Method for detecting reaction volume deviations in digital polymerase chain reaction

Abstract

The present disclosure relates to a method for detecting reaction volume deviations in digital polymerase chain reaction (dPCR) and a method for determining the amount or concentration of a nucleic acid of interest in a sample using dPCR. The method analyzes an optical image using convolution with a kernel function, and each partition is assigned a convolution value that is compared to a convolution threshold.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Patent Application No. 63 / 018183, filed April 30, 2020. The disclosures of the referenced applications are incorporated herein by reference.

[0002] Field of Disclosure The present disclosure relates to methods for detecting reaction volume variations in digital polymerase chain reaction (dPCR) and dPCR methods for determining the amount or concentration of a nucleic acid of interest in a sample that accounts for the reaction volume variations. [Background technology]

[0003] background Many biological, biochemical, diagnostic, or therapeutic purposes require precise and accurate determination of the quantity or concentration of nucleic acids in a sample. Digital PCR offers an alternative to traditional real-time quantitative PCR for absolute quantification of nucleic acids and rare allele detection. Digital PCR works by dividing a nucleic acid sample into many individual parallel PCR reactions, some of which contain the target molecule (positive) and others not (negative). Following PCR analysis, the proportion of negative reactions is used to generate an absolute count of the number of target molecules in the sample. One of the key advantages of dPCR over real-time PCR is its superior quantitative accuracy. This advantage relies on the inherent properties of dPCR, since quantification requires only an accurate count of the positive partitions (or reaction volumes) and knowledge of the theoretical partition volumes (count numbers are not very sensitive to PCR efficiency). No quantification standards are required, eliminating potential quantification errors caused by the standards themselves.

[0004] Prior art provides methods for identifying false positive or negative counts and calibrating or normalizing signals in droplet-based assays (U.S. Patent Application Publication No. 2013 / 0302792). This normalization should improve the separation between positive and negative counts. Thus, normalization reduces the risk of false positive or false negative counts. The ultimate goal is to improve the precision and accuracy of nucleic acid concentration determination by correcting the signal obtained for nucleic acids.

[0005] However, prior art methods do not account for PCR errors resulting from situations in which the true volume within a dPCR partition differs from that expected or intended. Therefore, there is a need for a method for quantifying nucleic acids of interest by dPCR that accounts for reaction volume deviations. Summary of the Invention

[0006] overview The present disclosure provides a method for detecting reaction volume deviations in a dPCR assay, wherein the dPCR assay is used to quantify the amount or concentration of a nucleic acid of interest in an array of partitions. The method comprises the following steps: (a) combining optical signals across (x, y) coordinates in the array using convolution with a kernel function, where each partition is assigned a convolution value; and (b) identifying valid and empty partitions by comparing the convolution value of each partition with a convolution threshold; Includes.

[0007] Additionally, the method may also include (c) subjecting the data collected in step (b) to one or more additional steps, including clustering operations and morphological image processing operations. Such morphological image processing operations may include dilation and / or erosion, and the clustering may include valid and / or empty trimming.

[0008] The present disclosure also contemplates a method for determining the amount or concentration of a nucleic acid of interest in a sample, the method comprising the steps of: (a) providing a sample suspected of containing the nucleic acid of interest; (b) performing dPCR with the sample in a dPCR plate comprising an array of partitions; (c) identifying one or more valid partitions in the array of partitions; and (d) calculating the amount or concentration of the nucleic acid of interest as the number of nucleic acids determined in step (b) per valid partition volume. Additionally, the method also includes the step of calculating the copy number N of the nucleic acid of interest in the one or more valid partitions identified in step (c). c and determining N c by the effective partition volume.

