WO2021102942A1 - 随机乳化数字绝对定量分析方法及装置 - Google Patents

随机乳化数字绝对定量分析方法及装置 Download PDF

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WO2021102942A1
WO2021102942A1 PCT/CN2019/122068 CN2019122068W WO2021102942A1 WO 2021102942 A1 WO2021102942 A1 WO 2021102942A1 CN 2019122068 W CN2019122068 W CN 2019122068W WO 2021102942 A1 WO2021102942 A1 WO 2021102942A1
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droplets
volume
total number
reaction
droplet
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PCT/CN2019/122068
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English (en)
French (fr)
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夏贇
赵霞
席阳
陈芳
蒋慧
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深圳华大智造科技有限公司
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Priority to CN201980102465.9A priority Critical patent/CN114729397B/zh
Priority to US17/756,625 priority patent/US20220411858A1/en
Priority to PCT/CN2019/122068 priority patent/WO2021102942A1/zh
Publication of WO2021102942A1 publication Critical patent/WO2021102942A1/zh

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/686—Polymerase chain reaction [PCR]
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/6851—Quantitative amplification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR

Definitions

  • This application relates to the field of biological information analysis, and in particular to a random emulsification digital absolute quantitative analysis method and device.
  • test samples are usually used to analyze the sample to be tested to determine the concentration of nucleic acid molecules such as DNA or RNA in the sample to be tested.
  • the general process of analysis of test samples is as follows: divide a certain volume of sample system equally to form several isolated reaction zones, and perform PCR amplification on each reaction zone at the same time, so as to only cause one or more targets before amplification
  • the amplified fluorescent signal (or other signal) is generated in the partition where DNA/RNA exists, so the target can be directly determined by obtaining the proportion of the number of the partition in which the amplified signal occurs in all the partitions and the volume of each partition. DNA/RNA initial copy number and concentration.
  • the inventor found that the absolute quantitative methods provided by the related equipment and instruments provided in the related technology all rely on the partition setting of equal size to achieve the equal probability distribution of sample molecules, and the partitions of equal size are used.
  • the dynamic range of the set method is severely limited by the total number of partitions. Therefore, the relevant equipment and instruments provided in the related technology often display high sensitivity, high accuracy and good anti-interference ability in the detection of low-concentration or low-abundance nucleic acid samples.
  • Technical advantages For the quantitative detection of samples with higher concentrations, it is generally necessary to perform several gradient dilutions of the samples before partitioning to obtain a more ideal response result, which cannot meet the absolute quantitative requirements of nucleic acid samples of any concentration.
  • This application aims to solve one of the technical problems in the related technology at least to a certain extent.
  • the first purpose of this application is to propose a random emulsification digital absolute quantitative analysis method.
  • the second purpose of this application is to propose a calculation method for simulating and executing any size subarea or any volume of dispersed droplets to achieve digital absolute quantitative detection.
  • the third purpose of this application is to propose a random emulsification digital absolute quantitative analysis device.
  • the fourth purpose of this application is to propose a simulation system.
  • the fifth purpose of this application is to propose an electronic device.
  • the sixth purpose of this application is to provide a computer-readable storage medium.
  • the embodiment of the first aspect of the present application proposes a random emulsification digital absolute quantitative analysis method, which includes: performing random emulsification processing on the system to be emulsified in a preset container to obtain a number of isolated reaction zones or droplets, wherein, the system to be emulsified includes a sample to be tested, the total number of reaction zones or droplets is randomly generated, and the total number is a positive integer greater than 1, the reaction zone or the droplets are randomly formed, each The volume is randomly generated, and the total volume is not greater than the volume of the emulsification system; the reaction zone or droplet is amplified; after the amplification is completed, the reaction zone or droplet is imaged to obtain the target Image; analyze the image area corresponding to each reaction zone or droplet in the target image, obtain volume information of each reaction zone or droplet, determine the existence of the target molecule to be detected inside, and count the reaction that does not contain the target molecule The number of partitions or
  • the random emulsification digital absolute quantitative analysis method performs random emulsification processing on the system to be emulsified in a preset container to obtain a number of isolated reaction zones or droplets, and cause reaction zones or liquids containing target molecules to be detected.
  • the amplification reaction occurs inside the droplet, and at the end of the amplification process, image acquisition is performed on the amplified reaction zone or droplet to obtain the target image; the image area corresponding to each reaction zone or droplet in the target image is analyzed, Obtain the volume information of each reaction zone or droplet, determine the existence of the target molecule to be detected inside it, and count the number of reaction zones or droplets that do not contain the target molecule; according to the reaction zone or the total number of droplets, each reaction zone or liquid
  • the volume information of the drop, the existence of the target molecule to be detected inside, and the number of reaction zones or droplets that do not contain the target molecule determine the total number of target molecules in the sample to be detected. As a result, the total number of target molecules in the sample to be detected is accurately calculated to meet the absolute quantitative analysis requirements of the sample to be detected at any concentration.
  • the second aspect of the present application provides a calculation method for simulating and executing discrete droplets of any size or volume to achieve digital absolute quantitative detection.
  • the method is applied in an analog system.
  • the third aspect of the present application provides a random emulsification digital absolute quantitative analysis device, including: a random emulsification processing module, which is used to perform random emulsification processing on the system to be emulsified in a preset container to obtain several isolations.
  • the reaction zone or droplet of the reaction zone wherein the system to be emulsified includes the sample to be tested, the total number of the reaction zone or the droplet is randomly generated, and the total number is a positive integer greater than 1, and the reaction zone or the sample
  • the droplets are randomly formed, each volume is randomly generated, and the total volume is not greater than the volume of the emulsification system; an amplification processing module for performing amplification processing on the reaction zone or droplets; an image acquisition module for When the end of the amplification process is detected, image acquisition is performed on the reaction zone or droplet to obtain a target image; an image analysis module is used to analyze the image area corresponding to each reaction zone or droplet in the target image, Obtain the volume information of each reaction zone or droplet, determine the existence of the target molecule to be detected inside it, and count the number of reaction zones or droplets that do not contain the target molecule; the determination module is used to determine the total number of reaction zones or droplets according to the reaction zone or the total number of droplets.
  • the random emulsification digital absolute quantitative analysis device performs random emulsification processing on the system to be emulsified in the preset container to obtain several isolated reaction partitions or droplets, and performs amplification processing on the reaction partitions or droplets.
  • the amplification perform image acquisition on the amplified reaction zone or droplet to obtain the target image; analyze the image area corresponding to the reaction zone or droplet in the target image to obtain the volume of each reaction zone or droplet Information, determine the existence of the target molecule to be detected inside, and count the number of reaction zones or droplets that do not contain the target molecule; according to the reaction zone or the total number of droplets, the volume information of each reaction zone or droplet and its interior to be detected.
  • the presence of target molecules and the number of reaction zones or droplets that do not contain target molecules determine the total number of target molecules in the sample to be detected. As a result, the total number of target molecules in the sample to be detected can be accurately calculated, which facilitates absolute quantitative analysis of the sample to be detected at any concentration.
  • n is an integer greater than 1
  • a first calculating module configured to The volume value v i corresponding to each of the n reaction zones or droplets is used to calculate the total area or volume of the fluid system to be quantified
  • the generating module is used to calculate the total area or volume of the fluid system to be quantified Randomly generate m sets of coordinate value sets, wherein the value range of the elements in the coordinate value set does not exceed the total volume of the fluid system to be quantified;
  • the area of the partition or the volume value of the droplet v i is expressed as n numerical intervals with the dimensions connected according to a preset order; the first determining module is used to determine the values in each of the n numerical intervals.
  • the second calculation module is used to calculate the total volume of the fluid system to be quantified
  • the reaction partition or the total number of droplets n, C 0 the number of partitions each reaction droplet or volume values v i and partition the reaction or droplets not containing the target molecule, the calculated estimate the total number of the target molecule Value M; a verification module for comparing the set total number of target molecules m with the estimated value M of the total number of target molecules within a preset error range, if it is within the preset error range, it is determined that the simulation system can be used for execution Calculation of digital absolute quantitative detection.
  • an embodiment of the fifth aspect of the present application proposes an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor.
  • the processor executes the program when the program is executed.
  • an embodiment of the sixth aspect of the present application proposes a computer-readable storage medium, which when the instructions in the storage medium are executed by a processor, realizes the above-mentioned random emulsification digital absolute quantitative analysis method.
