Published online 22 February 2009Nucleic Acids Research, 2009, Vol. 37, No. 6e45
Amplification efficiency: linking baseline and bias in
the analysis of quantitative PCR data
J. M. Ruijter1,*, C. Ramakers2, W. M. H. Hoogaars1, Y. Karlen3, O. Bakker4,
M. J. B. van den Hoff1and A. F. M. Moorman1
1Heart Failure Research Center, Academic Medical Center, University of Amsterdam, The Netherlands,
2Department of Neuroscience, Faculty of Mental Health, University of Maastricht, The Netherlands,
3Nestec Ltd, PTC Orbe, Switzerland and4Department of Endocrinology and Metabolism, Academic
Medical Center, University of Amsterdam, The Netherlands
Received August 6, 2008; Revised and Accepted January 15, 2009
Despite the central role of quantitative PCR (qPCR)
in the quantification of mRNA transcripts, most
analyses of qPCR data are still delegated to the soft-
ware that comes with the qPCR apparatus. This is
especially true for the handling of the fluorescence
baseline. This article shows that baseline estimation
errors are directly reflected in the observed PCR
efficiency values and are thus propagated exponen-
tially in the estimated starting concentrations as
well as ‘fold-difference’ results. Because of the
unknown origin and kinetics of the baseline fluores-
cence, the fluorescence values monitored in the ini-
tial cycles of the PCR reaction cannot be used to
estimate a useful baseline value. An algorithm that
estimates the baseline by reconstructing the log-
linear phase downward from the early plateau
phase of the PCR reaction was developed and
shown to lead to very reproducible PCR efficiency
values. PCR efficiency values were determined per
sample by fitting a regression line to a subset of
data points in the log-linear phase. The variability,
as well as the bias, in qPCR results was significantly
reduced when the mean of these PCR efficiencies
per amplicon was used in the calculation of an esti-
mate of the starting concentration per sample.
During the last decade, quantitative real-time reverse tran-
scriptase PCR, or qPCR for short, has become the method
of choice for the quantification of mRNA transcripts (1,2).
Despite the large number of papers on qPCR data anal-
ysis, most researchers still delegate this analysis to the
software that comes with their PCR system (3). The main-
stream of qPCR data analysis is based on the direct appli-
cation of the basic equation for PCR amplification (Box 1;
Equation 1), which describes the exponential increase in
observed fluorescence when the PCR reaction is moni-
tored using a fluorescent DNA-binding dye (e.g. SYBR
Green I) (4). Alternative qPCR data analysis methods,
such as those based on nonlinear curve fitting (5–7) will
be considered in a separate section of this article.
The calculation of starting concentrations in qPCR ana-
lysis requires an estimate of the PCR efficiency, the setting
of a fluorescence threshold and the determination of the Ct
value, which is the fractional cycle number that is required
to reach this threshold (8). Originally, qPCR analysis used
a PCR efficiency value that was assumed to be constant (8)
but currently the efficiency is derived from a standard
curve (2,9) or calculated as the mean efficiency per ampli-
con (10–12). Analysis methods that are based on the
PCR efficiency per sample (13–15) were shown to give
highly variable results (10–12,16,17). This high variability
remained a conundrum until it became clear that the
observed PCR efficiency is strongly affected by the applied
baseline estimate (Figure 1A). In the real-time PCR chem-
istry considered in this article, the baseline fluorescence is
due to the fluorescence of unbound fluorochrome (e.g.
SYBR Green I), and to fluorochrome bound to, among
others, double strand cDNA and primers annealing
to nontarget DNA sequences (Figure 1B). Other fluores-
cence sources also contribute to the baseline fluorescence.
Although it was reported that a baseline has to be
subtracted before a valid PCR efficiency value can be
*To whom correspondence should be addressed. Tel: +30 20 5665386; Fax: +30 20 6976177; Email: email@example.com
C. Ramakers, Department of Clinical Chemistry & Hematology, St Elisabeth Hospital, Tilburg, The Netherlands
W. M. H. Hoogaars, Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
? 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
determined (18) and shortcomings in the baseline subtrac-
tion methods in system software have been recognized
(19,20), the need to determine the correct baseline value
has mainly been ignored in the literature. It has been
addressed in some papers (7,21) and then it is mainly dis-
cussed in the context of the fit of the employed analysis
model (5,7). Validation of the baseline estimation relies on
visual inspection of the shape of the resulting dataset
The current study shows how an improper baseline
setting severely affects the estimated PCR efficiencies and
will thus increase the variability as well as the bias in the
reported absolute and relative levels of gene expression.
To solve these issues, an algorithm to estimate the optimal
baseline for each individual sample was developed.