[0009] In certain embodiments, the present disclosure provides laboratory equipment adapted to perform the steps of the methods described herein, as well as computer program products comprising instructions to cause the laboratory equipment to perform the steps of the methods, and computer readable media having stored thereon the computer program products described herein. [Brief explanation of the drawings]

[0010] [Figure 1A] Figures 1A-1B show a schematic of the method described herein for detecting reaction volume deviations in dPCR assays. Figure 1A shows the method used to analyze dPCR plates after nucleic acid amplification, and Figure 1B shows the complete method from dPCR plate preparation to analysis using the method described herein. [Figure 1B] Figures 1A-1B show a schematic of the method described herein for detecting reaction volume deviations in dPCR assays. Figure 1A shows the method used to analyze dPCR plates after nucleic acid amplification, and Figure 1B shows the complete method from dPCR plate preparation to analysis using the method described herein. [Figure 2] FIG. 2 is a schematic diagram of the experimental setup described herein. DETAILED DESCRIPTION OF THE INVENTION

[0011] Detailed Description As detailed above, a method for reliably determining the amount or concentration of nucleic acids is particularly relevant in some industrial applications, for example, in the medical field. Such applications may require accurate and precise determination of the amount or concentration of nucleic acids in a sample, for example, a sample obtained from a patient or a product. This may be of interest, for example, in diagnosing the severity of a disease, in environmental technology, or as a means of determining the quality of a product, for example, to identify contamination or impurities.

[0012] Digital PCR is a biotechnology variant of conventional polymerase chain reaction (dPCR) that can be used to directly quantify and optionally clonal amplify nucleic acids, including DNA, cDNA, RNA, or mixtures thereof. The primary difference between dPCR and conventional PCR (e.g., qPCR) lies in the method of measuring nucleic acid content. While the former is more accurate and precise than PCR, it is also more prone to error in the hands of inexperienced users. The smaller dynamic range of dPCR may require sample dilution. While dPCR also performs a single reaction within a sample, the sample is separated into multiple partitions, and reactions are performed individually in each partition. This separation allows for more reliable collection and sensitive measurement of nucleic acid content. Furthermore, this method allows for precise quantification.

[0013] Detailed descriptions of dPCR devices and methods can be found, for example, in U.S. Patent Application Publication No. 20080160525, Vogelstein, et al., Proc. Natl. Acad. Sci. USA, Vol. 96, 9236-9241, August 1999; McCaughan, et al., J. Pathol. 2010; 220:297-306; Mao, et al., Am. J. Transl. Res. 2019; 11(12):7209-7222;; U.S. Patent No. 10,564,102; U.S. Patent Application Publication No. 20180045641; European Patent No. 3299471; U.S. Patent Application Publication No. 20180147574; and U.S. Patent Application Publication No. 20180087090. The disclosure of each of these publications is incorporated herein by reference in its entirety.

[0014] In a specific embodiment, a dPCR sample is separated or distributed within an array containing multiple partitions (also called reaction volumes or wells) so that individual nucleic acid molecules within the sample are localized and concentrated within many distinct regions within the array. This sample distribution allows the number of nucleic acids to be estimated by assuming that the target molecule counts within each partition follow a consistent Poisson distribution. After PCR amplification, each partition is identified as a negative or positive reaction (0 or 1 or more molecules, respectively). Target molecules can be quantified by counting the number of positive and negative partitions and then estimating the underlying Poisson distribution using maximum likelihood estimation. In conventional quantitative PCR, quantification results can depend on the amplification efficiency of the PCR process. However, dPCR does not rely on the number of amplification cycles to determine the initial sample volume, eliminating reliance on uncertain exponential data to quantify target nucleic acids and therefore providing absolute quantification.

[0015] As samples are distributed within the array, the distribution process can result in partially filled or unfilled partitions, i.e., reaction volume deviations; partially filled or unfilled partitions are alternatively referred to as empty or filled-empty. Fluorescent signals from empty partitions can be difficult to distinguish from signals observed from otherwise filled partitions that contain low or no target molecules. Furthermore, bright spots can be observed in empty partitions, which can be mistaken for positive signals from filled partitions.

[0016] These problems can be addressed using the disclosed method, which is shown schematically in Figure 1A. Briefly, regions of the dPCR plate where the signal is consistently low across all channels can be analyzed to identify and distinguish voids in the signal data. Analysis of these low signal regions involves the following: a) Feature computation 101 by combining optical signals across (x, y) coordinates in the array using convolution with a kernel function to assign a convolution value within each partition; b) identifying valid and empty partitions by comparing the convolution value of each partition with a convolution threshold 102; c) optionally, an additional clean-up step 103 comprising subjecting the data collected in step (b) to one or more additional steps including clustering operations and morphological image processing operations. Includes.

[0017] Each step of the method is described in more detail below with particular reference to an exemplary dPCR plate, shown in FIG. 1B.