  • Figure 1 is a schematic flow chart of a random emulsification digital absolute quantitative analysis method provided by an embodiment of the application
  • FIG. 2 is a schematic diagram of droplet target images taken after random emulsification and amplification of DNA template molecules of different concentrations (6 concentrations in total from 10 -1 dilution to 10 -6 dilution) collected through a fluorescence microscope according to an embodiment of the application;
  • FIG. 3 is a linear fitting result of quantitative data of DNA template molecules of different concentrations obtained after processing and analyzing the target image in FIG. 2 according to an embodiment of the application.
  • FIG. 4 is a schematic flow chart of a calculation method for simulating and executing discrete droplets of any size or volume to achieve digital absolute quantitative detection according to an embodiment of the application;
  • Figure 5 is a schematic diagram of the calculation principle described by simplifying the random emulsification amplification model to a one-dimensional Poisson process.
  • Figure 6 shows the preset total number m of target molecules is 500, the number of partitions or dispersed droplets is 256, the volume obeys a log Gaussian distribution with a mean value of 4 and a coefficient of variation of 0.001, one-dimensional random simulation and calculation results;
  • Figure 7 shows the preset total number m of target molecules is 500, the number of partitions or dispersed droplets is 256, the volume obeys a log Gaussian distribution with a mean value of 4 and a coefficient of variation of 0.1, one-dimensional random simulation and calculation results;
  • Figure 8 shows the preset total number m of target molecules is 500, the number of partitions or dispersed droplets is 256, the volume obeys a log Gaussian distribution with a mean value of 4 and a coefficient of variation of 10, one-dimensional random simulation and calculation results;
  • Figure 9 shows the preset total number of target molecules m green and m blue as 1000 and 25, respectively, the number of partitions or dispersed droplets is 256, the volume obeys a log Gaussian distribution with a mean value of 4 and a coefficient of variation of 1, and a double target detection Dimensional random simulation and calculation results;
  • Figure 10 shows the total number m of preset target molecules respectively taken as 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, divided or dispersed droplets
  • the number is 256
  • the volume obeys the mean value of 4
  • the coefficient of variation is 0.001, 0.01, 0.1, 1, 10, 100 log Gaussian distribution, each condition performs 500 repeated experiments of one-dimensional random simulation and calculation results statistics, Verify the influence of partition or dispersion droplet volume variation on the calculation results;
  • Figure 11 shows the statistical results of 2001,000 Monte Carlo experiments using the simulation calculation method.
  • the number of partitions or dispersed droplets is 256, and the volume obeys a log Gaussian distribution with a mean value of 4 and a coefficient of variation of 1;
  • FIG. 12 is a schematic structural diagram of a random emulsification digital absolute quantitative analysis device provided by an embodiment of the application.
  • FIG. 13 is a schematic structural diagram of another random emulsification digital absolute quantitative analysis device provided by an embodiment of the application.
  • FIG. 14 is a schematic structural diagram of a simulation system provided by an embodiment of this application.
  • FIG. 15 is a schematic structural diagram of an electronic device provided by an embodiment of this application.
  • Figure 1 is a schematic flow chart of a random emulsification digital absolute quantitative analysis method provided by an embodiment of the application.
  • the random emulsification digital absolute quantitative analysis method may include:
  • Step 101 Randomly emulsify the system to be emulsified in a preset container to obtain a number of isolated reaction zones or droplets.
  • the system to be emulsified includes the sample to be tested, and the total number of reaction zones or droplets is randomly generated. The number is a positive integer greater than 1, the reaction zones or droplets are randomly formed, each volume is randomly generated, and the total volume is not greater than the volume of the system to be emulsified.
  • the execution body of the random emulsification digital absolute quantitative analysis method is a random emulsification digital absolute quantitative analysis device, and the random emulsification digital absolute quantitative analysis device can be configured in an electronic device.
  • the electronic device in this embodiment is an electronic device with a random emulsification digital absolute quantitative analysis function.
  • the electronic device can accurately calculate the total number of target molecules in the sample to be detected through its internal random emulsification digital absolute quantitative analysis method .
  • the above-mentioned preset container may include, but is not limited to, a flat rectangular capillary tube, a double-sided glass sealed sandwich cell, or a glass-single crystal silicon sealed sandwich cell, etc., which can make the quantitative random emulsification system form a quasi- or two-dimensional droplet array .
  • reaction partitions or droplets in this embodiment are randomly formed, indicating that the reaction partitions and the size of the droplets in this embodiment are random, that is, the volume of the reaction partitions or droplets is random. of.
  • Any size partition or any volume dispersed droplets in the present invention means that the fluid system to be quantified is divided into several smaller volumes to form isolated reaction partitions or droplets.
  • the number of these can be given
  • the natural number of a certain value can also be a certain random number or variable without a given value.
  • the numerical value with a smaller volume mentioned here can be unconstrained and unrestricted by rules, and includes all possible division results.
  • the smaller volume can be set to a certain constant (such as dividing 1 ⁇ L into 1000 pieces of 1nL) or multiple constants (such as dividing 1 ⁇ L into 100 pieces of 1nL, 2nL, 3nL, and 4nL), where
  • reaction zones or droplets formed there are some reaction zones or droplets without target molecules, while there are one or more target molecules in the remaining zones, or some A certain number of target molecules exist in the reaction zone or droplet, and another certain number of target molecules exist in the remaining zones.
  • the system to be emulsified in this embodiment may also include a preset amplification system, a preset continuous phase fluid and corresponding surfactants.
  • the preset amplification system may include, but is not limited to, PCR, Loop-mediated isothermal amplification (LAMP), Helicase-Dependent Amplification (HDA), and recombinase polymerase amplification ( Different amplification systems such as Recombinase Polymerase Amplification (RPA) and Strand Displacement Amplification (SDA).
  • LAMP Loop-mediated isothermal amplification
  • HDA Helicase-Dependent Amplification
  • RPA Recombinase Polymerase Amplification
  • SDA Strand Displacement Amplification
  • the target molecule in this embodiment may be described by taking a biological molecule represented by a nucleic acid molecule as an example.
  • the target molecule in this embodiment may also be other types of biomolecules.
  • the target molecule may be a protein, which is not limited in this implementation.
  • the target molecule in this embodiment can be not only a biological molecule, but also a chemical substance molecule.
  • the target molecule can be a metal ion.
  • the specific process of calculating the metal ion content is similar to that disclosed in this application.
  • the random emulsification digital absolute quantitative analysis method is similar and will not be repeated here.
  • the preset amplification system is an amplification system preset by the user in the electronic device according to the target molecule, so as to meet the user's purpose of adjusting the amplification system according to the target molecule.
  • the predetermined continuous phase fluid may include, but is not limited to, carbon-based, silicon-based, and fluorinated oil.
  • Step 102 Perform amplification processing on the reaction zone or droplet.
  • amplification processing can be performed on all reaction partitions or droplets at the same time.
  • reaction zone or droplet there is the reaction zone or droplet of the target molecule, and the specific primer will cause temperature-sensitive cyclic amplification of the target molecule under the high-efficiency catalysis of nucleic acid polymerase. , Thereby causing the amplification of the target molecule signal to be measured, and the signal of the indicator in the corresponding zone or droplet will also be enhanced.
  • the reaction zone or droplet that does not contain the target molecule will not cause the indicator enhancement signal caused by the amplification reaction, so that it is determined whether each zone or droplet contains or does not contain the target molecule according to different indicator signal enhancement states.
  • the indicator may include, but is not limited to, a fluorescent agent.
  • the nucleic acid molecule in the reaction zone or droplet where the nucleic acid molecule exists, the nucleic acid molecule relies on specific primers and nucleic acid polymerase to efficiently catalyze the temperature-sensitive cyclic amplification of DNA, thereby reducing the test
  • the biomolecule signal of the gene or nucleic acid fragment is amplified exponentially, and the fluorescence quantum yield of the specific dye molecule in the corresponding amplification system will also be amplified, that is, the intensity of the fluorescence signal will increase.
  • Step 103 After the amplification is completed, image collection is performed on the reaction zone or droplet to obtain a target image.
  • the preset amplification system includes a preset indicator.