The body of this article deals chronologically with
each of the issues required for qPCR data analysis, thus
aiming at presenting a comprehensive qPCR data analysis
protocol (Figure 1C). The described methods have been
incorporated into the LinRegPCR quantitative PCR data
MATERIALS AND METHODS
Thirty hearts of chicken embryos of 3 days of develop-
ment were isolated and separated into the five different
compartments, i.e. sinus venosus (SV), atrium (A), atrio-
ventricular canal (AVC), ventricle (V) and outflow tract
(OFT). Post-mortem cortical brain tissue of eight control
persons and 10 Huntington disease patients was obtained
from Prof Dr R.A.C. Roos (Leiden University, the
Netherlands). Total RNA was isolated using RNAeasy
columns (Qiagen) according to the manufacturer’s instruc-
tions. The total RNA was treated with DNase RQ1
(Promega) and the integrity of the RNA was checked
The basic equation for PCR kinetics (Equation 1) states that the amount of amplicon after c cycles (Nc) is the starting concentration of the
amplicon (N0) times the amplification efficiency (E) to the power c. The PCR efficiency in this equation is a number between 1 and 2 (2 indicates
100% efficiency). The PCR efficiency can be defined as the increase in amplicon per cycle (Equation 5). During the exponential phase of the PCR
reaction this efficiency is constant. When a fluorescence baseline is included in the PCR model (parameter BL in Equation 4), and the estimation
of the baseline is incorrect, the cycle-to-cycle efficiency contains a constant (B?) in both the denominator and the numerator (Equation 6), which
leads to the observation of a biased efficiency. Equation 1 can be inverted (Equation 2) to calculate the starting concentration (N0) from the user-
defined fluorescence threshold (Nt), the efficiency and the fractional number of cycles needed to reach the threshold (Ct). This N0is expressed in
arbitrary fluorescence units. The starting concentration of amplicon A (N0,A) can be expressed relative to that of amplicon B (N0,B) by direct
division of these starting concentrations (Equations 3A and 3B). When the fluorescence thresholds for both amplicons are equal, the expression
ratio in Equation 3B can be ‘simplified’ to Equation 3C. However, further reduction of the number of parameters leading to Equation 3D,
requires that the efficiencies of both amplicons (EAand EB) are equal. If this requirement is not met, a bias is introduced in the expression ratio
(Equation 7A). This bias is defined as the real ratio (Equation 3C) divided by the biased ratio (Equation 3D). Rearrangement (Equation 7B) and
the assumption that Ecommonis the geometric mean of the amplicon efficiencies EAand EBthen leads to Equation 7C. From this equation, it
follows that the bias introduced by ignoring the difference between amplicon efficiencies is an exponential function of the relative error of Ecommon
and the sum of the Ctvalues. Note that the application of Equations 2, 3B or 3C is mathematically equivalent to extrapolation of the regression
line(s) through the log-linear phase to cycle 0 (Figure 1A).
Equations used in the analysis of quantitative PCR data. The equations are numbered according to their appearance in the text.
if Nt,A= Nt,B
only if EA = EB
(if Ecommonis the geometric mean of EAand EB )
basic equation of PCR kinetics:
estimation of starting concentration:
estimation of fold difference:
bias of ignoring an efficiency difference:
with baseline parameter:
, 0 /
Nucleic Acids Research,2009, Vol. 37,No. 6PAGE2 OF12
using the BioAnalyzer and the Agilent RNA 6000 Nano
kit (II). A 1–0.5mg total RNA was converted into cDNA
using an anchored poly-dT primer and the Superscript II
(human samples) or III (chicken samples) Reverse tran-
scription kit (Invitrogen).
The samples of the ‘Huntington Disease’ and the ‘serial
dilution’ data series were amplified in 96-well plates in an
Applied BioSystems ABI7300. The qPCR reaction was
done in 20ml with primers for ATG5 (Forward: GGCC
ATCAATCGGAAACTCAT; Reverse: AGCCACAGGA
CGAAACAGCTT; product: 123bp), PSMB5 (Forward
TGTCCCAGAAGAGCCAGGAAT; Reverse: GCAAT
GTAAGCACCCGCTGTA; product 116bp) or EEF1A1
(Forward: AAGCTGGAAGATGGCCCTAAA; Reverse:
Q-PCR SYBR Green Mastermix (Applied Biosystems)
in a concentration of 0.3mM. The used protocol was iden-
tical for all primer sets: 10min 958C, 40? (15s 958C, 30s
608C, 30s 728C).
The samples of the ‘developing chicken heart’ dataset
LightCycler480. The qPCR reaction was done in 10ml
with a primer concentration of 1mM and SYBR Green
qPCR Master Mix (Roche). The primers used were
157bp), NDUFB3 (Forward: CTCGAGGAGGTCCAA
CC; product 101bp). These samples were measured in
three separated runs using the following protocol 5min
968C, 45? (10s 958C, 20s 588C, 20s 728C).
PCR efficiency determination
The raw (i.e. not baseline-corrected) PCR data were used
in the analysis. Baseline correction was carried out with a
baseline trend based on a selection of early cycles or with
the algorithm developed in this study. The PCR efficiency
for each individual sample was derived from the slope of
the regression line fitted to a subset of baseline-corrected
data points in the log-linear phase using LinRegPCR (15).
Next to the three datasets mentioned above, 19 raw data-
sets of four different qPCR platforms (2–10 datasets per
platform) were used in the development of a baseline esti-
mation algorithm. These datasets were selected because of
the presence of ‘difficult’ samples. In these datasets, 56
different targets were amplified (3–30 tissue samples per
amplicon per PCR run, 1–13 different amplicons per run).
Ctvalues ranged from 4.5 up to 47. Several alternatives for
the developed method to estimate the baseline fluorescence
were applied to each of the datasets to determine their
robustness. Similarly, the algorithm to set the Window-
of-Linearity (W-o-L) was tested on all datasets.
Baseline is defined in this article as the level of fluorescence
measured before any specific amplification can be
detected. The raw qPCR data, i.e. the fluorescence inten-
sities measured after each amplification cycle, follow the
exponential model given by Equation 4 in which the base-
line is assumed to be constant for all cycles.