[0018] Briefly, in certain embodiments, a dPCR plate can contain one to eight reaction mixtures (samples) in a standard microwell plate format (SBS format, not shown). Each of the one to eight sample locations in a dPCR plate can consist of an inlet port, e.g., position A1 of the plate, a microstructured portion between positions A1 and A12, and an outlet port at position A12. Once a volume of fluid sample is added to each inlet port, a distribution fluid is added to each inlet. This can be done manually using a single- or eight-channel pipette, or using an automated distribution station. The separation or distribution fluid is a hydrophobic liquid, e.g., a long-chain fluorinated hydrocarbon or silicone oil, that is immiscible and unreactive with the reaction mixture. Separation (or distribution) of individual partitions containing the reaction mixture can be achieved passively, by applying overpressure to the inlet port, or by applying negative pressure to the outlet port. Monitor sensors can be used to ensure the process stops when separation is complete, i.e., when the separation fluid reaches the outlet port. The sample preparation process is shown in Figure 1B, 104.

[0019] After partitioning, the dPCR plate is subjected to a thermal cycling process 105, followed by image analysis to detect fluorescent signals associated with individual partitions within the array. Signal data from the preliminary image analysis step is collected 106. Table 1 summarizes the data inputs used in the methods described herein: [Table 1]

[0020] The first step in detecting reaction volume deviations is feature computation 107. The purpose of feature computation is to combine signals in a way that better reveals the separation between empty and valid partitions. Signals are first combined across channels and then convolved with a kernel based on the (x, y) coordinates. Signals are convolved with a kernel to refine or "smooth" the image. In one embodiment, channels can be convolved individually, or the sum of normalized signals across all of the channels can be convolved.

[0021] In certain embodiments, signals are summed to highlight cases where a partition is dim in all channels, not just one. Signal values ​​are normalized within each channel to ensure that all partitions contribute equally. In one embodiment, only those channels with a significant proportion of positive signal are used in the calculation. Channels are specified using the useChannel flag, i.e., Flag パーティション =1 (enabled) to be selected for use. The signal sum is calculated as follows: Signal channel = Fluorescence (channel) パーティション

number

[0022] Convolution is used to distinguish between dim regions and dim partitions. Convolution involves several steps: first, a distance and kernel function are established. Standard L2 distance and custom exponential kernels can be used.

number

number

[0023] For notation, z represents the (x, y) coordinate of a given partition, and the convolution of z is represented by:

number

[0024] Note that if {isValidPartition(z')^Dist(z, z') <= radius} is the empty set, this value is not well-defined. In that case, the result is set to an impossible default value, e.g., -1. In general, the focus of this method is on convolving valid partitions, so the use of such an invalid default value is acceptable.

[0025] A threshold is set on the convolution value to initially distinguish between empty and valid partitions, 108. Partitions with convolution values ​​below the threshold are marked as empty, while partitions with convolution values ​​above the threshold remain valid. The method used to determine the threshold involves analyzing candidate reference regions within a dPCR plate to select those that represent the filled portion of the plate, and then setting a threshold based on the convolution values ​​within that region.

[0026] There are many potential reference regions that can be drawn on a dPCR plate. Because analyzing all potential reference regions would be slow, a subset is selected, six reference regions in this example. The ideal number of candidate reference regions may vary depending on the plate size and partition density. Six regions are used on a representative dPCR plate for illustrative purposes: (maxx and maxy are the maximum x and y coordinates of a partition)

[0027] For 0≦i<4,

number

[0028] For i=0.1,

number

[0029] For each reference region, the average of the effective convolutions is calculated for that region (value not equal to the default -1). The region with the second highest average convolution is taken as the reference. In this particular embodiment, the second highest point is chosen instead of the first highest point due to the fact that certain image artifacts can amplify convolutions in some regions. The second bright region is less likely to have these problems, but is very likely to contain a completely filled partition.

[0030] To set the threshold, the expected deviation for the convolution of the filled partition is determined. First, the median of absolute deviations (MAD) is used:

number

[0031] The default convolution value is excluded from this formula, resulting in the following standard thresholds:

number

[0032] Next, the need for an alternative threshold is evaluated. The standard threshold may not be appropriate when the MAD is very small, for example when lambda is very high or very low. While there are limited possible remedies available when lambda is very low, a simple solution can be used to restore an appropriate threshold when lambda is very high. In this case, an alternative threshold is used when mean(ref) is above the high convolution threshold and MAD(ref) is below the low variance threshold.

number

[0033] If a threshold is set, the partition is classified as empty if the convolution value is below the threshold, and the partition is considered valid if the convolution value meets or exceeds the threshold 109 .