  • a preset indicator When performing amplification processing on the reaction zone or droplet, it is possible to determine whether the amplification process is over by checking the intensity of the indicator signal in the preset indicator , When it is detected that the indicator signal intensity of the preset indicator no longer changes significantly, the amplification process is determined to be completed.
  • the post-amplification process in order to facilitate the subsequent acquisition of volume information of each partition or droplet based on the captured image, before image acquisition is performed on the reaction partition or droplet to obtain the target image to be analyzed, the post-amplification process can also be performed.
  • the droplets are squeezed and deformed.
  • the amplified droplets in the preset container may be appropriately squeezed and deformed, and the preset container may be processed through the image acquisition module. Image acquisition is performed on the reaction zone or droplet in the system to obtain the target image.
  • the image acquisition module includes a camera (CCD (Charge-coupled Device) image sensor or CMOS (Complementary Metal-Oxide-Semiconductor, complementary metal oxide semiconductor) image sensor), excitation light source, lens group, spectroscope, Filter modules, etc.
  • CCD Charge-coupled Device
  • CMOS Complementary Metal-Oxide-Semiconductor, complementary metal oxide semiconductor
  • the camera can be used to collect images of all reaction zones or droplets, so that the target image contains the image area corresponding to each reaction zone or droplet.
  • Step 104 Analyze the image area corresponding to each reaction zone or droplet in the target image, obtain volume information of each reaction zone or droplet, determine the existence of the target molecule to be detected inside, and count the reaction zones that do not contain the target molecule Or the number of droplets.
  • each reaction zone or droplet in the target image After acquiring the target image, the image area of each reaction zone or droplet in the target image can be determined, and each reaction zone can be calculated according to the position information of each reaction zone or the image area of the droplet in the target image Or the volume information of the droplet.
  • the image area corresponding to each reaction zone or droplet in the target image is analyzed to obtain the specific realization process of the reaction zone or the number of droplets that does not contain the target molecule: Perform feature extraction on the image area corresponding to the droplet to obtain the feature information corresponding to each image area; for each image area, match the feature information of the image area with the preset feature information; if the feature information of the image area is consistent with the preset feature information If the feature information does not match, it is determined that the reaction zone or droplet corresponding to the image area does not contain the target molecule; the total number of image areas in the target image that does not match the preset feature information is determined, and the total number of image areas is regarded as the reaction that does not contain the target molecule The number of partitions or droplets.
  • image acquisition is performed on the amplified droplets in the sequencing flow cell through a camera, and a schematic diagram of the acquired target image is shown in FIG. 2.
  • the corresponding reaction zone or the image area corresponding to the droplet contains the target according to the characteristics of the image in the image area corresponding to each reaction zone or droplet in the target image.
  • molecular For example, if the bright droplet area in the target image is a droplet containing the target molecule, and the dark droplet area is a droplet that does not contain the target molecule.
  • Step 105 Determine the sample to be tested according to the total number of reaction zones or droplets, the volume information of each reaction zone or droplet and the presence of the target molecule to be detected inside, and the number of reaction zones or droplets that do not contain the target molecule. The total number of target molecules in.
  • the number of target molecules in each reaction zone or droplet obeys the Poisson distribution of independent and non-identical distribution, and the number of reaction zones or droplets that do not contain the target molecule obeys the Poisson binomial distribution.
  • the number of target molecules in each reaction zone or droplet is determined by the following formula. State the total number of target molecules in the sample to be tested:
  • m represents the total number of target molecules to be determined in the emulsion system
  • n is the total number of reaction indicates partitions or droplets
  • j denotes the number of the reaction does not comprise a partition target molecules or droplets of C takes the value
  • e is a natural constant.
  • n, j, v i, v p, v q are all determined or obtained by statistical analysis of the target image.
  • V i is the volume of the reaction partition or droplets, wherein the target molecule comprises a total number X i is k (k is a nonnegative integer) probability:
  • X i obey the binomial distribution. among them, In order to randomly select the number of combinations of k molecules from the total number of m target molecules, The probability of assigning a single target molecule to the reaction zone or droplet. When the total number of target molecules m is a undetermined constant, the droplet contains the mathematical expectation of the total number of target molecules X i Is also a constant, and if the probability Is sufficiently small, then the approximate subject to parameters X i Poisson distribution:
  • the probability that the reaction zone or droplet does not contain the target molecule is:
  • the probability that the reaction zone or the number of droplets C 0 that does not contain the target molecule is j is:
  • the formula that needs to be satisfied to maximize the conditional probability can be derived according to formula (6):
  • n, j, v i , v p , and v q are all determined by analyzing the target image or obtained by statistics, they are known quantities, so formula (7) becomes only m only The equation of the unknown. Therefore, the interval dichotomy, Newton iteration method, chord intercept method, Newton interpolation method, etc. can be used to calculate the optimal m value when the left and right ends of the formula (7) are equal. This value is the total number of target molecules to be determined in the emulsification system.
  • FIG. 3 is the calculation method provided by the embodiment of the application, and the linear fitting result of the quantitative data of DNA template molecules of different concentrations obtained after processing and analyzing the collected target image in FIG. 2.
  • the pre-established statistical analysis model for determining the number of target molecules can also be used to determine the number of target molecules in the sample to be detected. The total amount.
  • the volume information of each reaction partition or droplet, and the number of reaction partitions or droplets that do not contain the target molecule are input into the pre-established analysis model, and the output of the analysis model is The total number of target molecules in the sample to be tested.
  • the pre-established analysis model has learned the mapping relationship with the target molecule based on the total number of partitions or droplets, the volume information of each reaction partition or droplet, and the number of reaction partitions or droplets that do not contain the target molecule. .
  • the concentration of the corresponding target molecules can be further calculated.
  • the concentration of the target molecule in the sample to be detected can be determined according to the total number of target molecules in the sample to be detected and the volume information of the sample to be detected.
  • the random emulsification digital absolute quantitative analysis method performs random emulsification processing on the system to be emulsified in a preset container to obtain several isolated reaction zones or droplets, and performs amplification processing on the reaction zones or droplets. And at the end of the amplification, image acquisition is performed on the amplified reaction partitions or droplets to obtain the target image; the image area corresponding to each reaction partition or droplet in the target image is analyzed to obtain the image of each reaction partition or droplet.
  • Volume information determine the existence of the target molecule to be detected inside, and count the number of reaction zones or droplets that do not contain the target molecule; according to the total number of reaction zones or droplets, the volume information of each reaction zone or droplet and its internal waiting Detect the presence of target molecules and the number of reaction zones or droplets that do not contain target molecules, and determine the total number of target molecules in the sample to be detected.
  • the total number of target molecules in the sample to be detected is accurately calculated to meet the absolute quantitative analysis requirements of the sample to be detected at any concentration.
  • this embodiment in order to verify the feasibility of the above method of calculating the total number of target molecules, this embodiment also proposes a calculation method for simulating and executing discrete droplets of any size or volume to achieve digital absolute quantitative detection. Used in simulation systems.
  • the arbitrary size partition or any volume dispersed droplets in the present invention refers to dividing the fluid system to be quantified into several smaller volumes to form isolated reaction partitions or droplets.
  • FIG. 4 is a schematic flow chart of a calculation method for simulating and executing discrete droplets of any size or volume to achieve digital absolute quantitative detection according to an embodiment of the application;
  • the method may include:
  • Step 401 Set the total number m of target molecules, where m is an integer greater than or equal to zero.
  • the setting method can be a fixed value of a certain constant, or a variable whose value is within a certain value range through a certain function.
  • the value of the variable is a certain constant each time the simulation is completed, and the value will be re-valued after the end .
  • the total volume of all reaction zones or droplets formed is equal to the total volume of the fluid system to be quantified.
  • the setting method can be implemented by determining the value to be set as one or several constants, or by using a random number or variable generator using a discrete or continuous distribution function in the simulation terminal, or using two Any combination of methods to implement.
  • the aforementioned reaction zone or droplet volume may also conform to a certain distribution law.
  • the user can also set the parameters of the preset distribution that the reaction zone or droplet volume obeys. information.
  • the formation of n reaction zones or the corresponding volume value of the n droplets including: according to the parameter information of the preset distribution and the set reaction zone or droplet The total number n forms the volume value corresponding to each of the n reaction zones or droplets.
  • the preset distribution includes a logarithmic Gaussian distribution
  • the parameter information includes a mean value, a standard deviation, and a coefficient of variation.
