U.S. patent application number 17/207488 was filed with the patent office on 2021-10-07 for methods and systems for a digital pcr experiment designer.
The applicant listed for this patent is LIFE TECHNOLOGIES CORPORATION. Invention is credited to Gordon A. Janaway, Joanna Lankester, Nivedita Sumi Majumdar, Jeffrey A. Marks, Shweta Raizada, Marcin Sikora, Daniel Wessel, Thomas Wessel, David C. Woo.
Application Number | 20210313010 17/207488 |
Document ID | / |
Family ID | 1000005654964 |
Filed Date | 2021-10-07 |
United States Patent
Application |
20210313010 |
Kind Code |
A1 |
Majumdar; Nivedita Sumi ; et
al. |
October 7, 2021 |
METHODS AND SYSTEMS FOR A DIGITAL PCR EXPERIMENT DESIGNER
Abstract
A computer-implemented method for designing a digital PCR (dPCR)
experiment is provided. The method includes receiving, from a user,
a selection of optimization type. The optimization type may be
maximizing the dynamic range, minimizing the number of substrates
including reaction sites needed for the experiment, determining a
dilution factor, or determining the lower limit of detection, for
example. The method further includes receiving, from the user, a
precision measure for an experiment, and a minimum concentration of
a target in a reaction site for the experiment. The method also
includes determining a set of dPCR experiment design factors for
the experiment based on the optimization type. The set of dPCR
experiment design factors is then displayed to the user.
Inventors: |
Majumdar; Nivedita Sumi;
(Foster City, CA) ; Wessel; Thomas; (Pleasanton,
CA) ; Woo; David C.; (Foster City, CA) ;
Sikora; Marcin; (Burlingame, CA) ; Janaway; Gordon
A.; (Castro Valley, CA) ; Raizada; Shweta;
(Southfield, MI) ; Lankester; Joanna; (San Carlos,
CA) ; Marks; Jeffrey A.; (Mountain View, CA) ;
Wessel; Daniel; (Pleasanton, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
LIFE TECHNOLOGIES CORPORATION |
Carlsbad |
CA |
US |
|
|
Family ID: |
1000005654964 |
Appl. No.: |
17/207488 |
Filed: |
March 19, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14428312 |
Mar 13, 2015 |
10984888 |
|
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PCT/US13/59815 |
Sep 13, 2013 |
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17207488 |
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61701380 |
Sep 14, 2012 |
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61714137 |
Oct 15, 2012 |
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61758216 |
Jan 29, 2013 |
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61788272 |
Mar 15, 2013 |
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61830507 |
Jun 3, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/686 20130101;
G16B 25/20 20190201; G16B 25/00 20190201; G16B 40/00 20190201; G16B
40/10 20190201 |
International
Class: |
G16B 25/20 20060101
G16B025/20; G16B 25/00 20060101 G16B025/00; G16B 40/00 20060101
G16B040/00; C12Q 1/686 20060101 C12Q001/686 |
Claims
1. A method for designing a digital PCR (dPCR) experiment, the
method comprising: receiving, from a user, a selection of
optimization type; receiving, from the user, a precision measure
for an experiment; receiving, from the user, a minimum
concentration of a target in a reaction site for the experiment;
determining a set of dPCR experiment design factors for the
experiment based on the optimization type; and displaying the set
of dPCR experiment design factors to the user.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. application Ser.
No. 14/428,312 filed Mar. 13, 2015, which is a 371 of International
Application No. PCT/US2013/059815 filed Sep. 13, 2013, which claims
the benefit of U.S. Provisional Application Nos. 61/830,507 filed
Jun. 3, 2013, 61/788,272 filed Mar. 15, 2013, 61/758,216 filed Jan.
29, 2013, 61/714,137 filed Oct. 15, 2012, and 61/701,380 filed Sep.
14, 2012, all of which disclosures are incorporated herein by
reference.
BACKGROUND
[0002] Digital PCR (dPCR) is an analytical technique that used to
provide absolute quantitation of nucleic acid samples, to detect
and quantify the concentration of rare targets, and to measure low
fold-changes in nucleic acid concentration.
[0003] In dPCR, a solution containing a relatively small number of
a target polynucleotide or nucleotide sequence may be subdivided
into a large number of small test samples, such that each sample
generally contains either one or more molecule of the target
nucleotide sequence or none of the target nucleotide sequence. When
the samples are subsequently thermally cycled in a PCR protocol,
procedure, or experiment, the samples containing the target
nucleotide sequence are amplified and produce a positive detection
signal, while the samples containing no target nucleotide sequence
are not amplified and produce no detection signal.
[0004] Potentially, a dPCR system may have a very high precision
enabling accurate measurement for genetic quantification. The
challenge with an unknown sample is to perform the experiment at a
dilution that falls within the dynamic range supported by the
system.
[0005] Generally, increasing the number of replicates increases the
accuracy and reproducibility of dPCR results. The dynamic range
depends on the total number of available reaction vessels and on
the measurement precision necessary for your application.
SUMMARY
[0006] In one exemplary embodiment, a computer-implemented method
for designing a digital PCR (dPCR) experiment is provided. The
method includes receiving, from a user, a selection of optimization
type. The optimization type may be maximizing the dynamic range,
minimizing the number of substrates including reaction sites needed
for the experiment, determining a dilution factor, or determining
the lower limit of detection, for example. The method further
includes receiving, from the user, a precision measure for an
experiment, and a minimum concentration of a target in a reaction
site for the experiment. The method also includes determining a set
of dPCR experiment design factors for the experiment based on the
optimization type. The set of dPCR experiment design factors is
then displayed to the user.
DESCRIPTION OF THE FIGURES
[0007] FIG. 1A and FIG. 1B illustrate graphs showing the
relationship of dynamic range, measurement precision, and the lower
and upper limits of detection according to various embodiments of
the present teachings.
[0008] FIG. 2 illustrates a plot showing the relationship of the
lower limit of detection (LLOD) and the upper limit of detection
(ULOD) to the number of substrates including reaction sites
according to various embodiments described herein.
[0009] FIG. 3 illustrates a plot showing the optimal concentration
ranges for dPCR experiments according to various embodiments
described herein.
[0010] FIG. 4 illustrates a plot showing how precision improves
with a larger number of reactions according to various embodiments
described herein.