[0034] If the signal is noisy and / or the active partitions are dim, or if the empty partitions contain substantial signal, it may be desirable to use a final cleanup step. The cleanup step may include one or more of the following steps: empty trimming 110, expansion 111, and active trimming 112.

[0035] (i) Sky trimming Emptiness can be trimmed by clustering using path connectivity. Briefly, the neighbors of a given partition include partitions that share walls with it. For example, if the shapes of partitions in a device are square, rectangular, or hexagonal, the neighbors of a given partition are four or six partitions, respectively, that share walls with it. In this example, a path can be created connecting one partition to the next by starting with one partition, moving to one of its neighbors, then moving to one of the neighbors of that new partition, and so on. Two empty partitions are path-connected if a path exists between them that passes only through empty partitions. Empty partitions can be clustered by path connectivity by creating groups of partitions that are all path-connected to each other. Small clusters with a size less than VoidNoise are reclassified as valid.

[0036] (ii) Expansion After the false voids have been removed, the remaining voids are expanded to ensure that any boundary voids are removed. The expansion can be done using any suitable brush, for example a "diamond" brush with radius cleanupRadius, i.e. for any valid partition with coordinate z,

number

[0037] (iii) Effective Trimming In the final step, the valid partitions are trimmed by removing small groups. This step is performed as described herein for empty trimming, but not for valid partitions. First, the valid partitions are clustered by path connectivity, and then clusters with a size less than goodNoise are reclassified as valid.

[0038] Once the empties are properly identified, the flags for those partitions are changed to empty.

[0039] The output of the algorithm is summarized in Table 2: [Table 2]

[0040] Once empty partitions are identified using the methods described herein, the dPCR system can quantify the amount or concentration of the nucleic acid of interest in the active partitions within the array, ignoring the signal data collected from the empty partitions. In certain embodiments, the concentration of the nucleic acid of interest in one or more active partitions is calculated as the number of nucleic acid molecules per active partition volume.

[0041] Therefore, the concentration is the target molecule count (also known as copy number) N cThe copy number N can be calculated by dividing by the volume of liquid sampled. c is derived as follows: First, the dPCR system identifies which valid partitions are positive and negative for the target molecule and derives a sum for each.

[0042] As an example, for a dPCR plate with 2000 negative and 8000 positive valid partitions, maximum likelihood estimates are used to calculate the parameters underlying the Poisson distribution λ. An estimate of the probability that a partition is negative can be calculated as follows: P(negative) = negative number / total number For example, in the exemplary dPCR plate above, P(negative)=2000 / (2000+8000)=0.2.

[0043] If X is a Poisson random variable that models the number of molecules in a partition, then P(negative)=P(X=0)=exp(-λ) is used to estimate λ, the only parameter of the Poisson distribution. Apply this rationale to an exemplary dPCR plate: exp(-λ) = P(negative) = 0.2, i.e., λ = -log(0.2) = 1.61 (circular).

[0044] λ is an important value because it is also the expectation or mean value of the Poisson distribution. In other words, λ is the average number of target molecules per partition based on the Poisson estimation. The λ estimate is N c Used to determine:

[0045] N c = λ* number of filled partitions.

[0046] The number of filled partitions is the number of non-empty partitions: valid + invalid, not empty. For an exemplary dPCR plate, assuming 1000 additional partitions that are invalid and not empty, the number of filled partitions = 2000 + 8000 + 1000 = 11000, and the copy number is N c =1.61*11000=17710.

[0047] The concentration calculation includes an additional correction factor. If the sample has been processed, e.g., diluted, before use in dPCR, the processing and dilution steps should be included in the calculation to obtain the amount or concentration of the nucleic acid of interest in the analyzed sample. Thus, in an exemplary dPCR plate, assuming the volume of one partition is 1 mL and the sample is diluted to a 1 / 10 concentration before dPCR, the concentration before amplification is as follows: N c / (partition volume * number of filled partitions) = 17710 / (11000*0.001L) = 1610 copies / liter.