  • the aforementioned preset distribution may also be other distributions.
  • the preset distribution is uniform, and the total volume of the fluid system to be quantified is 1 ⁇ L.
  • the total volume of the fluid system to be quantified will be divided into 100 1nL, 2nL each, 3nL and 4nL droplets, a total of 400 multi-volume droplets.
  • Step 403 Calculate the total volume of the fluid system to be quantified according to the respective volume values of the n reaction zones or droplets.
  • step 404 m sets of coordinate values are randomly generated according to the total volume of the fluid system to be quantified, wherein the value range of the elements in the coordinate value set does not exceed the total volume of the fluid system to be quantified.
  • the dimension of the coordinate value set may be one-dimensional, two-dimensional or three-dimensional, which is not limited in this embodiment.
  • a random number or variable generator can be used to generate m sets of coordinate values satisfying a certain distribution.
  • a specific distribution satisfied by the set of coordinate values can be uniform distribution, Gaussian distribution, log Gaussian distribution, etc.
  • Step 405 According to the dimension of the coordinate value set, the volume value of each reaction zone or droplet is expressed as n numerical intervals with dimensions connected according to a preset order.
  • the volume value of each subarea or dispersed droplet is expressed as n numerical intervals with dimensions connected in a certain order.
  • n numerical intervals with dimensions connected in a certain order.
  • Step 406 Determine the number of coordinate values contained in each of the n numeric intervals.
  • Step 407 Count the total number of numerical intervals including the number of coordinate values of zero, and use the calculated total number as the number of reaction zones or droplets C 0 that does not contain the target molecule.
  • Step 408 according to the total volume of the fluid system to be quantified
  • Step 409 Compare the set total number m of target molecules with the estimated value M of the total number of target molecules whether it is within a preset error range, and if it is within the preset error range, it is determined that the analog system can be used to perform the calculation of digital absolute quantitative detection.
  • a stochastic simulation method for simulating and executing any size subarea or any volume of dispersed droplets to achieve digital absolute quantitative detection.
  • Design and implement a digital absolute quantitative amplification experiments and analyzes and acquires the values of the respective partition or the size of the droplets, such as droplets or partitions volume v i, n and the partition or the total number of droplets of the experimental data.
  • the partitions or droplets containing the target molecule can cause amplified amplification signals, while the partitions or droplets that do not contain the target molecule will not cause amplified amplification signals, so that each partition is determined according to different reaction states.
  • the droplet contains the target molecule or does not contain the target molecule, and the total number C 0 of the partition or the droplet that does not contain the target molecule is calculated.
  • the following uses the simulation system to perform the calculation of absolute quantitative detection:
  • Example 1 for verifying the stochastic simulation method This example is used to verify the feasibility of using the stochastic simulation method to perform random emulsification zone simulation, and to evaluate the influence of zone volume variation on the absolute quantitative results.
  • the default number of molecules m is 500 to simulate the situation when the number of target molecules in the system is 500.
  • the number n of dispersed droplets generated by random emulsification is preset to 256, and the volume of dispersed droplets v i is set to follow a log Gaussian distribution with a mean value of 4 (in general, the volume of droplets formed by random emulsification meets the log Gaussian distribution) , Set the standard deviation of the dispersed droplet volume to 0.004, 0.04, 0.4, 4, 40, and 400, and the corresponding coefficient of variation to be 0.001, 0.01, 0.1, 1, 10, and 100, respectively. Randomly generate 256 volume values according to the determined parameters.
  • the estimated value M of the total number of target molecules in all the dispersed droplets are 516.8, 518.15, 507.9, 526.4, 493.7, and 522.05, respectively.
  • Figure 6 shows the simulation and calculation visualization results when the coefficient of variation is 0.001.
  • the total volume of dispersed droplets is 1024.0897, C 0 is 34, and the estimated value M is 516.8.
  • 7 is the simulation and calculation visualization results when the coefficient of variation is 0.1, the total volume of the dispersed droplets is 1022.1397, C 0 is 36, and the estimated value M is 507.9.
  • Figure 8 is the simulation and calculation visualization results when the coefficient of variation is 10.
  • the total volume of the drop is 841.1841, C 0 is 154, and the estimated value M is 493.7.
  • Figure 9 shows the simulation and calculation visualization results of random emulsification of dual target molecules when the coefficient of variation is 0.1.
  • the total number of the two target molecules is 25 and 1000, respectively. Assuming that the total volume of the dispersed droplet is 1062.4776, the C 0 of the target molecule marked in green is 233, the estimated value M is 24.4, the C 0 of the target molecule 2 marked in blue is 15, and the estimated value M is 1010.8.
  • Example 2 for evaluating the influence of partition volume variation on absolute quantitative results This example uses the stochastic simulation method of the present invention to evaluate the influence of the volume variation of dispersed droplets generated by random emulsification on the absolute quantitative results.
  • the preset number of molecules m is 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000.
  • the number n of dispersed droplets generated by random emulsification is preset to 256, and the volume of dispersed droplets v i is set to follow a log Gaussian distribution with a mean value of 4 (in general, the volume of droplets formed by random emulsification meets the log Gaussian distribution) , Set the standard deviation of the dispersed droplet volume to 0.004, 0.04, 0.4, 4, 40, and 400, and the corresponding coefficient of variation to be 0.001, 0.01, 0.1, 1, 10, and 100, respectively. Randomly generate 256 volume values according to the determined parameters.
  • the visualization results of the simulation experiment are shown in Figure 10.
  • the data in the figure shows that the volume variation has a greater impact on the quantitative results. The larger the volume variation, the wider the dynamic range.
  • the coefficient of variation is 100, only 256 dispersed droplets are needed to accurately quantify 100,000 target molecules.
  • Example 3 to verify the embodiment of the simulation calculation method This embodiment adopts the random simulation method of the present invention to analyze the mapping relationship between the partition or the total number of droplets C 0 without target molecules and the total number of target molecules m, and is determined by This calculates the possible value range of m and the most likely value M.
  • the preset number of molecules m is all integers from 1 to 2001, namely 1, 2, 3,..., 2000, 2001.
  • the number n of dispersed droplets generated by random emulsification is preset to 256, and the volume of dispersed droplets v i is set to follow a log Gaussian distribution with a mean value of 4 (in general, the volume of droplets formed by random emulsification meets the log Gaussian distribution) , Set the standard deviation of the dispersed droplet volume to 4, and the corresponding coefficient of variation to 1. Randomly generate 256 volume values according to the determined parameters, and maintain the volume parameters unchanged for each subsequent simulation.
  • the visualization result based on the simulation calculation method is shown in the multimodal histogram in Figure 11, where the horizontal axis of the coordinate system is the possible range of m values, and the vertical axis of the coordinate system is the frequency of each m value.
  • Each peak in the figure represents the statistical results of all possible preset m values corresponding to a certain C 0 value.
  • the C 0 values represented from left to right are 255, 240, 225, 210, 195, 180, 165, 150, 135, 120, 105, 90, 75, 60, 45, 30, 15, and 0.
  • the probability density function of m value can be calculated by fitting or interpolation, so as to calculate the result of simulation calculation method E(m) and the confidence interval of corresponding m [ m min , m max ] is the calculation result and confidence interval of the total number of molecules obtained by this simulation calculation method.
  • FIG. 12 is a schematic structural diagram of a random emulsification digital absolute quantitative analysis device provided by an embodiment of the application.
  • the random emulsification digital absolute quantitative analysis device includes a random emulsification processing module 110, an amplification processing module 120, an image acquisition module 130, an image analysis module 140, and a determination module 150, wherein:
  • the random emulsification processing module 110 is used to perform random emulsification processing on the system to be emulsified in the preset container to obtain a number of isolated reaction zones or droplets, where the system to be emulsified includes the sample to be tested, the total number of reaction zones or droplets It is randomly generated, the total number is a positive integer greater than 1, the reaction zone or droplets are randomly formed, each volume is randomly generated (or randomly generated), and the total volume is not greater than the volume of the emulsification system.
  • the amplification processing module 120 is used to perform amplification processing on the reaction zone or droplet.
  • the image acquisition module 130 is used for image acquisition of the reaction zone or droplet at the end of the amplification process to obtain the target image.