[0011] FIG. 5 illustrates the effect of total interrogated volume
on the lowest limit of detection according to various embodiments
described herein.
[0012] FIG. 6 illustrates the relationship of precision with false
calls according to various embodiments described herein.
[0013] FIG. 7 illustrates contours showing the desired load for
optimal precision under the influence of nonzero false positives
and false negatives according to various embodiments described
herein.
[0014] FIG. 8 illustrates contours showing the lower limit of
detection under the influence of false positive and false negative
calls according to various embodiments described herein.
[0015] FIG. 9 illustrates contours showing the desired load
concentrations to compensate for positive/negative reaction loss
according to various embodiments described herein.
[0016] FIG. 10 illustrates contours showing the lower limit of
detection under the effect of reaction dropouts according to
various embodiments described herein.
[0017] FIG. 11 illustrates a graph showing how volumetric
variations in partition sizes deteriorate measurement capability at
the higher concentration according to various embodiments described
herein.
[0018] FIG. 12 illustrates a graph showing the target load in
percent negatives for best precision to compensate for partition
size variation according to various embodiments described
herein.
[0019] FIG. 13 illustrates an example of multiple dilutions with
partitioning according to embodiments described herein.
[0020] FIG. 14 illustrates the effect of dilutions on detection of
a target in a dPCR system according to various embodiments
described herein.
[0021] FIG. 15 illustrates a plot showing the effect of dilution on
the lower limit of detection and the dynamic range from a single
substrate according to various embodiments described herein.
[0022] FIGS. 16A and 16B illustrate the constraints on dilution
factors for continuous detection when using a combination of
multiple dilutions according to various embodiments described
herein.
[0023] FIG. 17 illustrates an increase in dynamic range with
dilutions according to various embodiments described herein.
[0024] FIGS. 18A, 18B, and 18C illustrate the tradeoff between the
number of dilution points and the increase in the dynamic range
according to various embodiments described herein.
[0025] FIG. 19 illustrates an exemplary computing system that
various embodiments described herein may be implemented.
[0026] FIG. 20A and FIG. 20B illustrate a flow chart with the
various digital PCR methods implemented by a dPCR experiment
designer according to various embodiments described herein.
[0027] FIG. 21A, FIG. 21B, FIG. 21C, FIG. 21D illustrate a method
for a gene expression workflow using the dPCR experiment designer
according to various embodiments described herein.
[0028] FIGS. 22A, FIG. 22B, FIG. 22C, and FIG. 22D illustrate a
method within the rare mutation detection workflow using the dPCR
experiment designer according to various embodiments described
herein.
[0029] FIG. 23A and FIG. 23B illustrate quantification results of a
dPCR experiment designer according to various embodiments described
herein.
[0030] FIG. 24 illustrates rare target detection against a
background signal according to various embodiments described
herein.
DETAILED DESCRIPTION
Digital PCR Modeling for Maximal Sensitivity, Dynamic Range and
Measurement Precision
[0031] The great promise of digital PCR is the potential for
unparalleled precision enabling accurate measurements for genetic
quantification. When maximal precision is desired, a challenge with
an unknown sample is to perform the experiment at a dilution that
supports the detection of one or multiple targets of interest at
the required measurement precision. A mathematical framework can be
used for modeling a digital PCR system with factors impacting
precision such as the number of available reaction sites, sample
volume reduction (due to a variety of causes), and false
negative/false positive rates. This framework is used to develop
graphics showing the relationship between precision and the
supported dynamic range. The impact of total input sample volume on
the lowest limit of detection or sensitivity is also illustrated.
According to various embodiments, this framework may be used in
methods encoded on a computer-readable medium implementable on a
processor of a computing system as a digital PCR experiment
designer.
[0032] According to various embodiments, a set of graphics modeling
the effects of various system parameters can serve as a powerful
tool for users to estimate dilution factors and number of reaction
sites necessary to get to a digital answer with the desired
precision. The model predicts an increase in supported dynamic
range, at a given precision, for the same number of reaction sites
with the use of two dilution points (using half the number of
reaction sites for each dilution). This increase in dynamic range
is obviously advantageous where continuous detection across an
entire dynamic range is desirable (e.g., genetic quantification).
The loss of half the number of reaction sites to a second dilution
point incurs a slight loss in the detectable concentration range at
a given precision. However, this loss is more than offset by the
gain in the set of detectable concentrations because of an
overlapping effect of the second dilution point. The results may
also predict possibilities to leverage the available number of
reaction sites to enable precise detection of two targets present
at largely different proportions within a given sample by careful
choice of dilution factors. In some embodiments, a majority of the
available reaction sites may be dedicated to detecting the rare
type and the remaining sites may be dedicated to detecting the wild
type at a very different dilution.
The Digital PCR Model
[0033] In a digital PCR experiment, sample DNA is partitioned into
a large number of reaction sites so that each gets none or one or
more copies. After performing PCR, amplification may be detected in
reaction sites that contained a DNA template whereas no
amplification may be detected in reaction sites lacking a DNA
template.
[0034] The reaction sites that do not show an amplified sample are
referred to as negatives and reaction sites that show amplification
are referred to as positives. Let A denote the average number of
molecules per reaction chamber and p denote the fraction of
negatives across n reaction sites in a digital PCR experiment.
Thus, the fraction of negatives `p` is related to .lamda. by the
following equations:
p = .times. e - .lamda. = .times. r n , ( 1 ) ( 2 )
##EQU00001##
where r=number of negative reaction sites; n=total number of
reaction sites. The number of substrates including reaction sites
in a system is N. Thus, for example, if a substrate includes 20000
reaction sites, then n=20000*N.
[0035] Using a large number of reaction sites with the assumption
of Poisson distribution of copies, the average number of copies per
reaction site can be calculated as .lamda.=-ln (r/n), where r is
the number of negative results and n is the total number of
reaction sites. Thus, the concentration of target in the input
volume may be estimated.
[0036] The confidence bounds around the estimate of A are given by
equation 3.
Confidence .times. .times. Bound ( Lower , Upper ) = exp .function.
[ ln .function. ( - ln .times. p ) .-+. 1 . 9 .times. 6 .times.
