is used through the randomisation process.
Why REST
Prior to
REST (Relative Expression Software
Tool,
Pfaffl et al 2002), Relative Quantitation in qRTPCR was a
technique
which allowed the estimation of gene expression. While useful, it did
not
provide statistical
information suitable for comparing groups of treated versus untreated
samples
in a robust fashion. To illustrate with an example, let
us
say we are testing to see if a particular mRNA is responsible for
sending pain
messages.
We split up our patients into two groups: one which will be subject to
pain (such
immersion of the hand in icecold water), and the other, which is our
control group. Following this, we measure the quantities of
gene of interest mRNA in both groups, relative to reference genes. Our
question is:
did the group subject to pain release more gene of interest mRNA than
the other? Prior approaches are insufficient
to answer this question. They may calculate an average expression value
indicating whether a particular subject in one group appeared to
release more or less
gene of interest mRNA than another subject, but
without
any statistical test to determine accuracy. Due to the use of ratios in
gene
expression, it becomes very complex to perform traditional statistical
analysis,
as ratio distributions do not have a
standard
deviation. REST 2005 overcomes these problems by using simple
statistical
randomisation
tests. Such tests can appear counterintuitive and so it is recommended
to read
the discussions on randomisation techniques in the topic Links before
continuing.
Hypothesis Test
The purpose of REST
2008 is to determine whether there is a significant difference between
samples and controls, while taking into account issues of reaction
efficiency and reference gene normalisation. Because the normalisation
and efficiency calculations involve ratios and multiple sources of
error, it would be extremely difficult to devise a traditional
statistical test, and so randomisation techniques are used instead.
The hypothesis test P(H1) indicated in the results table, represents
the probability of the alternate hypothesis that the difference between
sample and control groups is due only to chance. To devise a strong
randomisation test, we use the following randomisation scenario: "If
any perceived variation between samples and controls is due only to
chance, then we could randomly swap values between the two groups and
not see a greater difference than what we see between the labelled
groups."
The hypothesis test performs a large number of random reallocations of
samples and controls between the groups. It then counts the number of
times the relative expression of the randomly assigned group is greater
than the sample data.
Reference Gene Normalisation
REST 2008 allows the researcher to take into consideration multiple
reference genes when determining expression, although it still remains
possible to use a single reference. When estimating a sample's
expression ratio, an intermediate absolute concentration value is
calculated according to the following formula:
concentration =
efficiencyavg(Controls) – avg(Samples)
Errors in calculation
of concentration occur due to linear variation in CT values. Estimates
of concentration use an equation of the form c = A x e^{Ct}
and so vary
exponentially.
This
formula is used to obtain mean estimates of the uncorrected
absolute concentration for each gene. For a single reference gene,
the concentration of the gene of interest is divided by the reference
gene value
to obtain an expression level, as is done in the Two Standard Curve
technique:
expression = goiConcentration
÷ refConcentration
For multiple reference genes, the geometric mean is taken of all
reference gene concentrations, since concentration estimates vary
exponentially (Vandesompele et al.,
2002):
expression = goiConcentration
÷ GEOMEAN (refConc1, refConc2,, …)
Alternatively,
to normalise according to multiple reference genes, a
second approach can be used, to normalise the individual expressions
relative to
each reference gene which represents an alternative approximations of
the true expression value. To take all into account
simultaneously, they are averaged using a geometric mean (since ratios
are being
used):
expression =
GEOMEAN (goiConcentration ÷ refConc1, goiConcentration ÷
refConc2, …)
Since
the mean concentrations of each gene do not change, they can be
calculated at the beginning of the algorithm, and expressed as a
single value, called the "Normalisation factor", equal to their
geometric
mean.
Greater Accuracy for Hypothesis
Tests
The
redevelopment of the REST 2005 software as a standalone application
provides an order of magnitude of increase in
performance. The speed improvements have been used to increase the
number of
randomisation iterations from 2,000 to 50,000, compared to earlier REST
versions (Pfaffl et al.,
NAR 2002),
increasing the accuracy and reproducibility of
hypothesis
tests to a level equivalent to traditional statistical tests.