In the exponential phase of the PCR reaction, the
amplification efficiency can theoretically be estimated
from cycle to cycle as E=Nc+1/Nc, which is the fold
(10,22,23). When this cycle-to-cycle efficiency represents
the real efficiency in the current PCR run, an underesti-
mation of the baseline value will lead to the addition of a
positive constant in both the numerator and the denomi-
nator of the cycle-to-cycle efficiency (Equation 6), which
leads to an underestimation of the efficiency. In the same
way, an overestimation of the baseline leads to an over-
estimation of the PCR efficiency. A simple exercise
(Figure 1A and Supplementary Figure S1) shows that an
error in the baseline leads to a similar error in the observed
efficiency, whereas the resulting error in the N0estimation
is about an order of magnitude larger.
Most PCR systems currently use a linear baseline trend
derived from a user-defined set of early amplification
cycles. Application of three baseline choices (‘baseline’
cycles 3–5, 3–10 and 3–15) to the three datasets results
(Figure 2B, Supplementary Figures S2B, S3B and S4B).
The log-linear plots of the baseline-corrected datasets
show the characteristic convex and concave amplification
curves that result from over- or underestimation of the
fluorescence baseline (21) (Figure 2A; upper panel,
Supplementary Figures S2A, S3A and S4A). A baseline
based on a fixed number of early observations always runs
the risk of being overestimated due to inclusion of ampli-
Attempts to implement published algorithms, e.g. ref.
(5) to determine the baseline value from the ground cycles
prior to observable amplification proved to be pointless
because of the noise and the behavior of the signal at the
start of the PCR (7). The very nature of the data in the
first cycles, as well as the unknown chemistry and physics
underlying those data values, which display no reproduc-
ible trends from amplicon to amplicon, run to run and
platform to platform, effectively prohibited the attempts
to develop a robust baseline estimation algorithm based
on the ground phase data (results not shown). The base-
line estimation algorithm we propose is based on the
assumption that amplification efficiency is constant from
the very first cycle onward. The cycle-dependent change in
efficiency that is predicted by some alternative analysis
models, e.g. refs (19,20,24), will be considered in the dis-
cussion. A constant PCR efficiency would, on a semi-
logarithmic plot, lead to a straight line of data points in
the whole log-linear phase. A proper baseline estimate will
therefore result in a dataset in which these points are on a
straight line (Figure 3B). Details on the baseline estima-
tion algorithm are given in Figure 3A. The algorithm is
PAGE 3 OF 12 Nucleic AcidsResearch, 2009, Vol.37,No. 6e45
applied to each sample separately and does not include a
criterion on the value of the slope of the resulting log-
linear data points. However, when this baseline estimation
algorithm is applied to the three datasets, all datasets
show corrected data with amplification curves that are
closely parallel per amplicon (Figure 2A, Supplementary
Figures S2A; lower panel, S3A and S4A).
Window-of-Linearity andfluorescence threshold
The residual measurement noise, even after optimal base-
line subtraction, still strongly affects the fluorescence
values at the lower end of the log-linear phase.
Therefore, a decision has to be made which data points
in the log-linear phase will be used for the estimation of
the PCR efficiency of each sample. In this article, these
data points will be referred to as the data points in the
W-o-L (15). Most PCR data analysis methods assume the
PCR efficiency to be the same for all samples per amplicon
and PCR run. Indeed, the variability in observed efficiency
values seems to reflect primarily a random error, and not a
real variation (16). Or, to quote Peirson and co-workers
(10), ‘one must assume that the amplification efficiency is
comparable unless there is sufficient evidence to suggest
Based on this consideration, the algorithm to set the
W-o-L searches for the window with the least variation
between efficiencies. This algorithm is illustrated in
Figure 4A. The procedure has to be carried out per ampli-
con, because the PCR efficiency can differ per primer pair
and amplicon sequence. The window with the minimum
coefficient of variation of efficiency values is chosen as
the optimal W-o-L. No criterion is set on the absolute
value of the efficiencies. However, in all datasets, the min-
imum variance coincides closely with a maximum mean
efficiency (Figure 4B, right). When the experimental
Figure 1. Effect of baseline estimation errors in quantitative PCR data analysis. (A) The graph shows amplification curves of a reference (closed
symbols, dashed lines) and a target gene (open symbols, solid lines) after subtraction of the correct and erroneous baselines. The different intercepts
of the lines with the Y-axis illustrate the calculation of the starting concentration (N0) based on the observed PCR efficiency values (Box 1;
Equation 2). The table shows that with an independent and random baseline estimation error of up to 2%, in both the reference gene and the
target gene, the observed expression ratio varies from 0.7 to 9.5. Note that the extrapolation of the regression line(s) through the log-linear phase to
cycle 0 is mathematically equivalent to the application of Equations 2, 3B or 3C (Box 1). (B) Raw fluorescence data of a PCR reaction with different
primer concentrations. The curves show the amplification of NppB in chicken heart tissue. The fluorescence baseline increases with increasing primer
concentration. (C) Flowchart of the analysis of quantitative PCR data described in this article.
Nucleic Acids Research,2009, Vol. 37,No. 6PAGE4 OF12
condition is suspected to influence the PCR efficiency, a
W-o-L has to be set per condition and the resulting PCR
efficiencies should be compared.
The threshold cycle Ct, which is defined as the fractional
PCR cycle at which a preset threshold of PCR product is
observed, has been the mainstay of qPCR data analysis
since the introduction of fluorescence monitoring of
the PCR reaction (8). This Ctvalue is proportional to
the logarithm of the initial target concentration at con-
stant amplification efficiency (Figure 1A, Equation 2).