.sigma. ] , ( 3 ) ##EQU00002##
[0037] Precision is defined as the spread of the confidence
interval around .lamda. compared to the true value of .lamda.. The
smaller this spread, the more precise is the estimate. Precision
governs the upper limit of how close two values can be and yet be
detectable by the system. The precision measurement is not uniform
at all concentrations. FIG. 3 shows a plot 300 showing a confidence
interval around the measurement for a range of concentrations
(expressed in molecules per reaction) for 10K, 20K, 40K and 1M
reaction sites. In this example, the best precision is achieved at
a concentration showing 20.32% fraction of negative reactions
(irrespective of number of reactions), indicated by marker 310.
FIG. 3 also shows that precision deteriorates more rapidly toward
the higher concentrations. Plot 302 shows a percent deviation
versus the concentration in each reaction site and the percentage
of negatives for 10 k reactions. Plot 304 shows a percent deviation
versus the concentration in each reaction site and the percentage
of negatives for 20 k reactions. Plot 306 shows a percent deviation
versus the concentration in each reaction site and the percentage
of negatives for 1M reactions. In this example, the drop in
precision is sharper as the load concentration increases (right to
left on the x axis) than as the load concentration decreases (left
to right on the x axis). From this perspective, it may be advisable
to err on the side of using more dilute samples for the experiment.
The measurement precision for .DELTA. is given by:
Precision = 1 .lamda. .times. ( exp .function. [ ln .function. ( -
ln .times. p ) .-+. 1 . 9 .times. 6 .times. .sigma. ] - .lamda. ) (
4 ) ##EQU00003##
[0038] The variation represented by .sigma. in log .lamda. space
constitutes Poisson or sampling related component as shown in
equation 5:
.sigma. = .sigma. sampling = - 1 - p ( ln .times. .times. p )
.times. n .times. p ( 5 ) ##EQU00004##
[0039] Digital PCR results are based on having at least one
negative or one positive result. Otherwise, with all negatives or
all positives, it is not possible to deduce the concentration of a
sample within the reaction volume within a reaction site based on
the dPCR theory. The experimental scenario with only one negative
or only one positive result gives the limits of detection for a
dPCR experiment.
[0040] The low limit of detection (LLOD) occurs where there is only
one positive. Given that there exists any samples, the probability
of getting all negatives can be set to (1-confidence); or
equivalently, the probability of getting at least one positive can
be set to the confidence level. For example, for a 95% confidence
level at the low limit, the presence of the sample should be
detected in 95% of experiments, while the other 5% of experiments
would show no positives. Solving for the .lamda. at that point
gives .lamda. at low limit of detection, or .lamda..sub.LLOD given
as:
.lamda. LLOD = - ln .function. ( ( 1 - C ) 1 / n ) ) , ( 6 )
##EQU00005##
[0041] where C is the confidence level.
[0042] The upper Limit of Detection (ULOD) occurs where there is
only one negative. The probability of getting all positives can be
set to (1-confidence); or equivalently, the probability of getting
at least one negative can be set to the confidence level. Solving
for the A at that point gives A at high limit of detection,
.lamda..sub.ULOD as:
.lamda. ULOD = - ln .function. ( 1 - ( 1 - C ) 1 / n ) , ( 7 )
##EQU00006##
[0043] where C is the confidence level.
[0044] The ULOD and LLOD as defined described the theoretical
limits of detection. However, because the measurement precision at
the ULOD and LLOD are very poor, one can conceive of defining the
limits of detection in terms of a minimum required precision.
Alternately, one can choose to define arbitrary limits of detection
depending on how many actual positives or negatives one would like
to see in an experiment based upon the noise characteristics of the
system. The limits of detection can also depend on the number of
reaction sites. Plot 202 of graph 200 in FIG. 2 shows how the lower
limit of detection is lowered by increasing the number of reaction
sites. Plot 204 of graph 200 in FIG. 2 shows how the upper limit of
detection is raised by increasing the number of reaction sites.
[0045] Within this context, the dynamic range defines the span of
detectable concentrations in log 10 units. The dynamic range is
usually qualified by two other pieces of information: a detection
precision and the lowest detectable concentration. Plot 100 in FIG.
1A shows the dynamic range at 10% precision for 20000 reaction
sites. Plot 150 in FIG. 1A also shows how the dynamic range
increases with lower precision requirement from the system. The
dynamic range (DR) may also be constrained by a defining an
explicit lower and upper limits for detection as shown in FIG.
1B.
D .times. .times. R = log .times. .times. 10 ( .lamda. ULOD .lamda.
LLOD ) ( 8 ) ##EQU00007##
[0046] The detection precision is mainly influenced by the number
of available reaction sites and the lowest detectable concentration
is mainly influenced by the total sample volume interrogated. FIG.
3 and FIG. 4 show how precision improves with the larger numbers of
reaction vessels. The contours in FIG. 4 are values of measurement
precision expressed as a fraction. The precision values become
lower (improved) with increased number of reaction sites. FIG. 5
shows how the lowest detectable concentration changes with volume
for a fixed number of reaction sites (assuming reaction sites
accommodate a larger unit volume); this clarifies that the
contributing factor toward improved lower limit of detection is the
total sample volume interrogated. For detecting rare event, the
focus should thus be toward higher total sample volume than number
of reaction sites. The plots generated in FIG. 5 show 20,000
reaction sites, as an example. Plot 502 shows concentration versus
precision for a 10 .mu.L reaction volume. Plot 504 shows
concentration versus precision for a 20 .mu.L reaction volume. Plot
506 shows concentration versus precision for a 200 .mu.L reaction
volume. Plot 508 shows concentration versus precision for a 600
.mu.L reaction volume. Plot 510 shows concentration versus
precision for a 1000 .mu.L reaction volume.
Error Modeling
[0047] This section introduces noise factors into the pure Poisson
model. A reaction site with a target molecule that goes undetected
produces a false negative. A reaction site that does not have a
target molecule, but gets classified as a positive reaction
produces a false positive. Possible causes for false negatives
could be an amplification failure, for example. Possible causes for
false calls include contamination, chemistry effects, source sample
related effects, and optical or system noise effects, for example.
As such, a variation component of Equation 5 can be expanded to
include variation from two other factors: [0048] False positive,
false negative call rate [0049] System related bias
[0050] This additional variation is estimated as follows: Let
.lamda..sub.false denote the .lamda. observed because of the false
positive and false negative calls. It is related to the true
.lamda. as shown in equation 9.
.lamda. false = - ln .function. ( e - .lamda. - false .times.