[0048] Therefore, before dilution, the concentration is 10 x 1610 = 16100 copies / liter.

[0049] The methods described herein are used to determine the amount or concentration of nucleic acids in a sample using dPCR analysis. A sample, in this context, is a quantity of material suspected of containing one or more nucleic acids to be detected or measured and quantified. As used herein, the term includes, but is not limited to, specimens (e.g., biopsies or medical specimens), cell or tissue cultures, blood, serum, plasma, needle aspirates, urine, semen, seminal fluid, seminal plasma, prostatic fluid, feces, tears, saliva, sweat, biopsies, ascites, cerebrospinal fluid, pleural fluid, amniotic fluid, peritoneal fluid, interstitial fluid, sputum, milk, lymph, bronchial and other lavage fluid samples, or tissue extracts. The source of the sample can be fresh, frozen, and / or preserved organ or tissue samples, or solid tissues such as from biopsies or aspirates, or cells derived from any point in a subject's pregnancy or development. The sample may contain compounds not naturally mixed with the source of the sample in nature, such as preservatives, anticoagulants, buffers, fixatives, nutrients, antibiotics, etc.

[0050] As detailed above, the sample contains a nucleic acid of interest, the amount or concentration of which is determined by the method of the present disclosure. Nucleic acids are biopolymers essential to all known forms of life. Thus, nucleic acids can be used as indicators of specific organisms, but also as indicators of disease, for example, in the case of mutations or naturally occurring variants. The nucleic acid of interest may be selected from the group consisting of DNA, cDNA, RNA, and mixtures thereof, or any other type of nucleic acid. The nucleic acid may contain non-nucleic acid components. It may be naturally occurring, chemically synthesized, or bioengineered. Specifically, the nucleic acid is selected from the group consisting of DNA, cDNA, RNA, and mixtures thereof.

[0051] Nucleic acids may be indicative of microorganisms (such as pathogens) and may be useful in diagnosing diseases such as infections. Infections can be caused by bacteria, viruses, fungi, and parasites or other nucleic acid-containing entities. Pathogens can be exogenous (obtained from environmental or animal sources or other people) or endogenous (derived from normal flora). Samples may be selected based on signs and symptoms, should be representative of a disease process, and should be collected before the administration of antimicrobial agents. The amount of nucleic acid in an untreated sample can indicate the severity of the disease.

[0052] Alternatively, nucleic acids may indicate genetic disorders. Genetic disorders are hereditary problems caused by one or more abnormalities in the genome, especially conditions present from birth (congenital). Most genetic disorders are very rare, affecting one in thousands or millions of people. Genetic disorders may be hereditary or non-hereditary, i.e., passed down from parental genes. In non-hereditary genetic disorders, defects may be caused by new mutations or changes in DNA. In such cases, defects are inheritable only if they occur in the germline. The same disease, such as some forms of cancer, may be caused by hereditary genetic conditions in some people, by new mutations in others, and primarily by environmental causes in still others. Clearly, the amount of nucleic acids with mutations may indicate a disease state.

[0053] In certain embodiments, the sample is a biological fluid collected from a pregnant mammal, containing maternal and fetal nucleic acid sources (e.g., RNA or DNA).In this specific embodiment, the chromosome dosage due to fetal aneuploidy can be detected using nucleic acid from the maternal sample.In addition to the empirical determination of the frequency of nucleic acid from a specific chromosome, the proportion of fetal nucleic acid in the maternal sample also affects the level of statistically significant variation for risk calculation, making it useful for determining the risk of fetal aneuploidy based on chromosome dosage.By utilizing such information when calculating the risk of aneuploidy in one or more fetal chromosomes, more precise results can be obtained that reflect the biological differences between samples.The proportion of fetal DNA in the maternal sample is used as part of the risk calculation, because the proportion of fetal DNA provides important information about the expected statistical presence of chromosome dosage.Variations from the expected statistical presence can indicate fetal aneuploidy, particularly fetal trisomy or monosomy of a specific chromosome.