  • the image analysis module 140 is used to analyze the image area corresponding to each reaction zone or droplet in the target image, obtain volume information of each reaction zone or droplet, and determine the existence of the target molecule to be detected in the target image. Statistics do not include the target The number of reaction zones or droplets of the molecule.
  • the determining module 150 is used to determine the total number of reaction zones or droplets, the volume information of each reaction zone or droplet and the presence of target molecules to be detected inside, and the number of reaction zones or droplets that do not contain target molecules. The total number of target molecules in the sample to be tested.
  • the device in order to facilitate subsequent analysis based on the target image, the volume information of each reaction zone or droplet and the existence of the target molecule to be detected inside, and the reaction zone or droplet that does not contain the target molecule are quickly analyzed.
  • the device may further include:
  • the deformation processing module 160 is configured to perform extrusion deformation processing on each reaction zone or droplet after the amplification processing.
  • the image analysis module 140 is specifically configured to: perform feature extraction on image regions corresponding to each reaction zone or droplet in the target image to obtain feature information corresponding to each image region. For each image area, the feature information of the image area is matched with the preset feature information. If the feature information of the image area does not match the preset feature information, it is determined that the reaction zone or droplet corresponding to the image area does not contain the target molecule. Determine the total number of image areas in the target image that do not match the preset feature information, and use the total number of image areas as the number of reaction zones or droplets that do not contain the target molecule.
  • the number of target molecules in each reaction zone or droplet obeys the Poisson distribution of independent and non-identical distribution, and the number of reaction zones or droplets that do not contain the target molecule obeys the Poisson binomial distribution, according to the following formula Determine the total number of target molecules in the sample to be tested:
  • m represents the total number of target molecules to be determined in the emulsion system
  • n is the total number of reaction indicates partitions or droplets
  • j denotes the number of the reaction does not comprise a partition target molecules or droplets of C takes the value
  • e is a natural constant.
  • n, j, v i, v p, v q are all determined or obtained by statistical analysis of the target image.
  • the preset amplification system includes a preset indicator.
  • the expansion is determined The increase processing is over.
  • the random emulsification digital absolute quantitative analysis device performs random emulsification processing on the system to be emulsified in a preset container to obtain several isolated reaction zones or droplets, and performs amplification processing on the reaction zones or droplets.
  • each reaction partition or droplet corresponding to the image area in the target image is analyzed to obtain each reaction partition Or the volume information of the droplet, determine the existence of the target molecule to be detected inside it, and count the number of reaction zones or droplets that do not contain the target molecule; according to the volume information of each reaction zone or droplet, the preset number and the target does not contain The number of reaction zones or droplets of molecules determines the total number of target molecules in the sample to be tested. As a result, the total number of target molecules in the sample to be detected can be accurately determined, which facilitates the absolute quantitative analysis requirements of the sample to be detected at any concentration.
  • FIG. 14 is a schematic structural diagram of a simulation system provided by an embodiment of this application. Among them, it should be noted that the analog system is used to simulate and execute any size subarea or any volume of dispersed droplets to realize the calculation of digital absolute quantitative detection.
  • the simulation system includes:
  • the first setting module 210 is used to set the total number m of target molecules, where m is an integer greater than or equal to 0;
  • the first calculation module 230 is used to calculate the total volume of the fluid system to be quantified according to the respective volume values of the n reaction zones or n droplets
  • the generating module 240 is configured to randomly generate m sets of coordinate value sets according to the total volume of the fluid system to be quantified, wherein the value range of the elements in the coordinate value set does not exceed the total volume of the fluid system to be quantified;
  • the presentation module 250 is used to represent, according to the dimensions of the coordinate value set, the volume value of each reaction zone or droplet as n numerical intervals with dimensions connected according to a preset order;
  • the first determining module 260 is configured to determine the number of coordinate values X i contained in each of the n numeric intervals;
  • the statistics module 270 is used to count the total number of numerical intervals including the number of coordinate values of zero, and use the calculated total number as the number of reaction zones or droplets C 0 that does not contain the target molecule;
  • the second determining module 280 is used to determine the total volume of the fluid system to be quantified The total number of reactive or droplets partitions n, each reaction volume of partitions or the number of droplets value C 0 v i and partition the reaction or droplets not containing the target molecule, the total number of M determined estimates of the target molecule;
  • the verification module 290 is used to compare whether the set total number of target molecules m and the estimated value M of the total number of target molecules are within the preset error range, and if it is within the preset error range, determine that the analog system can be used to perform the calculation of digital absolute quantitative detection .
  • the device may further include:
  • the second setting module is used to set the parameter information of the preset distribution subject to the reaction zone or the droplet volume;
  • the data forming module 220 is specifically configured to: form n reaction zones or volume values corresponding to each of the n droplets according to the parameter information of the preset distribution and the set reaction zone or the total number n of droplets.
  • the preset distribution may include but is not limited to Gaussian distribution, log Gaussian distribution, uniform distribution, and the parameter information includes mean value, standard deviation, and coefficient of variation.
  • FIG. 15 is a schematic structural diagram of an electronic device provided by an embodiment of this application.
  • the electronic equipment includes:
  • the processor 1002 executes the program, the random emulsification digital absolute quantitative analysis method provided in the foregoing embodiment is implemented, or the simulation method provided in the foregoing embodiment executes discrete droplets of any size partition or any volume to implement the calculation method of digital absolute quantitative detection.
  • the electronic equipment also includes:
  • the communication interface 1003 is used for communication between the memory 1001 and the processor 1002.
  • the memory 1001 is used for storing computer programs that can run on the processor 1002.
  • the memory 1001 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), for example, at least one disk memory.
  • the processor 1002 is configured to implement the random emulsification digital absolute quantitative analysis method of the foregoing embodiment when executing a program.
  • the bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus.
  • ISA Industry Standard Architecture
  • PCI Peripheral Component
  • EISA Extended Industry Standard Architecture
  • the bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used in FIG. 15 to represent it, but it does not mean that there is only one bus or one type of bus.
  • the memory 1001, the processor 1002, and the communication interface 1003 are integrated on a single chip, the memory 1001, the processor 1002, and the communication interface 1003 can communicate with each other through internal interfaces.
  • the processor 1002 may be a central processing unit (Central Processing Unit, referred to as CPU), or a specific integrated circuit (Application Specific Integrated Circuit, referred to as ASIC), or may be configured to implement one or more of the embodiments of the present application integrated circuit.
  • CPU Central Processing Unit
  • ASIC Application Specific Integrated Circuit
  • This embodiment also provides a computer-readable storage medium on which a computer program is stored, which is characterized in that when the program is executed by the processor, the random emulsification digital absolute quantitative analysis method as described above is realized, or the simulation executes any size partition or arbitrary Volume of dispersed droplets to achieve the calculation method of digital absolute quantitative detection.
  • first and second are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with “first” and “second” may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of” means at least two, such as two, three, etc., unless specifically defined otherwise.
  • a "computer-readable medium” can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, device, or device or in combination with these instruction execution systems, devices, or devices.
  • computer readable media include the following: electrical connections (electronic devices) with one or more wiring, portable computer disk cases (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable and editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because it can be used for example by optically scanning the paper or other medium, and then editing, interpreting, or other suitable media if necessary. The program is processed in a manner to obtain the program electronically, and then stored in the computer memory.
  • each part of this application can be implemented by hardware, software, firmware, or a combination thereof.
  • multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • Discrete logic gate circuits for implementing logic functions on data signals
  • Logic circuits application specific integrated circuits with suitable combinational logic gates
  • PGA programmable gate array
  • FPGA field programmable gate array
  • a person of ordinary skill in the art can understand that all or part of the steps carried in the method of the foregoing embodiments can be implemented by a program instructing relevant hardware to complete.
  • the program can be stored in a computer-readable storage medium, and the program can be stored in a computer-readable storage medium. When executed, it includes one of the steps of the method embodiment or a combination thereof.
  • the functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.