.times. Positive .times. .times. Rate + false .times. .times.
Negative .times. .times. Rate ) ( 9 ) ##EQU00008##
[0051] The fraction of negatives observed is given by equation
10.
p false = exp .function. ( - .lamda. false ) ( 10 )
##EQU00009##
[0052] Using the fraction of negatives given by (10) in equation
(3), the 95% confidence bounds can be found as shown in equation
(11):
Confidence .times. .times. Bound ( Lower , Upper ) False = exp
.function. [ ln .function. ( - ln .times. .times. p false ) .-+. 1
. 9 .times. 6 .times. 1 - p false - ( ln .times. p false ) .times.
n .times. p false ] . ( 11 ) ##EQU00010##
[0053] The variation from sampling and non-zero false positive and
false negative call rates is given as:
.sigma. false .times. .times. calls , sampling = max .function. (
ln .function. ( Confidence .times. .times. Bound ( Upper , Lower )
False ) - ln .function. ( .lamda. false ) 1.96 ) ( 12 )
##EQU00011##
[0054] An arbitrary source of variation related to system noise,
.sigma..sub.systemBias, is pooled along with above variation,
giving the total variation as:
.sigma. total = .sigma. false .times. .times. calls , sampling 2 +
.sigma. SystemBias 2 ( 13 ) ##EQU00012##
[0055] This leads to an expanded confidence bound given by equation
(14).
Confidence Bound.sub.(Lower,Upper)=exp[ln(-ln
p).-+.1.96.sigma..sub.total] (14)
[0056] Expression (14) is substituted into the precision formula in
equation (4) for a more accurate estimate of precision:
Precision .times. = 1 .lamda. .times. ( exp .function. [ ln
.function. ( - ln .times. .times. p ) .-+. 1 . 9 .times. 6 .times.
.sigma. total ] - .lamda. ) ( 15 ) ##EQU00013##
[0057] The impact from false call rates are investigated using
Monte Carlo simulations as follows: Under the influence of zero
false call rates, a load concentration yielding 20% negatives
affords the best precision. But as the false negative rate
increases, it is desirable to target a higher percent negatives for
optimal measurement precision. The lower (upper) limit of detection
is maximally impacted by false positives (negatives).
[0058] FIG. 6 illustrates a graph 600 showing that precision
degrades with false calls (false positives impact the lower end
while false negatives impact the higher end of detectable
concentrations.
[0059] FIG. 7 illustrates example contours useful for determining
how to recover from noise factors by targeting different percent
negatives for best measurement precision. As mentioned above, under
the influence of zero false call rates, the percent negatives
affording best precision is at 20% negatives. However, as the false
negative rate increases, it is desirable to target a higher percent
negatives for optimal measurement precision. The labels of the
contours present load concentration values for best precision in
percent negatives.
[0060] FIG. 8 illustrates a graph showing that lowering the false
positive calls improves the lower limit of detection. The labels of
the contours present the minimum detectable copies/reaction values
at 20% detection precision.
[0061] The impact from reaction dropouts due to a variety of causes
including, but not limited to, quality considerations such as
presence of dust or debris are also investigated using Monte Carlo
simulations.
[0062] FIG. 9 illustrates contours with desired load concentrations
for measurement with best precision when compensating for
positive/negative reaction loss. For an ideal system, the peak
measurement precision was derived to be at 20% negatives. Thus, the
same positive drop rate impacts a larger number of actual reaction
sites for dropped positive reaction sites versus dropped negative
reaction sites around the best measurement precision point. This is
evidenced from the fact that rate of change is faster with increase
in the positive drop rate versus increase in the negative drop
rates. To recover from this effect, simulations suggest moving to
higher loading sample concentrations for both positive and negative
reaction drops. The labels of the contours present load
concentration values for best precision in percent negatives.
[0063] FIG. 10 illustrates a lower bias toward rejection of
negative reactions to reduce impact to the lower limit of
detection. The labels of the contours present the minimum
detectable copies/reaction values at 20% precision.
[0064] The effect of volumetric variation among reaction sites on
estimating the concentration was investigated with Monte Carlo
simulations. More volume is represented by increased probability of
a reaction site containing a molecule. Normal distribution of
volume variation is assumed with the standard deviation taken as a
percentage of the mean volume.
[0065] FIG. 11 illustrates the volumetric variations in reaction
volumes deteriorate measurement capability at higher concentrations
of the target.
[0066] FIG. 12 illustrates the target load in percent negatives for
best precision to compensate for partition size variation.
Extending the Dynamic Range Using Dilutions
[0067] The error modeling in the previous section showed how the
theoretical dynamic range is depressed by noise factors. One way to
mitigate this problem and enhance the dynamic range from digital
PCR experiments is by running one or more dilution points. FIG. 13
illustrates an exemplary dPCR workflows. Sample 302 may be
partitioned into a plurality of reaction sites as shown in
substrate 304. Sample 302 may be diluted at least once and
partitioned into reaction sites. In FIG. 13, sample 302 is diluted
once and loaded into a set of reaction sites in substrate 306.
Further, the sample may be diluted a second time and loaded into a
second set of reaction sites in substrate 308. Sample 302 may be
diluted a third time and loaded into a third set of reaction sites
in substrate 310. In examples, at least one dilution is performed
on a sample to increase dynamic range and precision, according to
various embodiments of the present teachings.
[0068] FIG. 14 illustrates the effect of dilutions on precision.
Dilutions help detect samples of concentrations higher than a
supported range, but may put samples near the lower limit of
detection outside of supported range. Further, dilutions used in
various combination of dilutions extend the dynamic range with the
original concentration of a sample preserving the detection of the
rare target and the dilution point enabling the detection the
abundant targets. FIG. 15 illustrates a plot showing the effect of
dilution on the lower limit of detection and the dynamic range for
a single substrate when half the reaction sites are donated to a
second dilution point.
[0069] The impact on the lower limit of detection due to splitting
of available reactions between two dilutions is illustrated as
follows
.lamda. LLOD_Diluted = - ln .function. ( ( 1 - C ) 2 / n ) = - 2 *
ln .function. ( ( 1 - C ) 1 / n ) = 2 * .lamda. LLOD > .lamda.