[0054] In the method of the present disclosure, the amount or concentration of nucleic acid is determined. The amount of a substance is a standard defined amount. The International System of Units (SI) defines the amount of a substance as proportional to the number of elementary entities present, with the inverse of Avogadro's constant being the proportionality constant (molar unit). The SI unit of amount of substance is the mole. A mole is defined as the amount of a substance that contains the same number of elements as there are atoms in 12 g of the isotope carbon-12. Therefore, the amount of a substance of a sample is calculated as the sample mass divided by the molar mass of the substance. In this context, "amount" usually refers to the number of copies of the nucleic acid sequence of interest.

[0055] In dPCR, partitions may be miniaturized chambers of a microarray or nanoarray, chambers of a microfluidic device, microwells or nanocells, on a chip, in a capillary, on a nucleic acid-binding surface, or on beads, particularly on a microarray or chip. The methods described herein are particularly suitable for use with commercially available digital PCR platforms, such as Fluidigm's microwell-chip-based BioMark® dPCR, and array-based systems, including but not limited to Life Technologies' hole-based QuantStudio 12k flex dPCR and 3D dPCR. Microfluidic chip-based dPCR can have up to hundreds of partitions per panel. The QuantStudio 12k dPCR performs digital PCR analysis on OpenArray® plates, which contain 64 partitions per subarray and 48 total subarrays, equating to a total of 3,072 partitions per array.

[0056] Typically, using more partitions can improve accuracy, and more importantly, the precision of the dPCR determination. Approximately 100-200, 200-300, 300-400, 700 or more partitions can be used to determine the quantity or concentration of interest by PCR. In certain embodiments, dPCR is performed identically on at least 100 partitions, particularly at least 1,000 partitions, and particularly at least 5,000 partitions. In certain embodiments, dPCR is performed identically on at least 10,000 partitions, particularly at least 50,000 partitions, and particularly at least 100,000 partitions.

[0057] For example, dPCR is similarly performed on an array having at least 100 to 100,000 partitions, such as at least 1,000 to 100,000 reaction sites, or at least 10,000 to 100,000 reaction sites.

[0058] The methods described herein are performed in laboratory equipment or systems configured to perform digital nucleic acid amplification reactions. As used herein, the term "nucleic acid amplification reaction" refers to a method or reaction used in molecular biology to amplify a single or several copies of a target DNA segment (analyte) into a detectable amount of copies of the DNA segment, involving repeated cycles of temperature-dependent reactions mediated by a polymerase. Each cycle may include at least a denaturation phase (e.g., 95°C for 30 seconds), an annealing phase (e.g., 65°C for 30 seconds), and an extension phase (e.g., 72°C for 2 minutes). The dPCR plate may be in thermal contact with a thermoelectric element to heat and / or cool the sample holder to predetermined temperatures during different phases. Typically, the nucleic acid amplification reaction consists of 20 to 40 repeated cycles, and the signal intensity of light emitted from the reaction volume within the dPCR plate is measured by a detector after the nucleic acid amplification reaction is completed. Based on the measured signal light intensity, the presence of nucleic acid in the sample can be determined.

[0059] Laboratory equipment for performing nucleic acid amplification reactions is well known in the art and may include one or more of the following components (a representative laboratory equipment 200 is shown schematically in FIG. 2): i. a sample preparation module 201, which may be a component of laboratory equipment or a separate system, including a pipetting device for pipetting samples and / or reagents onto a dPCR plate 202, and for distributing samples into one or more reaction volumes in an array 203 within the dPCR plate; ii. a dPCR plate support / handling module 204 that transports dPCR plates from one module to another within the lab instrument; iii. a thermal cycling module 205 including thermoelectric elements for heating and / or cooling the dPCR plate during the amplification reaction; iv. a detection module 206 including a light source configured to emit light toward the dPCR plate (or a subsection thereof) and a light detector configured to measure the signal light intensity of the light emitted from the dPCR plate (or a subsection thereof); and v. A control device 207, for example, any physical or virtual processing device including a processor 208 configured to control the laboratory equipment and its components such that sample analysis steps are performed by the laboratory equipment.

[0060] The sample preparation module may be contained within the labware housing or may be a separate, stand-alone device that is not contained within the labware housing. In embodiments where the sample preparation module is a separate device, the dPCR plate is prepared in the sample preparation module, and then the plate is transported (automatically or manually) to the dPCR plate support / handling module 204 within the labware.