The best reproducibility of the Ctvalue is achieved when
the fluorescence threshold is placed in the upper part of
the log-linear phase (Supplementary Figure S3D). The
effect of the baseline estimation on the observed Ctvalue
is small; even with a 30% baseline error, the observed Ct
values fall within a range of 0.3 cycles on either side of the
‘true’ Ctvalue (Supplementary Figure S1F).
The W-o-L algorithm was applied to all baseline-
corrected datasets and the resulting PCR efficiencies per
sample were plotted (Figure 2B). When the baseline was
corrected with the above described baseline estimation
algorithm, the variance among individual PCR efficiency
values was significantly reduced compared to the variance
after baseline correction with a baseline trend based on
early cycles (Supplementary Figures S2B, S3B and S4B).
Ctvalues were only marginally affected by different base-
lines (Supplementary Figure S4D).
The choice of the efficiency value to be used in qPCR data
analysis is a recurring theme in qPCR papers. In an exten-
sive hierarchical design, Karlen and co-workers (11)
showed that bias was removed and that high resolution,
Figure 2. Effect of the baseline estimation method on qPCR data analysis. (A) PCR amplification curves of NppB and NDUFB3 in samples of five
different parts of the developing chicken heart. Baseline fluorescence was estimated by the system software as a linear trend through the observations
of cycles 3 through 10 (BL 3–10, top panel) or with the baseline estimation method described in this article (LinRegPCR, bottom panel). See
Supplementary Figure S2A for additional system baseline settings.(B) PCR efficiency values of NppB and NDUFB3 from each individual sample
(open circles) in three independent PCR runs. An optimal W-o-L was applied per amplicon per plate. Mean efficiencies per plate and per amplicon
were calculated. PCR efficiencies were determined after application of three baseline trends, as well as after the LinRegPCR baseline subtraction. The
variation was lowest in LinRegPCR-derived PCR efficiency values (see Supplementary Figure S2B). (C) NppB/NDUFB3 gene expression ratio in
different parts of the developing chicken heart for each of the baseline correction methods. Note that the pattern of observed expression ratios
depends on the applied baseline correction method. Variation in expression ratios per tissue is lowest in data derived from LinRegPCR-corrected
PAGE 5 OF 12 Nucleic AcidsResearch, 2009, Vol.37,No. 6 e45
precision and robustness were reached when PCR efficien-
cies of different samples per amplicon were averaged
over all measurements done on one cDNA. Similarly,
Cikos and co-workers (12) showed that intra- and inter-
assay variability decreased when the individual efficiencies
were replaced by the mean efficiency per amplicon. These
recommendations were based on the estimation of PCR
efficiencies per sample in which each sample was fitted
to its own W-o-L (15). However, setting the W-o-L
per amplicon already reduces the variation between
Different W-o-L settings were compared to determine
which W-o-L to use, and which efficiencies to average.
A dataset of qPCR samples of brain tissue containing
Huntington patients and control samples served to study
the effect of averaging efficiencies on the variation and the
bias of the reported concentrations for two amplicons.
Three different window settings were used. We previously
proposed to base the W-o-L on the best-fitting straight
line though 4–6 data points (15). This setting resulted in
Figure 3. Fluorescence baseline estimation. (A) Flowchart of the baseline estimation algorithm. For each sample, an initial baseline is set to the
minimum observed fluorescence. Samples are skipped when less than seven times increase in fluorescence values is observed. For each sample that
shows amplification, an iterative algorithm than repeatedly adjusts the baseline value until the slope of the regression line through the data points in
the upper half of the exponential phase differs less than 0.0001 from the slope of the line through the data points in the lower half. At a PCR
efficiency of 1.8, this criterion translates into an efficiency difference of 0.0004. The algorithm results in a set of data points on a straight line and
effectively reconstructs the exponential phase. (B) Comparison of amplification curves resulting from an optimal baseline (filled squares) with the
curves resulting from 1% to 5% over-estimated (gray and open triangles) and 1% to 5% under-estimated baselines (gray and open circles) showing
that the shape of the curves is dependent on the baseline estimate (21). This change in shape of the curve is used to estimate the optimal baseline (A).
(C) The graph shows the baseline values in both phases of the baseline estimation. (D) The graph shows the slopes of the regression lines through the
upper (Supper) and lower (Slower) halves of the continuous set of data points in the exponential phase when the baselines in (C) are applied.
Nucleic Acids Research,2009, Vol. 37,No. 6PAGE6 OF12
Figure 4. Setting the W-o-L. (A) Flowchart of the algorithm to determine the position of the W-o-L. The search for the optimal W-o-L starts with the
upper limit of this window set at the mean fluorescence level found at the maximum of the second derivative (SDM) of the baseline-corrected fluorescence
data. After application of this initial window, a loop is started in which the window is systematically lowered by half of the fluorescence increase per cycle.
For each window, the coefficient of variation (CV) is calculated from the mean and the standard deviation of the PCR efficiencies. The minimum CV
marks the W-o-L in which the PCR efficiencies of the samples show the least variation relative to the meanefficiency. (B) Intermediate results of the W-o-L
setting algorithm. The left panel shows the baseline-corrected amplification curves of an example data set and the optimal W-o-L. The mean PCR
efficiency and its standard deviation are plotted for each W-o-L (right panel). The smallest CV, and thus the smallest between-sample PCR efficiency
variation, marks the optimal W-o-L. Data points at the beginning of the log-linear phase are preferentially present when a positive statistical noise carries
them just above baseline. Consequently, in a very low W-o-L those samples behave as if their baseline was under-estimated and they contribute a low
efficiency to the mean. This leads to the decrease of the mean efficiency in the lower-than-optimal windows.