LLOD ( 16 ) ##EQU00014##
[0070] The impact on the upper limit of detection due to splitting
of available reactions between two dilutions is illustrated as
follows:
.lamda. ULOD_Diluted = - ln .function. ( 1 - ( 1 - C ) 2 / n ) <
- ln .function. ( 1 - ( 1 - C ) 1 / n ) = .lamda. ULOD ( 17 )
##EQU00015##
[0071] FIG. 17 illustrates an example where the greater dynamic
range afforded with one additional dilution using 20K reaction
sites. In this example, the original sample was run with an
additional dilution point, to take advantage of detection range
from the original concentration point and the detection range from
the dilution point. However, as shown in equation 16 and 17, if
splitting available reactions between dilutions, there will be
slight rise in lower limit of detection due to fewer available
reaction sites dedicated to sample volumes at the original
concentration. However, higher concentrations are now detectable
from the set of reactions with the diluted sample. To try to
achieve meeting the precision requirement from the system for any
answer, the upper x % of the dynamic range from the original
concentration point is overlapped with the lower y % of the dynamic
range from the dilution point, ensuring continuous detection at
required precision.
[0072] FIGS. 16A and 16B illustrates the limit on dilution factors
described above. Let the two dilution points be named dilution
points 1 and 2, where dilution point 1 is more concentrated than
dilution point 2. For a continuous detection ability between the
two dilution points, the second dilution point can have its lower
limit of detection less than or equal to the upper limit of
detection from the first dilution point. Otherwise, there may be a
discontinuous gap as indicated in FIGS. 16A and 16B. This indicates
there is a limit on the lowest concentration one may dilute to if
one needs to be able to continuously detect between the LLOD of the
first dilution point and the ULOD of the second dilution point.
[0073] There exists a tradeoff between the required precision, the
lower limit of detection, and the use of additional dilutions to
extend the dynamic range. FIG. 18A illustrates how additional
dilutions can extend the dynamic range from the system beyond using
just two dilutions. 20K reaction sites were split in equal
partitions in this simulation. FIG. 18B shows the impact to the
lower limit of detection as wells from the initial dilution get
distributed to additional dilution points. It can be seen that more
than two dilution points at 5% precision produces limited
additional dynamic range. However, as the precision requirement is
dropped, substantial gains in dynamic range are possible with more
dilution points. These gains in dynamic range are conditional upon
a willingness to accept a deterioration to the lower limit of
detection. Also, performing the dilutions may introduce additional
source of variation, which could in turn limit the effective
precision of the system. FIG. 18C shows the effect of introducing
four dilution points with 5% and 10% precision requirements as
examples.
[0074] Using the foregoing teachings, methods may be implemented by
a computing system to provide a dPCR experiment designer tool to a
user according to various embodiments of the present teachings. A
user may be able to more easily plan a desired experiment based on
the outputs provided by the dPCR experiment designer. Further,
after both the Dynamic Range Expansion related dilution factors or
the Target Digital PCR related dilution factors are estimated, a
further set of calculations are employed to suggest
stock-to-reaction mix dilution factors to convert from stock
concentration to targeted dPCR reaction mix dilution. These
calculations are described in the following section.
Stock-to-Reaction Mix Dilution Factors--Stock Concentration to
Targeted Dpcr Dilution Factor
[0075] According to various embodiments, a dPCR experiment designer
may further be used to calculate stock-to-reaction mix dilution
factors for diluting a stock sample to a targeted dPCR dilution
factor, also calculated by the dPCR experiment designer. In other
words, a dPCR experiment designer may further assist a user in
performing a desired experiment by providing additional dilution
factors for a user to dilute a stock solution of a known
concentration to the desired concentration based upon dynamic range
and/or precision requirements, for example.
[0076] Calculation of the stock-to-reaction mix dilution factors
are based on parameters such as the desired volume of the dPCR
reaction, the concentrations of reaction reagents, and minimum
pipette volumes for both sample and reaction reagents. Furthermore,
the stock-to-dilution dilution factors may be further based on the
appropriate volumes of each of the reaction components to add to
the reaction mix in order to get the stock sample to the targeted
dPCR reaction mix dilution. The stock-to-reaction mix dilution
factors may also be based on the minimum pipette volumes in order
to determine any initial dilutions of the sample or assays (prior
to their addition to the reaction mix) necessary to achieve the
target dPCR reaction mix dilution of the sample. A minimum pipette
volume may be needed to consider because the capability of a
pipette, such as the limitations of volume to be dispensed
accurately from a pipette, may affect the user's ability to prepare
a sample. Taking into account these factors, the user may need to
input the following parameters, for example, into the dPCR
experiment designer to calculate the stock-to-reaction mix dilution
factors according to various embodiments.
[0077] Input Parameters [0078] Targeted dPCR Dilution Factor in
dPCR Reaction Mix for the Sample [0079] Minimum Sample Pipet volume
[0080] Minimum Reagent Pipet volume [0081] Desired Total Reaction
volume [0082] Non-diluted Master Mix concentration [0083] Assay
List [0084] Non-diluted Assay concentrations
[0085] The results of the stock-to-reaction mix dilution portion of
the dPCR experiment designer may be a list of reaction component
volumes (and any necessary pre-dilution factors) to be added to the
reaction mix which produces the targeted dPCR reaction mix dilution
of the sample according to various embodiments. The provided
component volumes by the dPCR experiment designer may satisfy the
minimum pipette volume constraints. For example, the output of the
dPCR experiment designer may include, but is not limited, to the
following:
[0086] Output [0087] Initial Sample Dilution Factor (outside of
dPCR Reaction Mix) [0088] Initial Assay Dilution Factor (outside of
dPCR Reaction Mix) [0089] Volume and final concentration in mix for
[0090] Master Mix [0091] Initially Diluted Assays [0092] Initially
Diluted Sample [0093] Water [0094] Total Volume
[0095] According to various embodiments, method to determine the
stock-to-reaction mix dilution factors includes a first step of
checking if the final sample dilution factor is possible. The
second step may include calculating the initial dilution factor of
the sample and assays. The third step may include setting the test
volume as the desired volume for the experiment. The fourth step
may determine various parameters until the assay concentrations are
equal to 1.times.. The fifth step may include providing the results
to the user, including: initial dilution factor of sample, initial
dilution factor of assay(s), final master mix volume, final assay
volume(s), final sample volume, and final water volume. An example
of the method determining the stock-to-reaction mix dilution
factors is as follows:
[0096] Step 1: Check if Final Sample Dilution Factor possible.