[0061] Optionally, the control device may receive information from the data management unit regarding which steps need to be performed on a particular sample. The processor of the control device may be embodied, for example, as a programmable logic controller adapted to execute a computer-readable program containing instructions for performing the operations of the laboratory equipment. One operation is to perform a method for detecting reaction volume deviations in a dPCR system, as described herein.

[0062] One or more of the components of the above-described laboratory equipment are shown, for example, in U.S. Patent Application No. 20080160525; U.S. Patent No. 10,564,102; U.S. Patent Application Publication No. 20180045641; European Patent No. 3299471; U.S. Patent Application Publication No. 20180147574; U.S. Patent Application Publication No. 20180087090. The disclosure of each of these publications is incorporated herein by reference in its entirety.

[0063] Furthermore, the present disclosure contemplates a computer program product comprising instructions for causing the laboratory equipment described herein to perform the steps of the present method to detect reaction volume deviations in the dPCR plates described herein. Additionally, the present disclosure also provides a computer-readable medium having stored thereon a computer program product comprising instructions for causing the laboratory equipment described herein to perform the steps of the present method to detect reaction volume deviations as described herein.

[0064] Embodiments of the subject matter and operations described herein can be implemented in digital electronic circuitry, or computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or one or more combinations thereof. Embodiments of the subject matter described herein can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a computer storage medium for execution by or to control the operation of a data processing apparatus. A module may contain logic executed by one or more processors. As used herein, "logic" refers to any information in the form of instruction signals and / or data that can be applied to affect the operation of a processor. Software is an example of logic.

[0065] A computer storage medium may be or be included in a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or one or more combinations thereof. Further, a computer storage medium is not a propagating signal, although a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagating signal. A computer storage medium also can be or be included in one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). The operations described herein can be implemented as operations performed by a data processing apparatus on data stored in one or more computer-readable storage devices or received from other sources.

[0066] The term "programmed processor" encompasses all types of apparatus, devices, and machines for processing data, including, for example, a programmable microprocessor, a computer, a system on a chip, or a combination of the foregoing. An apparatus may include special-purpose logic circuitry such as an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). In addition to hardware, an apparatus may also include code that creates an execution environment for the computer program, such as code comprising processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or one or more combinations thereof. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, or grid computing infrastructures.

[0067] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted, declarative, or procedural, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files storing one or more modules, subprograms, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

[0068] The processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform actions by manipulating input data and generating output. The processes and logic flows may also be performed by, and apparatus may be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0069] Processors suitable for executing a computer program include, by way of example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Typically, a processor receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a processor for performing actions in accordance with the instructions and one or more memory devices for storing instructions and data. Typically, a computer also includes one or more mass storage devices, e.g., magnetic, magneto-optical, or optical disks, for storing data, or is operatively coupled to receive data, transfer data, or both. However, a computer does not require such devices. Devices suitable for storing computer program instructions and data include, by way of example, all forms of non-volatile memory, media, and memory devices, including, by way of example, semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0070] Embodiments of the subject matter described herein can be implemented in a computing system that includes a back-end component, such as a data server, or includes a middleware component such as an application server, or includes a front-end component, such as a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described herein, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include local area networks (“LANs”) and wide area networks (“WANs”), networks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0071] A computing system may include any number of clients and servers. Clients and servers are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server sends data (e.g., HTML pages) to client devices (e.g., for the purpose of displaying the data and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., the result of a user's operation) can be received from the client device at the server.

[0072] Unless otherwise defined, all technical and scientific terms and any acronyms used herein have the same meaning as commonly understood by those skilled in the art in the field of this disclosure.The definitions of common terms in molecular biology can be found in Benjamin Lewin, Genes V, Oxford University Press, 1994 (ISBN 0-19-854287-9); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, Blackwell Science Ltd., 1994 (ISBN 0-632-02182-9); and Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, VCH Publishers, Inc., 1995 (ISBN 1-56081-569-8).

[0073] This disclosure is not limited to the specific methodology, protocols, and reagents described herein, because they may vary.Although any method and material similar or equivalent to the methods and materials described herein can be used to implement this disclosure, specific methods and materials are described herein.In addition, the terms used herein are intended to describe only specific embodiments and are not intended to limit the scope of this disclosure.