  • the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

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Abstract

一种随机乳化数字绝对定量分析方法及装置，其中，包括通过对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，并结合采集到的包含扩增处理后的反应分区或液滴所对应的图像区域的目标图像，并通过对目标图像进行分析，确定分区或液滴总数量、各个反应分区或液滴的体积信息以及其内部待检测目标分子的存在情况、以及不包含目标分子的反应分区或液滴的数量，以及根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及不包含目标分子的反应分区或液滴的数量，准确计算出待检测样本中目标分子的总数量，方便对任意浓度的待检测样本的绝对定量分析。

Description

随机乳化数字绝对定量分析方法及装置 技术领域
本申请涉及生物信息分析领域，尤其涉及一种随机乳化数字绝对定量分析方法及装置。
背景技术
以核酸为代表的生化标志物或一般生物或化学物质分子或其他形式微粒、颗粒的精准定量检测，对于临床诊断、疾病进展监控与治疗，基因表达分析，测序质控与验证，微生物检测，转基因检测，具有重要意义。
相关技术中，通常采用具有数字聚合酶链式反应(数字PCR，digital PCR)功能的设备仪器对待检测样品进行分析，以确定待检测样品中DNA或RNA等核酸分子的浓度，其中，设备仪器对待检测样品进行分析的一般过程为：通过将某个体积的样本体系均等划分，形成若干隔离的反应分区，并对各反应分区同时实施PCR扩增，从而仅仅引起扩增前有一个或多个目标DNA/RNA存在的分区中产生放大的荧光信号(或其他信号)，于是可通过获取发生放大信号的分区在所有分区中的数量占比及各分区体积，经泊松二项分布校正直接确定目标DNA/RNA起始拷贝数及浓度。然而，在实现本申请的过程中，发明人发现相关技术中所提供的相关设备仪器所提供的绝对定量方式都依赖于尺寸均等的分区设置来实现样本分子的等概率分配，用尺寸均等的分区设置的方法动态范围严重受限于分区总数量，因此，相关技术中所提供的相关设备仪器往往在低浓度或低丰度核酸样本检测中才能发挥高灵敏度、高准确度及抗干扰性好等技术优势，对于浓度较高样本的定量检测，一般需要在分区前对样本进行若干梯度稀释才能获得较理想的响应结果，不能满足任意浓度核酸样本的绝对定量需求。另外，现有的数字PCR产品形式都依赖微流控技术及系统来准确地将流体划分至纳升甚至飞升，形成尺寸均等的分区或单分散液滴，相比于实时PCR给用户带来了额外的技术难度、操作难度、经济成本和时间成本。
发明内容
本申请旨在至少在一定程度上解决相关技术中的技术问题之一。
为此，本申请的第一个目的在于提出一种随机乳化数字绝对定量分析方法。
本申请的第二个目的在于提出一种用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法。
本申请的第三个目的在于提出一种随机乳化数字绝对定量分析装置。
本申请的第四个目的在于提出一种模拟系统。
本申请的第五个目的在于提出一种电子设备。
本申请的第六个目的在于提出一种计算机可读存储介质。
为达上述目的，本申请第一方面实施例提出了一种随机乳化数字绝对定量分析方法，包括：对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，其中，所述待乳化体系包括待检测样本，所述反应分区或液滴的总数量为随机生成，所述总数量为大于1的正整数，所述反应分区或所述液滴随机形成，各体积为随机产生，且体积总和不大于所述乳化体系的体积；对所述反应分区或液滴进行扩增处理；在扩增结束后，对所述反应 分区或液滴进行图像采集，得到目标图像；对所述目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量，根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及所述不包含目标分子的反应分区或液滴的数量，确定所述待检测样本中目标分子的总数量。
本申请实施例提供的随机乳化数字绝对定量分析方法，对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，并引起含有待检测目标分子的反应分区或液滴内部发生扩增反应，以及在扩增结束时，对扩增处理后的反应分区或液滴进行图像采集，得到目标图像；对目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量；根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及所述不包含目标分子的反应分区或液滴的数量，确定待检测样本中目标分子的总数量。由此，准确计算出待检测样本中目标分子的总数量，满足对任意浓度的待检测样本的绝对定量分析需求。
为达上述目的，本申请第二方面实施例提出了一种用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法，所述方法应用在模拟系统中，其特征在于，包括：设置目标分子总数m，其中，m为大于或等于0的整数；设置反应分区或液滴的总数量n，并且根据设置的反应分区或液滴的总数量n，随机形成n个反应分区或n个液滴各自对应的体积值v i，其中，v i表示第i个反应分区或液滴的体积值，i＝1，2，3，…，n，其中，n为大于1的整数；计算出n个反应分区或液滴的总体积
Figure PCTCN2019122068-appb-000001
根据所述待定量流体体系的总体积，随机产生m组坐标数值集合，其中，所述坐标数值集合中元素的取值范围不超过所述待定量流体体系的总体积；根据所述坐标数值集合的维度，将各反应分区或液滴的体积值v i表示为具有所述维度的依据预设次序连接的n个数值区间；确定所述n个数值区间中各数值区间内所包含的坐标数值的数量X i；统计出包含坐标数值数量为零(X i＝0)的数值区间的总个数，并将统计出的总个数作为不包含目标分子的反应分区或液滴的数量C 0；根据所述待定量流体体系的总体积
Figure PCTCN2019122068-appb-000002
所述反应分区或液滴的总数量n、所述各反应分区或液滴的体积值v i以及不包含目标分子的反应分区或液滴的数量C 0，确定出所述目标分子总数的估算值M；比较设置的目标分子总数m与所述目标分子总数的估算值M是否在预设误差范围内，如果在预设误差范围内，则确定所述模拟系统可用于执行数字绝对定量检测的计算。
为达上述目的，本申请第三方面实施例提出了一种随机乳化数字绝对定量分析装置，包括：随机乳化处理模块，用于对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，其中，所述待乳化体系包括待检测样本，所述反应分区或液滴的总数量为随机生成，所述总数量为大于1的正整数，所述反应分区或所述液滴随机形成，各体积为随机产生，且体积总和不大于所述乳化体系的体积；扩增处理模块，用于对所述反应分区或液滴进行扩增处理；图像采集模块，用于在检测到扩增处理结束时，对所述反应分区或液滴进行图像采集，得到目标图像；图像分析模块，用于对所述目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的 存在情况，统计不包含目标分子的反应分区或液滴的数量；确定模块，用于根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及所述不包含目标分子的反应分区或液滴的数量，确定所述待检测样本中目标分子的总数量。
本申请实施例提供的随机乳化数字绝对定量分析装置，对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，并对反应分区或液滴进行扩增处理，以及在扩增结束时，对扩增后的反应分区或液滴进行图像采集，得到目标图像；对目标图像中反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量；根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及所述不包含目标分子的反应分区或液滴的数量，确定所述待检测样本中目标分子的总数量。由此，准确计算出待检测样本中目标分子的总数量，方便对任意浓度的待检测样本进行绝对定量分析。
为达上述目的，本申请第四方面实施例提出了一种模拟系统，所述模拟系统用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算，包括：第一设置模块，用于设置目标分子总数m，其中，m为大于或等于0的整数；数据形成模块，用于设置的反应分区或液滴的总数量n，根据设置的反应分区或液滴的总数量n，随机形成n个反应分区或液滴各自对应的体积值v i(i＝1，2，3，…，n)，其中，n为大于1的整数；第一计算模块，用于根据n个反应分区或液滴各自对应的体积值v i，计算出待定量流体体系的总面积或总体积
Figure PCTCN2019122068-appb-000003
生成模块，用于根据所述待定量流体体系的总面积或总体积