[0097] a. Compute volume left over after reaction reagents have
been accounted for. [0098] b. If Final Sample Dilution Factor
greater than percentage of remaining volume, return
[0099] Step 2: Calculate Initial Dilution Factor of Sample and
Assays [0100] a. Calculate Minimum Reaction Volume needed based on
Reagent Concentrations and minimum pipette volume for reagents
[0101] b. Calculate initial Master Mix volume [0102] c. Calculate
initial Assay volumes [0103] d. Determine if Sample volume at
desired dilution fits in remaining volume [0104] i. If so, conduct
simple Sample volume computation fitting Sample into remaining
volume [0105] ii. If not, compute surplus Sample volume needed to
achieve Final Sample Dilution Factor [0106] e. Calculate initial
Dilution Factor of Sample based on computed Sample volume in 2d
[0107] f. Calculate initial Dilution Factor of Assay(s) based on
Desired Total Reaction Volume, Minimum Reaction Volume, and Final
Sample Dilution Factor
[0108] Step 3: Set Test Volume=Desired Volume
[0109] Step 4: Cycle until Reagent Concentrations equal 1.times.
[0110] a. Calculate Final Master Mix volume [0111] b. Calculate
test Assay volume(s) [0112] c. Calculate test Sample volume [0113]
d. Calculate test Water volume [0114] e. Recalculate Initial
Dilution Factor of Sample [0115] f. Recalculate Initial Dilution
Factor of Assay(s) [0116] g. Recalculate Final Assay volume(s)
based on recomputed Initial Dilution Factor of Assay(s) [0117] h.
Recalculate Final Sample volume based on recomputed Initial
Dilution Factor of Sample and Minimum Sample Pipette volume. [0118]
i. Compute Final Water volume [0119] j. Compute test Final Volume
(max of Test Volume and sum of Master Mix, Assay, Sample, and Water
volumes) [0120] k. If any Assay concentration not equal to 1,
increment the Test Volume
[0121] Step 5: Return Initial Dilution Factor of Sample, Initial
Dilution Factor of Assay(s), Final Master Mix volume, Final Assay
volume(s), Final Sample volume, and Final Water volume.
dPCR Experiment Designer Uses
[0122] The dPCR experiment designer is a tool built based on the
above digital PCR model has three typical workflows for digital PCR
experiments. If a user has alternate information in terms of a
nanodrop readings or a Ct value from a previous qPCR experiment,
the dPCR experiment designer can be used to calculate the target
digital PCR dilution factor by inputting that information. Further,
the dPCR experiment designer may generate a recommendation for the
user for the reaction mix for the digital PCR experiment.
[0123] Alternately, the dPCR experiment designer can be used to
generate recommendations for the PCR mix for a digital PCR
experiment performed across two substrates at two different
concentrations. This would support a gene expression quantification
workflow across a desired dynamic range, for example.
[0124] For rare target detection, the dPCR experiment designer can
provide recommendations for the number of substrates, each
including a predetermined number of reaction sites, needed to
detect a desired fold change at a certain confidence level. This
would support, as an example, a rare mutation detection workflow
using a dual reporter SNP assay.
[0125] FIG. 20A and FIG. 20B illustrate flowchart 2000 examples of
3 different workflows supported by the digital PCR experiment
designer tool according to various embodiments described herein. A
workflow includes types of experiments a user may want to perform
using dPCR. The dPCR experiment designer may help a user plan a
desired experiment.
[0126] Workflows included in a dPCR experiment designer may include
a rare mutation workflow 2004, an optimize detection attributes of
a dPCR experiment for absolute quantification workflow 2006, and
use of qPCR or NanoDrop data to estimate dilution factors for a
dPCR experiment workflow 2008. According to various embodiments,
the dPCR experiment designer allows a user to select the type of
problem the user is trying to solve in step 2002. In other words,
the user may select a workflow.
[0127] As an example, a user may select the rare mutation workflow
2004. The dPCR experiment designer may then lead the user to input
the needed information to design an experiment. For example, in
step 2010, the user will be asked to select the type of wild-type
concentration they have. If the user has NanoDrop concentrations,
the user will be asked to select the information about the genome
that is known in step 2012, such as the diploid genome weight or
the genome size and ploidy. If the user has qPCR readings as the
source of wild-type concentration, the user will be queried to
select whether the Ct values were derived with or without dilution
series in step 2014.
[0128] Then, in step 2016, the user will be asked to select how
they would like to constrain the lower limit of detection. The user
may want to set the false positive distribution or set the lower
limit of detection, for example.
[0129] The user may then input the needed information based on the
NanoDrop concentration, single Ct, or dilution series used, for
example, in step 2018. The user may also provide other advanced
inputs in step 2020, such as the type of instrument used, the false
positive rate, and the false negative rate.
[0130] Then, according to various embodiments, the user will be
provided with results information in step 2024 including, but not
limited to, wild type dilution information, dPCR set-up
information, interactive graphs, and/or stock solution set up
information. The user may then use this information to perform the
desired rare mutation experiment.
[0131] FIG. 22A-22D illustrate a user interface displayed to a user
implementing the rare mutation workflow 2004 of the dPCR experiment
designer according to various embodiments described by the present
teachings. FIGS. 22A-22D are described in more detail below.
[0132] In the optimize detection attributes of a dPCR experiment
for absolute quantification workflow 2006, the user is asked to
select the purpose of the experiment in step 2030. For example, the
user may select maximize dynamic range, minimize the number of
chips of reaction sites, calculate dilution factors, and/or
calculate the lower limit of detection. If the user selects the
purpose is to maximize dynamic range, the user is asked to select
how they would like to constrain the dynamic range in step 2032.
Depending on the purpose the user selects, the user inputs
different information in step 2034. The user may also provide
advanced inputs in step 2036, such as the type of instrument used,
the number of chips (including a known number of reaction sites)
used at a certain dilution, the false positive rate, and false
negative rate. The results are provided to the user in step
2038.
[0133] FIG. 21A-21D illustrate a user interface of the dPCR
experiment designer displayed to a user implementing optimize
detection attributes workflow 2006 according to embodiments
described by the present teachings. FIGS. 21A-21D are described in
more detail below.
