REST 2005
version
1.9.6 released November 2005
version
1.9.9 released December 2005
version 1.9.12 released April 2006
The
new standalone software versions REST 2005 was programmed and
designed by Matthew Herrmann, David Chiew, working at Corbett
Research (Sydney, Australia)
and Michael W. Pfaffl, Technical
University of Munich, Germany.
Download => Manual REST 2005 (
updated in April 2006 )
Download =>
REST 2005 software (link removed)
Please download newest
version REST 2009 =>
http://www.REST.de.com
Abstract
REST 2005 is
a new standalone software tool to estimate up and downregulation for
gene expression studies. The software addresses
issues surrounding the measurement of uncertainty for expression
ratios, by
using randomisation and bootstrapping techniques. By increasing the
number of iterations from 2,000 to
50,000 in this version hypothesis tests achieve a level of consistency
on
par with traditional statistical tests. New
confidence intervals for expression levels also allow scientists to
measure not
only
the statistical significance of deviations, but also their likely
magnitude,
even in the presence of outliers. Graphical
output of the data via a whisker boxplots provide a visual
representation
of
variation for each gene that highlights potential issues such as
distribution
skew.
Why REST 2005 ?
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.
Reference Gene Normalisation
REST 2005 is
more comprehensive than prior techniques, as it takes multiple
reference
genes into consideration when determining expression. When
estimating a sample's expression ratio, an intermediate absolute
concentration
value is calculated according to the following formula:
concentration =
efficiencyavg(Controls) – avg(Samples)
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 2005 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 the standard 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 2005
replaces the bar graph visualisation in prior versions with a
statistical whiskerbox plot. In statistical 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
"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
RotorGene Software User Guide (Corbett Life
Science)
Links
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
Please find
more information in the HELP manual of the REST 2005 software (by
pressing F1) or at
Obtain software updates to REST 2005 here: http://rest.genequantification.info/
If you have further questions or comments to improve the software, your
suggestion are always welcome.
Please contact us at this address: rest2005@genequantification.info?subject=REST2005
Corbett Research: http://www.corbettlifescience.com
Page 2: CP data
import & PCR efficiency calculation
Page 3:
Result page
Page 4: Whisker Plot
Page 5: Graphical output
Page 6 & 7: Help
function & Copyright