Expression Level Confidence
Intervals
While
previous REST publications provide a means of determining the mean
output and a P value for the likelihood of up or
downregulation using a hypothesis test, bootstrapping techniques can
be used to
provide 95% confidence intervals for expression ratios, without
normality or symmetrical distribution assumptions. While a hypothesis
test provides a measure of whether there was a statistically
significant
result, the confidence interval provides a range that can be checked
for semantic significance.
For example, drinking cough medicine before driving may increase the
chances
of an accident
by 1x10^6 %. While a statistical test may show the difference to be
significant, it clearly poses no real threat to drivers, when
taking into consideration the average number of accidents a driver has
in their
lifetime.
Efficiency Error Measurement
All
statistical tests in REST 2008 now include correction for variation in
efficiency. If variation in efficiency is low, hypothesis
tests will
produce more conclusive results, and confidence bands for estimated
expression will be smaller. As all statistics are calculated
using randomisation techniques, the approach for measuring standard
curve error
must also be stochastic, and is expressed as a challenge: If we ignore
variation in thestandard curve, the slope (m
value) will be expressed as a constant in all equations. Say, then, we
have a
standard curve of six data points for the gene GAPDH that we use to
estimate its efficiency. If there is no variation in the
standard curve, then we could pick any two points in the curve and
still measure the
same gradient. If, however, there is large variation between the
points, then random selection of points will greatly
vary the efficiency calculated. Using a few data points, we can then
simulate the
random variable representing the efficiency error. The randomised
efficiency value is then included in calculations instead
of the slope of the line of best fit, feeding any variation in
efficiency directly into the relative
quantitation hypothesis tests and confidence intervals.
WhiskerBox Plots
REST
2008
replaces the bar graph visualisation in prior versions with a
statistical whiskerbox plot. Instatistical applications,
whiskerbox plots provide additional information about the skew of
distributions that would not be available simply
by plotting the sample mean. See the link below for general information
about
whiskerbox plots: http://regentsprep.org/Regents/math/data/boxwhisk.htm
References & Links
"Relative Expression Software Tool
(REST) for groupwise comparison and statistical analysis of
relative
expression results in RealTime PCR", (Pfaffl et al, 2002)
"Accurate normalization of
realtime quantitative RTPCR data by geometric averaging of multiple internal control genes"
(Vandersompele et al, 2002)
"Bootstrap Methods and their Application" (A.C. Davidson, D.V. Hinkley
2002), Cambridge University Press (ISBN 0521573912, Cambridge
University Press 2002)
Corbett Research Ptd Ltd:
RotorGene 6000 Series Software User Guide (Australia, Sydney, 2008) (Corbett
Research)
This reference
provides a good introduction to the philosophy of randomised tests:
http://ordination.okstate.edu/permute.htm
This reference provides an online interactive example of the test:
http://www.bioss.ac.uk/smart/unix/mrandt/slides/frames.htm
This reference provides more detailed descriptions on how to carry out
traditional tests, such as determination of confidence intervals and
hypothesis testing using bootstrapping and randomisation:
http://www.uvm.edu/~dhowell/StatPages/Resampling/Resampling.html
A description of WhiskerBox Plots:
http://regentsprep.org/Regents/math/data/boxwhisk.htm
Contact Information
If you have further questions or
comments to improve the software, your
suggestion are always welcome.
Please contact us at this address: rest2008@genequantification.info?subject=REST2008
REST 2008 
Slide show
Page
1: Gene description &
PCR efficiency calculation
Page 2: CP data
import
Page 3:
Result page  normalized relative expression results
Page
4:
Result page  NONnormalized relative expression results
Page 5: Whisker Plot
Page 6: Graph export
Page 7: Help
function