[0134] In the use qPCR or NanoDrop data to estimate the dilution
factor for your digital experiment workflow 2008, the user will be
asked to input the type of data they have in step 2050. For
example, if the user has NanoDrop data, the user will be asked to
input the type of diploid genome weight and the genome size and
ploidy in step 2052. If the user has qPCR data, the user will be
asked to select whether the Ct values derived was with or without
dilution series in step 2054. Next, the user will be asked to
select the type of experiment in step 2056. The types of
experiments may be singleplex, duplex, SNP assay, or custom, for
example. The user may be asked to input other information depending
on the information selected in the previous queries in step 2058.
Further, in step 2058, the user may also be asked to input
parameters needed to determine stock-to-reaction mix dilution
factors. In step 2060, the user may provide other advanced inputs.
In step 2062, the user may be provided the results based on the
type of data they used, qPCR or NanoDrop, for example.
[0135] FIG. 21A, FIG. 21B, FIG. 21 C, and FIG. 21D illustrate an
example of a method including steps for a gene expression
quantification workflow using a dPCR experiment designer. The
measurement precision requirement and the minimum copies/reaction
inputs are used to estimate a dynamic range expansion dilution
factor using the digital PCR model.
[0136] FIG. 22A, FIG. 22B, FIG. 22C, and FIG. 22D illustrate the
steps of a rare mutation detection experiment design using the
digital PCR experiment designer. In one example, a user can first
run a qPCR experiment or a nanodrop reading to quantify the wild
type target present in their sample. This information can be used
by the dPCR experiment designer to estimate the target digital PCR
dilution factor to enable detection of the background at a desired
copies/reaction. Additionally, the dPCR experiment designer allows
a user to input what they can experimentally determine about the
false positive distribution for the assay and system. The user can
then run non-target controls and determine a mean and standard
deviation of the number of false positives typically seen in an
experiment. This information, along with a target fold change and
target p-value can allow the dPCR experiment designer to estimate
the number of chips necessary to detect the rare event at the
desired confidence level above background using the digital PCR
model.
Quantification Results
[0137] This section demonstrates quantifying anywhere between 1 to
1e6 copies per .mu.l on a dPCR system. In this example, the
QuantStudio 3D from Life Technologies with a two chip two dilution
strategy is used. In terms of the model, the requirements are 6
logs of dynamic range (DR), with a lowest limit of detection at 1
copy/.mu.l. Using a 0.025% false positive rate and a 0.05% false
negative rate, the dPCR experiment designer provides a
recommendation of a dilution factor of 0.001.
[0138] Samples AA to GG, 6 logs apart, are at the concentrations
given in the table below on the undiluted and diluted pairs of
chips. The concentrations marked in the table below with double
asterisk (**) were run on the system. Because this is a simulated
example, the concentrations that would not be detectable by this
system were not run.
TABLE-US-00001 Expected copies per Expected copies per
Copies/Reaction Sample .mu.liter at conc .mu.liter at .001 dilution
from the actual runs AA 1000000 1000 ** -- BB 100000 100 ** -- CC
10000 10 ** -- DD 1000 ** 1 ** 1.4986 EE 100 ** .1 0.1116 FF 10 **
.01 0.0101 GG 1 ** .001 0.0010
[0139] FIG. 23A illustrates the quantification results based on the
model described above, according to embodiments described herein.
Here, samples AA, BB and CC were accurately quantified on the
dilute chip, while samples EE, FF and GG were accurately quantified
on the undiluted chip. Sample DD was quantified using both data
points. FIG. 23B projects the sample on the modeling schema used in
the dPCR experiment designer according to various embodiments
described herein.
Ratio Estimation Results
[0140] The following section demonstrates detecting rare targets
against a background signal using computer simulated data with the
two chip two dilution strategy. A 1:1000 ratio translates to 3 logs
of dynamic range requirement. The lowest limit of detection was set
at 10 copies per micro liter. System parameters for the model were
chosen according to the Life Technologies QuantStudio 12K Flex.
Using a 0.07% false positive rate and a 0.18% false negative rate,
the system recommended a dilution factor of 0.005 for detecting at
each target better than 30% precision.
[0141] FIG. 24 illustrates a model showing rare target estimation
in the presence of 1000 fold background. Sample A was simulated
with 10000 copies/.mu.l of the abundant target and 10 copies/.mu.l
of the rare target. By sub-sampling from this data over a large
number of iterations, at both the undiluted and the diluted
configurations, the wild type was detected at the dilute point at
6.95% precision, while the rare target was detected at the
undiluted point at 4.49% precision. The ratio was accurately
predicted at 0.001.
Computer System
[0142] Those skilled in the art will recognize that the operations
of the various embodiments may be implemented using hardware,
software, firmware, or combinations thereof, as appropriate. For
example, some processes can be carried out using processors or
other digital circuitry under the control of software, firmware, or
hard-wired logic. (The term "logic" herein refers to fixed
hardware, programmable logic and/or an appropriate combination
thereof, as would be recognized by one skilled in the art to carry
out the recited functions.) Software and firmware can be stored on
computer-readable media. Some other processes can be implemented
using analog circuitry, as is well known to one of ordinary skill
in the art. Additionally, memory or other storage, as well as
communication components, may be employed in embodiments of the
invention.
[0143] FIG. 19 is a block diagram that illustrates a computer
system 1900 that may be employed to carry out processing
functionality, according to various embodiments of the dPCR
experiment designer. Computing system 1900 can include one or more
processors, such as a processor 1904. Processor 1904 can be
implemented using a general or special purpose processing engine
such as, for example, a microprocessor, controller or other control
logic. In this example, processor 1904 is connected to a bus 1902
or other communication medium.
[0144] Further, it should be appreciated that a computing system
1900 of FIG. 19 may be embodied in any of a number of forms, such
as a rack-mounted computer, mainframe, supercomputer, server,
client, a desktop computer, a laptop computer, a tablet computer,
hand-held computing device (e.g., PDA, cell phone, smart phone,
palmtop, etc.), cluster grid, netbook, embedded systems, or any
other type of special or general purpose computing device as may be
desirable or appropriate for a given application or environment.
Additionally, a computing system 1900 can include a conventional
network system including a client/server environment and one or
more database servers, or integration with LIS/LIMS infrastructure.