Figure PCTCN2019122068-appb-000004
随机产生m组坐标数值集合，其中，所述坐标数值集合中元素的取值范围不超过所述待定量流体体系的总体积；表示模块，用于根据所述坐标数值集合的维度，将各反应分区的面积或液滴的体积值v i表示为具有所述维度的依据预设次序连接的n个数值区间；第一确定模块，用于确定所述n个数值区间中每个数值区间内所包含的坐标数值的数量X i；统计模块，用于统计出包含坐标数值数量为零的数值区间的总个数，并将统计出的总个数作为不包含目标分子的反应分区或液滴的数量C 0；第二计算模块，用于根据所述待定量流体体系的总体积
Figure PCTCN2019122068-appb-000005
所述反应分区或液滴的总数量n、所述各反应分区或液滴的体积值v i以及不包含目标分子的反应分区或液滴的数量C 0，计算出所述目标分子总数的估算值M；验证模块，用于比较设置的目标分子总数m与所述目标分子总数的估算值M是否在预设误差范围内，如果在预设误差范围内，则确定所述模拟系统可用于执行数字绝对定量检测的计算。
为达上述目的，本申请第五方面实施例提出了一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上所述的随机乳化数字绝对定量分析方法。
为了实现上述目的，本申请第六方面实施例提出了一种计算机可读存储介质，当所述存储介质中的指令被处理器执行时，实现如上所述的随机乳化数字绝对定量分析方法。
本申请附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为本申请实施例提供的一种随机乳化数字绝对定量分析方法的流程示意图；
图2为本申请实施例提供的通过荧光显微镜采集的不同浓度(10 -1稀释至10 -6稀释共6个浓度)DNA模板分子随机乳化扩增后拍摄的液滴目标图像的示意图；
图3为本申请实施例提供的经图2中目标图像处理分析后得到的不同浓度DNA模板分子定量数据线性拟合结果。
图4为本申请实施例提供的一种模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法的流程示意图；
图5为将随机乳化扩增模型通过简化为一维泊松过程来刻画描述的计算原理示意图。
图6为预设目标分子总数m为500，分区或分散液滴的数量为256，体积服从均值为4，变异系数为0.001的对数高斯分布，一维随机模拟及计算结果；
图7为预设目标分子总数m为500，分区或分散液滴的数量为256，体积服从均值为4，变异系数为0.1的对数高斯分布，一维随机模拟及计算结果；
图8为预设目标分子总数m为500，分区或分散液滴的数量为256，体积服从均值为4，变异系数为10的对数高斯分布，一维随机模拟及计算结果；
图9为预设目标分子总数m green与m blue分别为1000与25，分区或分散液滴的数量为256，体积服从均值为4，变异系数为1的对数高斯分布，双重目标检测的一维随机模拟及计算结果；
图10为预设目标分子总数m分别取为1、2、5、10、20、50、100、200、500、1000、2000、5000、10000、20000、50000、100000，分区或分散液滴的数量为256，体积服从均值为4，变异系数分别为0.001、0.01、0.1、1、10、100的对数高斯分布，每个条件执行500次重复试验的一维随机模拟及计算的结果统计，验证分区或分散液滴体积变异对计算结果的影响；
图11为模拟计算法采用2001000次蒙特卡洛试验的统计结果图，分区或分散液滴的数量为256，体积服从均值为4，变异系数为1的对数高斯分布；
图12为本申请实施例提供的一种随机乳化数字绝对定量分析装置的结构示意图；
图13为本申请实施例提供的另一种随机乳化数字绝对定量分析装置的结构示意图；
图14为本申请实施例提供的一种模拟系统的结构示意图；
图15为本申请实施例提供的一种电子设备的结构示意图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
下面参考附图描述本申请实施例的随机乳化数字绝对定量分析方法及装置。
图1为本申请实施例提供的一种随机乳化数字绝对定量分析方法的流程示意图。
如图1所示，该随机乳化数字绝对定量分析方法可以包括：
步骤101，对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或 液滴，其中，待乳化体系包括待检测样本，反应分区或液滴的总数量为随机生成，总数量为大于1的正整数，反应分区或液滴随机形成，各体积为随机产生，且体积总和不大于待乳化体系的体积。
其中，需要说明的是，随机乳化数字绝对定量分析方法的执行主体为随机乳化数字绝对定量分析装置，该随机乳化数字绝对定量分析装置可以配置在电子设备中。
其中，本实施例中的电子设备是具有随机乳化数字绝对定量分析功能的电子设备，该电子设备通过其内部的随机乳化数字绝对定量分析方法，可准确计算出待检测样本中目标分子的总数量。
其中，上述预设容器可以包括但不限于扁平矩形毛细管、双侧玻璃密闭夹心池或玻璃-单晶硅密闭夹心池等容器，可使待定量随机乳化体系形成准二维或二维液滴阵列。
其中，可以理解的是，本实施例中的反应分区或液滴是随机形成的，说明本实施例中反应分区以及液滴的大小是随机的，即，反应分区或液滴的体积大小是随机的。
本发明所述的任意尺寸分区或任意体积分散液滴是指，将待定量的流体体系划分为若干个较小体积，形成隔离的反应分区或液滴，这里所述的若干个可以为给定某数值的自然数也可以是未给定数值的某随机数或变量，这里所述的较小体积的数值可以是不受条件约束，不加规则限制的，包含了所有可能出现划分结果的情况，例如所述的较小体积可以设置为某一常数(如将1μL划分为1000个1nL)，也可以设置为多个常数(如将1μL划分为各100个1nL，2nL，3nL和4nL)，其中所述多个常数的间隔或比例可以是恒定的也可以是随机的，也可以设置为一般化的具有某离散或连续分布函数的某随机数或变量(如将1μL划分为1nL至5nL之间具有均匀分布或高斯分布的随机数值体积)，也可以设置成只需满足总体积限制的任意变量(如将1μL划分为X 1nL，X 2nL，…，X nnL，其中X 1+X 2+…+X n＝1000，且X 1，X 2，…，X n≥0)。
可以理解的是，本实施例中，所形成的反应分区或液滴中，有一些反应分区或液滴中没有目标分子存在，而其余分区中有一个或多个目标分子存在，或，有一些反应分区或液滴中有某一数量的目标分子存在，而其余分区中有另外某一数量的目标分子存在。
其中，需要说明的是，本实施例中的待乳化体系除了包括待检测样本之外，还可以包括预设扩增体系以及预设连续相流体及对应表面活性剂。
其中，预设扩增体系可以包括但不限于PCR，环介导等温扩增(Loop-mediated isothermal amplification，LAMP)，赖解旋酶扩增(Helicase-Dependent Amplification，HDA)，重组酶聚合酶扩增((Recombinase polymerase amplification，RPA)，链置换扩增(Strand displacement amplification，SDA)等不同的扩增体系。
其中，本实施例中的目标分子可以是以核酸分子为代表的生物分子为例进行描述。
可以理解的是，本实施例中的目标分子还可以为其他类型的生物分子，例如，目标分子可以为蛋白质，该实施对此不作限定。
其中，需要理解的是，本实施例中的目标分子除了可以是生物分子外，还可以是化学物质分子，例如，目标分子可以为金属离子，计算金属离子含量的具体过程，与本申请公开的随机乳化数字绝对定量分析方法类似，此处不再赘述。
其中，预设扩增体系是用户根据目标分子在电子设备中预先设置的扩增体系，以满足用户根据目标分子对扩增体系进行调整的目的。
其中，预设连续相流体可以包括但不限于碳基，硅基以及氟化油等。
步骤102，对反应分区或液滴进行扩增处理。
具体地，在形成若干数量的随机大小的反应分区或液滴后，可对所有反应分区或液滴同时实施扩增处理。
可以理解的是，在反应分区或液滴进行扩增处理时，存在目标分子的反应分区或液滴内，特异性引物将在核酸聚合酶高效催化作用下使目标分子发生温度敏感的循环扩增，从而引起待测目标分子信号的放大，对应分区或液滴中指示剂的信号也会增强。然而，不包含目标分子的反应分区或液滴不会引起由扩增反应导致的指示剂增强信号，从而根据不同指示剂信号增强状态确定各分区或液滴含有或是不含有目标分子。
其中，指示剂可以包括但不限于荧光剂。
举例而言，以目标分子为核酸分子为例，在存在核酸分子的反应分区或液滴中，核酸分子依赖特异性引物与核酸聚合酶高效催化DNA发生温度敏感的循环扩增，从而将待测基因或核酸片段的生物分子信号进行指数放大，对应扩增体系中的特定染料分子的荧光量子产率也会被放大，即荧光信号强度会增大。
步骤103，在扩增结束后，对反应分区或液滴进行图像采集，得到目标图像。
在本实施例中，预设扩增体系包括预设指示剂，在对反应分区或液滴进行扩增处理时，可通过对预设指示剂中的指示信号强度，来确定扩增处理是否结束，在检测到预设指示剂的指示信号强度不再明显变化时，确定扩增处理结束。
在本实施例中，为了方便后续基于采集到的图像，得到各分区或液滴的体积信息，在对反应分区或液滴进行图像采集，得到待分析目标图像之前，还可以对扩增处理后的液滴进行挤压变形处理。