PAGE 7 OF 12 Nucleic AcidsResearch, 2009, Vol.37,No. 6e45
highly variable PCR efficiencies (Figure 5A, left). When
the W-o-L is set per amplicon, the variability between
(Figure 5A, right) and neither amplicon showed a differ-
ence in PCR efficiencies between Control and Huntington
patients (Supplementary Figure S4B). For both ampli-
cons, the frequency distribution of the observed PCR effi-
ciencies is normal and symmetrical around the mean PCR
efficiency (Figure 5C). This justifies the use of the mean of
these efficiencies as an estimate for the true PCR efficiency
per amplicon. For further discussion, the efficiency values
were also determined by setting a common window for
both amplicons (Figure 5A, middle) and a common
is significantly reduced
(mean) PCR efficiency (EC) was calculated, thus ignoring
the amplification difference between amplicons.
The variability in estimated starting concentrations
(Figure 5B) is not statistically different between the differ-
ent W-o-L settings but there appears to be slightly less
variation in the analysis in which a common efficiency is
used (Figure 5B, middle). However, when for each sample
the ratio of the two starting concentrations is calculated,
the individual and the amplicon efficiencies result in sim-
ilar ratios, with a larger variability for the individual effi-
ciencies (Figure 5D, left and right). The ratios resulting
from the common efficiency are significantly lower
(Figure 5D, middle), which illustrates that ignoring the
Figure 5. Comparison of the use of individual, common or amplicon-specific PCR efficiencies. (A) PCR efficiency values for ATG5 (gray) and
PSMB5 (white) in controls and Huntington patients were based on the individual sample (individual window), a W-o-L for all samples from both
amplicons (common window) and a W-o-L set for each of the two amplicons (amplicon window). For each amplicon, the variation in PCR values
was highest in individual windows and lowest when a W-o-L per amplicon (F-test, P<0.001 for both amplicons) was used. The mean efficiency per
amplicon did not differ between the three W-o-L settings (one-way ANOVA; P=0.183 and P=0.101, respectively) but for all windows the
efficiencies of the two amplicons differed significantly (t-test: all P<0.0001). ECindicates the common PCR efficiency that results when the difference
between amplicons is ignored. (B) Starting concentrations (N0expressed in arbitrary fluorescence units) in brain tissue for both amplicons in Controls
and Huntington patients calculated with the individual, common, and amplicon efficiency. There is no significant difference between the variation in
N0values per amplicon and experimental group although the variation is lowest when the common PCR efficiency was used. For both amplicons, the
starting concentrations are significantly lower when the results were obtained with a common efficiency (t-test, P?0.001 for both amplicons and
comparisons). The N0values do not differ when they were obtained with individual or amplicon efficiencies (t-test, P=0.916 and P=0.994 for
ATG5 and PSMB5, respectively). (C) Frequency distributions of the individual PCR efficiency values determined with a W-o-L per amplicon. The
distribution of efficiency values is symmetrical and normally distributed (Shapiro–Wilk test; P=0.933 and P=0.478 for ATG5 and PSMB5,
respectively). (D) When the gene expression ratio (PSMB5/ATG5) in Controls and Huntington patients is based on the N0values calculated with
the individual or the amplicon efficiency, the average ratios are similar (dotted lines), but the variation in the ratios is significantly reduced when the
amplicon efficiencies are used [F-test on log(ratio); P=0.009]. When the expression ratio is calculated with the common efficiency the average ratio is
significantly biased (t-test; P<0.0001 compared to both the individual and the amplicon efficiency results). This bias results from ignoring the
difference between the amplicon efficiencies (Box 1; Equation 7).
Nucleic Acids Research,2009, Vol. 37,No. 6PAGE8 OF12
difference in PCR efficiency gives rise to biased ratio
results. The magnitude of this bias depends on the relative
difference between the amplicon efficiencies and the
common efficiency, as well as on the Ctvalues of the
two amplicons in the ratio (Equation 7).
Alternative dataanalysis approaches
The efficiency value used in the qPCR data analysis has to
be derived from the observed amplification data. Some
papers report that a ‘mean’ efficiency can be calculated
from the slope of a standard curve, which is a plot of
observed Ct values versus the log-concentration of a
serial dilution of a standard sample (Figure 6A and 6B)
(2,8,25). The regression line fitted to these data points is
then described by the equation Ct=log(Nc)/log(E) ? 1/
log(E)?log(N0) which is Equation 2, log-transformed and
rearranged to show the linear dependence of Ct on
log(N0). However, this equation does not describe a
straight line with a fixed slope when the amplification
efficiencies of the samples are not all equal (18). In that
case, the presence of the log(E) variable in both the slope
and intercept term of the above equation will result in a
standard curve-derived efficiency that does not represent
the true mean PCR efficiency of the samples (Figure 6C)
(15,26). Accordingly, several authors reported that the
mean of the individual PCR efficiencies gave less biased
results than a standard curve-derived efficiency (10,11,26).
Figure 6. Bias in starting concentrations introduced by standard curve-derived PCR efficiency values or nonlinear analysis procedures. (A) PCR
amplification curves of a serially diluted brain tissue sample (4 steps of 10 times dilution; 5 replicates per dilution) after baseline correction (see also
Supplementary Figure S3). (B) The standard curve scatter plot shows the Ctvalues plotted versus the known log-concentration of each serial dilution.