[0074] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Similarly, the words "comprise," "contain," and "encompass" are to be construed as inclusive rather than exclusive. Similarly, the word "or" is intended to include "and" unless the context clearly dictates otherwise. The term "plurality" refers to two or more.

[0075] The foregoing description is intended to illustrate various embodiments of the present disclosure. Therefore, the specific modifications described should not be construed as limitations on the scope of the present disclosure. It will be apparent to those skilled in the art that various equivalents, changes, and modifications can be made without departing from the scope of the present disclosure, and therefore, such equivalent embodiments should be understood to be included herein.

[0076] Various publications are cited herein, the disclosures of which are incorporated by reference in their entireties.

Claims

1. 1. A method for detecting reaction volume deviations in a digital polymerase chain reaction (dPCR) assay, the dPCR assay comprising quantifying the amount or concentration of a nucleic acid of interest in an array of partitions, the method comprising: (a) combining optical signals across (x, y) coordinates in the array using convolution with a kernel function, where each partition is assigned a convolution value; and (b) identifying valid and empty partitions by comparing the convolution value of each partition with a convolution threshold; and optionally (c) subjecting the data collected in step (b) to one or more additional steps, including clustering operations and morphological image processing operations. Including, Step (a) [Equation 1] The method further comprises applying a kernel function of the distance function comprising:

2. The method of claim 1 , further comprising subjecting the data collected in step (b) to morphological image processing operations including dilation, erosion, and combinations thereof.

3. 3. The method of claim 1 or 2, further comprising subjecting the data collected in step (b) to clustering, including valid trimming and / or empty trimming.

4. The method of any one of claims 1 to 3, wherein empty partitions have convolution values ​​below the convolution threshold and valid partitions have convolution values ​​above the convolution threshold.

5. The array includes a plurality of channels, and step (a) determines which one or more of the plurality of channels to use in the method by setting a useChannel flag. [Equation 2] The method of any one of claims 1 to 4, further comprising determining by:

6. Let z represent the set of (x, y) coordinates of the first partition, and the convolution of z is [Equation 3] 6. The method according to claim 1, wherein [Request Item 7] [Number 4] 7. The method of claim 6, wherein if is an empty set, the output of the empty set is set to a default value outside the range of the convolution.

8. The method of any one of claims 1 to 7, wherein the convolution threshold is based on a set of convolution values ​​within a selected reference region of the array.

9. the selected reference area is selected from a vertical reference area i, a horizontal reference area j, and a combination thereof, and max x and max y are the maximum x and y coordinates of a partition in the vertical reference area and / or horizontal reference area; (a) the vertical reference area i is [Equation 5] is represented by; (b) The horizontal reference area j is [Equation 6] and 9. The method of claim 8, further comprising: for each vertical and / or horizontal reference area, calculating an average of the effective convolution values ​​of the vertical and / or horizontal reference area; and identifying the vertical and / or horizontal reference area having the second highest average convolution value as the selected reference area. [Request Item 10] [Number 7] The median of absolute deviations (MAD), expressed as: [Equation 8] 10. The method of claim 9, further comprising obtaining:

11. and using an alternative threshold if the mean(ref) is above a high ConvolutionThreshold and the MAD(ref) is below a low VarianceThreshold, wherein the alternative threshold is: [Equation 9] and The method of claim 10 , wherein the threshold AdjustmentFrac is a percentage of the average used as the alternative threshold.

12. Clustering comprises (a) grouping partitions in the array that are all pairwise connected to each other by continuous paths, the grouping being a cluster; and (b) determining whether the partitions are less than an empty noise threshold. and (c) designating the clusters identified in step (b) as valid.

13. The method of any one of claims 1 to 12, further comprising dilation to remove boundary voids.

14. For a valid partition with coordinate z, [Equation 10] 14. The method of claim 13, wherein if there exists an empty partition z' with

15. 15. The method of any one of claims 1 to 14, wherein clustering comprises path connectivity comprising: (a) grouping partitions in the array that are all pairwise connected to each other by continuous paths, said groupings being clusters; (b) identifying one or more clusters having a size below a valid noise threshold; and (c) designating the clusters identified in step (b) as empty.

16. The method of any one of claims 1 to 15, further comprising flagging partitions identified as empty.

17. A computer program comprising instructions for causing a laboratory instrument to carry out the steps of the method according to any one of claims 1 to 16.

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