A number of conventional network systems, including a local area
network (LAN) or a wide area network (WAN), and including wireless
and/or wired components, are known in the art. Additionally,
client/server environments, database servers, and networks are well
documented in the art.
[0145] Computing system 1900 may include bus 1902 or other
communication mechanism for communicating information, and
processor 1904 coupled with bus 1902 for processing
information.
[0146] Computing system 1900 also includes a memory 1906, which can
be a random access memory (RAM) or other dynamic memory, coupled to
bus 1902 for storing instructions to be executed by processor 1904.
Memory 1906 also may be used for storing temporary variables or
other intermediate information during execution of instructions to
be executed by processor 1904. Computing system 1900 further
includes a read only memory (ROM) 1908 or other static storage
device coupled to bus 1902 for storing static information and
instructions for processor 1904.
[0147] Computing system 1900 may also include a storage device
1910, such as a magnetic disk, optical disk, or solid state drive
(SSD) is provided and coupled to bus 1902 for storing information
and instructions. Storage device 1910 may include a media drive and
a removable storage interface. A media drive may include a drive or
other mechanism to support fixed or removable storage media, such
as a hard disk drive, a floppy disk drive, a magnetic tape drive,
an optical disk drive, a CD or DVD drive (R or RW), flash drive, or
other removable or fixed media drive. As these examples illustrate,
the storage media may include a computer-readable storage medium
having stored therein particular computer software, instructions,
or data.
[0148] In alternative embodiments, storage device 1910 may include
other similar instrumentalities for allowing computer programs or
other instructions or data to be loaded into computing system 1900.
Such instrumentalities may include, for example, a removable
storage unit and an interface, such as a program cartridge and
cartridge interface, a removable memory (for example, a flash
memory or other removable memory module) and memory slot, and other
removable storage units and interfaces that allow software and data
to be transferred from the storage device 1910 to computing system
1900.
[0149] Computing system 1900 can also include a communications
interface 1918. Communications interface 1918 can be used to allow
software and data to be transferred between computing system 1900
and external devices. Examples of communications interface 1918 can
include a modem, a network interface (such as an Ethernet or other
NIC card), a communications port (such as for example, a USB port,
a RS-232C serial port), a PCMCIA slot and card, Bluetooth, etc.
Software and data transferred via communications interface 1918 are
in the form of signals which can be electronic, electromagnetic,
optical or other signals capable of being received by
communications interface 1918. These signals may be transmitted and
received by communications interface 1918 via a channel such as a
wireless medium, wire or cable, fiber optics, or other
communications medium. Some examples of a channel include a phone
line, a cellular phone link, an RF link, a network interface, a
local or wide area network, and other communications channels.
[0150] Computing system 1900 may be coupled via bus 1902 to a
display 1912, such as a cathode ray tube (CRT) or liquid crystal
display (LCD), for displaying information to a computer user. An
input device 1914, including alphanumeric and other keys, is
coupled to bus 1902 for communicating information and command
selections to processor 1904, for example. An input device may also
be a display, such as an LCD display, configured with touchscreen
input capabilities. Another type of user input device is cursor
control 1916, such as a mouse, a trackball or cursor direction keys
for communicating direction information and command selections to
processor 1904 and for controlling cursor movement on display 1912.
This input device typically has two degrees of freedom in two axes,
a first axis (e.g., x) and a second axis (e.g., y), that allows the
device to specify positions in a plane. A computing system 1900
provides data processing and provides a level of confidence for
such data. Consistent with certain implementations of embodiments
of the present teachings, data processing and confidence values are
provided by computing system 1900 in response to processor 1904
executing one or more sequences of one or more instructions
contained in memory 1906. Such instructions may be read into memory
1906 from another computer-readable medium, such as storage device
1910. Execution of the sequences of instructions contained in
memory 1906 causes processor 1904 to perform the process states
described herein. Alternatively hard-wired circuitry may be used in
place of or in combination with software instructions to implement
embodiments of the present teachings. Thus implementations of
embodiments of the present teachings are not limited to any
specific combination of hardware circuitry and software.
[0151] The term "computer-readable medium" and "computer program
product" as used herein generally refers to any media that is
involved in providing one or more sequences or one or more
instructions to processor 1904 for execution. Such instructions,
generally referred to as "computer program code" (which may be
grouped in the form of computer programs or other groupings), when
executed, enable the computing system 1900 to perform features or
functions of embodiments of the present invention. These and other
forms of computer-readable media may take many forms, including but
not limited to, non-volatile media, volatile media, and
transmission media. Non-volatile media includes, for example, solid
state, optical or magnetic disks, such as storage device 1910.
Volatile media includes dynamic memory, such as memory 1906.
Transmission media includes coaxial cables, copper wire, and fiber
optics, including the wires that comprise bus 1902.
[0152] Common forms of computer-readable media include, for
example, a floppy disk, a flexible disk, hard disk, magnetic tape,
or any other magnetic medium, a CD-ROM, any other optical medium,
punch cards, paper tape, any other physical medium with patterns of
holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip
or cartridge, a carrier wave as described hereinafter, or any other
medium from which a computer can read.
[0153] Various forms of computer readable media may be involved in
carrying one or more sequences of one or more instructions to
processor 1904 for execution. For example, the instructions may
initially be carried on magnetic disk of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem. A
modem local to computing system 1900 can receive the data on the
telephone line and use an infra-red transmitter to convert the data
to an infra-red signal. An infra-red detector coupled to bus 1902
can receive the data carried in the infra-red signal and place the
data on bus 1902. Bus 1902 carries the data to memory 1906, from
which processor 1904 retrieves and executes the instructions. The
instructions received by memory 1906 may optionally be stored on
storage device 1910 either before or after execution by processor
1904.
[0154] It will be appreciated that, for clarity purposes, the above
description has described embodiments of the invention with
reference to different functional units and processors. However, it
will be apparent that any suitable distribution of functionality
between different functional units, processors or domains may be
used without detracting from the invention. For example,
functionality illustrated to be performed by separate processors or
controllers may be performed by the same processor or controller.
Hence, references to specific functional units are only to be seen
as references to suitable means for providing the described
functionality, rather than indicative of a strict logical or
physical structure or organization.
[0155] Although the present invention has been described with
respect to certain exemplary embodiments, examples, and
applications, it will be apparent to those skilled in the art that
various modifications and changes may be made without departing
from the invention.
* * * * *