This series of five dilutions, measured in five replicates per dilution, was used to construct 3125 (=55) standard curves with one measurement per
dilution. (C) Frequency distribution of the efficiency values derived from the slopes of the 3125 standard curves. The diamond indicates the efficiency
value derived from the slope of the regression line fitted to all 25 observations. Inset: The individual efficiencies of the 25 amplification curves,
calculated from the data points in a common W-o-L. The arrow marks the mean of these individual PCR efficiencies. (D) Starting concentrations
(N0) calculated with the mean of the individual efficiency values (C; arrow in inset). Results were expressed relative to the mean N0value of the
undiluted samples. The graph shows that these N0values (grey circles) show a good correlation with the input values (observed = 0.962 ? input;
R2>0.999). The N0values calculated with the minimum (white circles) or maximum (black circles) efficiency derived from the standard curves show a
positive or negative bias, respectively. (E) The dilution series was analyzed with LinRegPCR (15) and with the Real-time PCR Miner application (7).
Miner performs a nonlinear fit of Equation 4 (Box 1) to a subset of raw data points in the exponential phase. The PCR efficiency values resulting
from Miner and LinRegPCR are plotted (filled and open circles, respectively). The Miner results show an increasing PCR efficiency with lower input
concentrations (P<0.001), LinRegPCR results do not (P=0.06). (F) Starting concentrations (N0) for the serial dilution dataset calculated by Miner
(open circles). The solid line is the regression through the starting concentrations observed with LinRegPCR (D; gray circles). The Miner results show
an increasing negative bias with lower input concentrations.
PAGE 9 OF 12Nucleic AcidsResearch, 2009, Vol.37,No. 6 e45
A similar result was observed for the serial dilution dataset
in this study (Figure 6D).
Data analysis methods that are based on the application
of linear regression algorithms (10,15) require baseline
subtraction before the logarithmic transformation because
of the fit of the logarithm of Equation 1 to a subset of data
in the exponential phase. In contrast, analysis methods
based on nonlinear curve fitting do not require such an
a priori baseline subtraction because the fitted mathemat-
ical models contain an additive term (i.e. y0or Fb) that
represents a constant (6,27,28) or cycle-dependent baseline
(5,19). These algorithms were applied to raw data (5) as
well as data that were baseline corrected by the system
software (6,20,28,29). In the latter papers the baseline
term is, therefore, ignored (or set to zero) in the derivation
of additional equations. This practice might lead to the
erroneous opinion that these analysis approaches are inde-
pendent of the proper handling of the fluorescence
Several authors use a sigmoid or logistic curve fit to
select the data points in the exponential phase and then
use nonlinear curve fitting to fit the exponential equation
[FC=Fb+F0?EC(Equation 4)] to determine the PCR
efficiency E (5,7). The start of the dataset used for this fit is
defined as the first point above the ground phase noise
which leads to an overestimation of the baseline parameter
(Fb). The risk implied in the direct fitting of Equation 4 is
that the balance between the two additive parts of this
equation is determined by the input concentration of
the amplicon (F0); when the baseline is overestimated,
the second term in the equation has to compensate.
Especially, for low starting concentrations this compensa-
tion has to be found in a high efficiency value. Examples of
the resulting upward trend of efficiency values with
decreasing starting concentration can be found in litera-
ture, e.g. (19,24). The application of this nonlinear fit [i.e.
Miner (7)] to the serial dilutions dataset also shows such a
relation between input concentration and PCR efficiency
(Figure 6E). Starting concentrations, calculated with these
efficiency values, show an increasingly strong negative bias
with lower input concentrations (Figure 6F). The same
data were analyzed with the method described in this arti-
cle and show a constant PCR efficiency, irrespective of the
input concentration (Figure 6E).
The nonlinearfit of
(Equation 4) (5,7,24) differs from the method described
in the current article only because we propose a two step
approach: first find an estimate for the baseline value and
then fit the PCR amplification equation (Equation 1) to
the baseline-subtracted data. The logarithmic approach
used in our baseline estimation method gives more
weight to the low, close to baseline, observations; con-
structing a straight line down from the start of the plateau
phase thus leads to a more precise estimate of the baseline.
Currently, the mainstream of analysis of qPCR data is
based on the Ctvalue of each sample and a PCR efficiency
value per amplicon. Application of a calculation equation
derived from Equation 1 then leads to an estimate of the
starting concentration expressed in arbitrary fluorescence
units or an estimate of the ratio between two starting
concentrations of the transcript-of-interest (Equation 2
and Equations 3B or 3C, respectively). This article deals
with the analysis of qPCR data resulting from the mon-
itoring of DNA binding dyes like SYBR Green I, but most
of the principles discussed in this article also apply to data
collected with other fluorescent chemistries (e.g. hydrolysis
probes). However, analysis of such data sets requires extra
data processing steps that are not discussed in this text.
Analysis of qPCR data requires the derivation of a PCR
efficiency value from the observed data. This article shows
that the observed PCR efficiency is strongly influenced by
small errors in the applied baseline correction. As
described, it proves impossible to estimate a baseline
value from the so-called ground phase data because the
source of this fluorescence is not clear. The main source of
baseline fluorescence is unbound fluorochrome (e.g.
SYBR Green), which is not fully nonfluorescent (4).
However, baseline fluorescence also depends on sample
dilution, and thus on total cDNA concentration, and on
primer concentration (Figure 1B). Together with the uni-
dentified interactions between those fluorescence sources,
the prediction and modeling of baseline behavior is cur-
rently unfeasible. Our conclusion that there is not enough
ground for the development of an algorithm to determine
the baseline from the ground phase data is in line with the
findings of others (7).
The baseline estimation algorithm described in the
current article is based on the kinetic model of PCR ampli-
fication (Equation 1) and a constant PCR efficiency.
Cycle-dependent changes in PCR efficiency are predicted
by sigmoid models used in qPCR analysis (20,28,29). The
use of such sigmoid models is not based on biophysical/
biochemical considerations of PCR kinetics, but mainly
on their good fit to raw qPCR data. Recent papers show
that despite their overall good fit, these models do not fit
well to the exponential phase data (7,29). Therefore, these
‘empirical’ models do not provide a solid basis for
modeling of the behavior of the PCR efficiency during
the PCR reaction. On the other hand, it was established
that, when modeling PCR as a statistical branching
process, PCR efficiency is constant from the first cycle
until the beginning of the plateau phase (30). A modeling
study based on kinetic annealing confirmed this notion
(23). Moreover, the N0value estimated with Equation 2,
at large enough Ct, has been shown to be an unbiased
estimate of the real starting amount (22).
With a constant PCR efficiency the value of each data
point up till the start of the plateau phase is the sum of the
baseline fluorescence and an exponentially increasing
amplicon-dependent fluorescence (Equation 4). An algo-
rithm that searches for a baseline value that results in the
longest straight line of data points when plotted on a semi-
logarithmic scale, isolates the exponentially increasing
part of the observed fluorescence values. This algorithm
requires a sufficiently large baseline-to-plateau ratio as
well as a low observation noise. In datasets that do not
fulfill these requirements a reliable straight line in the log-
linear phase will not be found. The baseline value can be
Nucleic Acids Research,2009, Vol. 37,No. 6PAGE 10 OF12
lowered by lowering the primer concentration (Figure 1B);
observation noise can be reduced by setting a fixed,
instead of an adaptive, exposure time in the qPCR appa-
ratus. Note that the baseline estimation algorithm does
not include a ‘goodness-of-fit’ criterion. The chosen algo-
rithm ensures that points at lower cycle numbers are only
included as long as they randomly deviate from the
straight line defined by the points in the upper part of
the exponential phase. Such a provision would not be
possible when the algorithm includes a ‘goodness-of-fit’
criterion for the whole log-linear phase.
Even after minimizing PCR efficiency variability and
setting of a W-o-L per amplicon, similar samples show
slightly different observed PCR efficiencies. To the best
of our knowledge, no sample-dependent PCR efficiency
differences have ever been reported (10,31). Variability
of the PCR efficiency values has been attributed to a lim-
ited precision of individual data (12) and thus reflects
mainly a statistical error and not a real difference (16).
Accordingly, most researchers choose to use a fixed or
the mean efficiency per amplicon in their analysis of
qPCR data. The symmetric distribution of the individual
efficiency values (e.g. Figure 5B and inset of Figure 6C)
justifies using the arithmetic mean efficiency. Although the
use of a fixed PCR efficiency for all samples per amplicon
is well supported, it is still important to use an efficiency
value that represents the true efficiency. Equation 7
shows that the bias in the expression ratio resulting from
using a common efficiency value for two amplicons,
instead of the amplicon-specific efficiencies, depends on
the relative difference in efficiencies as well as the Ct
values of both samples. An example of such a bias is illu-
strated in Figure 5D.
Based on the results and considerations in the current
paper, the LinRegPCR analysis program (15) has been
updated. Although this updated version of the program
can be used in a ‘load-and-click’ mode, the different vari-
ation sources in qPCR analysis make that no analysis
system can be used as a black box. Every user of qPCR
should stay aware of hitherto unknown variables affecting
the analysis. The experimental set-up should be aimed at
recognizing the variables of interest and should enable the
analysis of the significance of such variables. Analysis sys-
tems cannot relief the researcher of this task.
Supplementary Data are available at NAR Online.
The authors wish to thank Drs Vincent Christoffels and
Fred van Leeuwen for their helpful discussions during the
course of this research. The post-mortem HD tissue sam-
ples used in the ‘brain’ dataset were generously provided
by Prof Dr R. A. C. Roos, Leiden University, the
Netherlands. The data on chicken heart development
were generated by Ms Saskia van der Velden.
European Union FP6 program HeartRepair (LSHM-
CT-2205-018630). Funding for open access charge: same
Conflict of interest statement. None declared.
1. Bustin,S.A. (2002) Quantification of mRNA using real-time reverse
transcription PCR (RT-PCR): trends and problems. J. Mol.
Endocrinol., 29, 23–39.
2. Nolan,T., Hands,R.E. and Bustin,S.A. (2006) Quantification of
mRNA using real-time RT-PCR. Nat. Protoc., 1, 1559–1582.
3. Rebrikov,D.V. and Trofimov,D.I. (2006) Real-time PCR: a review
of approaches to data analysis. Appl. Biochem. Microbiol., 42,
4. Zipper,H., Brunner,H., Bernhagen,J. and Vitzthum,F. (2004)
Investigations on DNA intercalation and surface binding by SYBR
Green I, its structure determination and methodological implica-
tions. Nucleic Acids Res., 32, e103.
5. Tichopad,A., Dilger,M., Schwarz,G. and Pfaffl,M.W. (2003)
Standardized determination of real-time PCR efficiency from a
single reaction set-up. Nucleic Acids Res., 31, e122.
6. Rutledge,R.G. (2004) Sigmoidal curve-fitting redefines quantitative
real-time PCR with the prospective of developing automated high-
throughput applications. Nucleic Acids Res., 32, e178.
7. Zhao,S. and Fernald,R.D. (2005) Comprehensive algorithm for
quantitative real-time polymerase chain reaction. J. Comput. Biol.,
8. Livak,K.J. (2001) ABI Prism 7700 Sequence Detection System.
User Bulletin #2, http://docs.appliedbiosystems.com/pebiodocs/
9. Pfaffl,M.W. (2001) A new mathematical model for relative quanti-
fication in real-time RT-PCR. Nucleic Acids Res., 29, e45.
10. Peirson,S.N., Butler,J.N. and Foster,R.G. (2003) Experimental
validation of novel and conventional approaches to quantitative
real-time PCR data analysis. Nucleic Acids Res., 31, e73.
11. Karlen,Y., McNair,A., Perseguers,S., Mazza,C. and Mermod,N.
(2007) Statistical significance of quantitative PCR. BMC
Bioinformatics, 8, 131.
12. Cikos,S., Bukovska,A. and Koppel,J. (2007) Relative quantification
of mRNA: comparison of methods currently used for real-time
PCR data analysis. BMC Mol. Biol., 8, 113.
13. Freeman,W.M., Walker,S.J. and Vrana,K.E. (1999) Quantitative
RT-PCR: pitfalls and potential. Biotechniques, 26, 112–115.
14. Gentle,A., Anastasopoulos,F. and McBrien,N.A. (2001) High-
resolution semi-quantitative real-time PCR without the use of a
standard curve. Biotechniques, 31, 504–506, 508.
15. Ramakers,C., Ruijter,J.M., Lekanne Deprez,R.H. and
Moorman,A.F.M. (2003) Assumption-free analysis of quantitative
real-time polymerase chain reaction (PCR) data. Neurosci. Lett.,
16. Nordgard,O., Kvaloy,J.T., Farmen,R.K. and Heikkila,R. (2006)
Error propagation in relative real-time reverse transcription poly-
merase chain reaction quantification models: the balance between
accuracy and precision. Anal. Biochem., 356, 182–193.
17. Kontanis,E.J. and Reed,F.A. (2006) Evaluation of real-time PCR
amplification efficiencies to detect PCR inhibitors. J. Forensic Sci.,
18. Wilhelm,J., Pingoud,A. and Hahn,M. (2003) Validation of an
algorithm for automatic quantification of nucleic acid copy numbers
by real-time polymerase chain reaction. Anal. Biochem., 317,
19. Batsch,A., Noetel,A., Fork,C., Urban,A., Lazic,D., Lucas,T.,
Pietsch,J., Lazar,A., Schomig,E. and Grundemann,D. (2008)
Simultaneous fitting of real-time PCR data with efficiency of
amplification modeled as Gaussian function of target fluorescence.
BMC Bioinformatics, 9, 95.
PAGE 11 OF 12Nucleic AcidsResearch, 2009, Vol.37,No. 6e45
20. Rutledge,R.G. and Stewart,D. (2008) A kinetic-based sigmoidal Download full-text
model for the polymerase chain reaction and its application to
high-capacity absolute quantitative real-time PCR. BMC
Biotechnol., 8, 47.
21. Bar,T., Stahlberg,A., Muszta,A. and Kubista,M. (2003) Kinetic
outlier detection (KOD) in real-time PCR. Nucleic Acids Res., 31,
22. Peccoud,J. and Jacob,C. (1996) Theoretical uncertainty of mea-
surements using quantitative polymerase chain reaction. Biophys. J.,
23. Gevertz,J.L., Dunn,S.M. and Roth,C.M. (2005) Mathematical
model of real-time PCR kinetics. Biotechnol. Bioeng., 92, 346–355.
24. Spiess,A.N., Feig,C. and Ritz,C. (2008) Highly accurate sigmoidal
fitting of real-time PCR data by introducing a parameter for
asymmetry. BMC Bioinformatics, 9, 221.
25. Pfaffl,M.W., Horgan,G.W. and Dempfle,L. (2002) Relative expres-
sion software tool (REST) for group-wise comparison and statistical
analysis of relative expression results in real-time PCR. Nucleic
Acids Res., 30, e36.
26. Schefe,J.H., Lehmann,K.E., Buschmann,I.R., Unger,T. and
Funke-Kaiser,H. (2006) Quantitative real-time RT-PCR data
analysis: current concepts and the novel ‘‘gene expression’s C (T)
difference’’ formula. J. Mol. Med., 84, 901–910.
27. Tichopad,A. and Pfaffl,M.W. (2002) Improving quantitative real-
time RT-PCR reproducibility by boosting pimer-liked amplification
efficiency. Biotechnol Lett., 24, 2053–2056.
28. Liu,W. and Saint,D.A. (2002) Validation of a quantitative method
for real time PCR kinetics. Biochem. Biophys. Res. Commun., 294,
29. Swillens,S., Dessars,B. and Housni,H.E. (2008) Revisiting the sig-
moidal curve fitting applied to quantitative real-time PCR data.
Anal. Biochem., 373, 370–376.
30. Stolovitzky,G. and Cecchi,G. (1996) Efficiency of DNA
replication in the polymerase chain reaction (polymerization
reaction/branching processes/kinetic model/quantitative
polymerase chain reaction). Proc. Natl Acad. Sci. USA, 93,
31. Fleige,S., Walf,V., Huch,S., Prgomet,C., Sehm,J. and Pfaffl,M.W.
(2006) Comparison of relative mRNA quantification models and the
impact of RNA integrity in quantitative real-time RT-PCR.
Biotechnol. Lett., 28, 1601–1613.
Nucleic Acids Research,2009, Vol. 37,No. 6PAGE 12 OF12