作为一种示例性的实施方式，在对反应分区或液滴进行扩增处理后，可将预设容器中扩增后的液滴进行适当挤压变形处理，并通过图像采集模块对预设容器中的反应分区或液滴进行图像采集，得到目标图像。
其中，图像采集模块包括摄像头(CCD(Charge-coupled Device，电荷耦合元件)图像传感器或CMOS(Complementary Metal-Oxide-Semiconductor，互补金属氧化物半导体)图像传感器)、激发光源、透镜组、分光镜、滤光模组等。
其中，可通过摄像头对所有反应分区或液滴进行图像采集，以使得目标图像中包含各反应分区或液滴所对应的图像区域。
步骤104，对目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量。
在获取目标图像后，可确定出每个反应分区或液滴在目标图像中的图像区域，并根据每个反应分区或液滴在目标图像中的图像区域的位置信息，计算出每个反应分区或液滴的体积信息。
在本实施例中，对目标图像中各反应分区或液滴对应的图像区域进行分析，得到不包含目标分子的反应分区或液滴的数量的具体实现过程为：对目标图像中各反应分区或液滴对应的图像区域进行特征提取，以得到每个图像区域对应的特征信息；针对每个图像区域，将图像区域的特征信息与预设特征信息进行匹配；如果图像区域的特征信息与预设特征信息不匹 配，则确定图像区域对应的反应分区或液滴中不包含目标分子；确定目标图像中与预设特征信息不匹配的图像区域总数，并将图像区域总数作为不包含目标分子的反应分区或液滴的数量。
其中，通过摄像头对测序流动池中扩增处理后的液滴进行图像采集，所采集到的目标图像的示意图，如图2所示。
其中，需要说明的是，在获取目标图像后，可根据目标图像中各反应分区或液滴所对应的图像区域中图像的特征，确定对应反应分区或液滴所对应的图像区域中是否包含目标分子。例如，如果目标图像中液滴区域明亮的为包含目标分子的液滴，液滴区域暗淡的为不包含目标分子的液滴。
步骤105，根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及不包含目标分子的反应分区或液滴的数量，确定待检测样本中目标分子的总数量。
在本实施例中，各反应分区或液滴中目标分子的数量服从独立非同分布的泊松分布，不包含目标分子的反应分区或液滴数量服从泊松二项分布，根据以下公式确定所述待检测样本中目标分子的总数量：
Figure PCTCN2019122068-appb-000006
其中，m表示乳化体系中待确定的目标分子总数，n表示反应分区或液滴总数量，j表示不包含目标分子的反应分区或液滴的数量C 0的取值，v i(i＝1，2，3，…，n)表示第i个反应分区或液滴的体积，v p(p＝1，2，3，…，n-j)表示包含有目标分子的第p个反应分区或液滴的体积，v q(q＝1，2，3，…，j)表示不包含目标分子的第q个反应分区或液滴的体积，e为自然常数。以上，n，j，v i，v p，v q皆是通过对目标图像进行分析确定或统计得到的。
具体而言，本体溶液中的目标分子分配到若干多体积液滴体系中的过程可视为一系列独立非同分布伯努利试验。对于体积为v i的反应分区或液滴，其中包含的目标分子总数量X i为k(k为非负整数)的概率为：
Figure PCTCN2019122068-appb-000007
即X i服从二项分布。其中，
Figure PCTCN2019122068-appb-000008
为从总数为m的目标分子中任意选取k个分子的组合数，
Figure PCTCN2019122068-appb-000009
为单个目标分子分配到该反应分区或液滴的概率。当目标分子总数m为待定常数时，该液滴中包含目标分子总数量X i的数学期望
Figure PCTCN2019122068-appb-000010
也为常数，同时如果概率
Figure PCTCN2019122068-appb-000011
足够小，那么X i近似服从参数为
Figure PCTCN2019122068-appb-000012
的泊松分布：
Figure PCTCN2019122068-appb-000013
特别地，当k＝0时，反应分区或液滴不包含目标分子的概率为：
Figure PCTCN2019122068-appb-000014
对应的，当k≥1时，反应分区或液滴包含有目标分子的概率为：
Figure PCTCN2019122068-appb-000015
那么进一步地，不包含目标分子的反应分区或液滴数量C 0为j的概率为：
Figure PCTCN2019122068-appb-000016
即C 0服从泊松二项分布。其中，
Figure PCTCN2019122068-appb-000017
为从总数为n的反应分区或液滴中任意选取j个不包含目标分子的反应分区或液滴的组合数。由此可计算出不包含目标分子的反应分区或液滴的体积分别为v q(q＝1，2，3，…，j)，且不包含目标分子的反应分区或液滴数量C 0为j的条件概率为：
Figure PCTCN2019122068-appb-000018
根据极大似然估计方法，可根据公式(6)推导得到使得该条件概率取最大值时需要满足的公式：
Figure PCTCN2019122068-appb-000019
由于公式(7)左右两端n，j，v i，v p，v q皆是通过对目标图像进行分析确定或统计得到的，为已知量，因此公式(7)变为仅含m唯一未知数的方程。因此可采用区间二分法、牛顿迭代法、弦截法、牛顿插值法等计算得到使得公式(7)左右两端相等时的最佳m值。此值即为乳化体系中所含待确定的目标分子总数量。
图3为本申请实施例提供的上述计算方法，对所采集到的图2中目标图像处理分析后得到的不同浓度DNA模板分子定量数据线性拟合结果。
在本实施例中，除了通过上述公式确定出待检测样本中目标分子的总数量外，还可以通过预先建立的用于确定目标分子的数量的统计分析模型，确定该待检测样本中目标分子的总数量。
具体地，将根据分区或液滴总数量、各反应分区或液滴的体积信息以及不包含目标分子的反应分区或液滴的数量输入至预先建立的分析模型中，该分析模型的输出即为该待检测样本中目标分子的总数量。
其中，预先建立的分析模型已学习得到了根据分区或液滴总数量、各反应分区或液滴的体积信息以及不包含目标分子的反应分区或液滴的数量，与目标分子之间的映射关系。
在本实施例中，在确定出检测样本中的目标分子的总数量之后，还可以通过进一步计算得到相应目标分子的浓度。
具体地，可根据待检测样本中目标分子的总数量以及待检测样本的体积信息，确定出待检测样本中目标分子的浓度。
本申请实施例提供的随机乳化数字绝对定量分析方法，对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，并对反应分区或液滴进行扩增处理，以及在扩增结束时，对扩增后的反应分区或液滴进行图像采集，得到目标图像；对目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量；根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及不包含目标分子的反应分区或液滴的数量，确定待检测样本中目标分子的总数量。由此，准确计算出待检测样本中目标分子的总数量，满足对任意浓度的待检测样本的绝对定量分析需求。
在本实施例中，为了验证上述计算目标分子总数方法的可行性，本实施例还提出一种用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法，方法应用在模拟系统中。需要说明的是，本发明的任意尺寸分区或任意体积分散液滴是指，将待定量的流体体系划分为若干个较小体积，形成隔离的反应分区或液滴。
图4为本申请实施例提供的一种模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法的流程示意图；
如图4所示，该方法可以包括：
步骤401，设置目标分子总数m，其中，m为大于或等于0的整数。
其中，设置方法可以为固定取值为某一常数，也可以为通过某函数取值为在某取值范围内的变量，每次模拟该变量取值为某一常数，结束后进行重新取值。
步骤402，设置反应分区或液滴的总数量n，根据设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值v i，其中，v i表示第i个反应分区或液滴的体积值，i＝1，2，3，…，n，其中，n为大于1的整数。
其中，所形成的所有反应分区或液滴的体积之和等于待定量流体体系的总体积。
其中，设置方法可以采用将待设置数值确定为某一个或若干个常数来实施，也可以采用在仿真终端中利用某离散或连续分布函数的某随机数或变量生成器来实施，或采用两种方法的任意组合来实施。
在本申请一个实施例中，上述反应分区或液滴体积还可以符合一定的分布规律，作为一种示例性的实施方式，用户还可以设置反应分区或液滴体积所服从的预设分布的参数信息。
对应地，根据设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值，包括：根据预设分布的参数信息和设置的反应分区或液滴的总数量n，形成n 个反应分区或液滴各自对应的体积值。
在本实施例中，预设分布包括对数高斯分布，参数信息包括均值、标准差和变异系数。
当然，上述预设分布还可以为其他分布。举例而言，预设分布为均匀分布，待定量流体体系的总体积为1μL，在形成不同体积的多个液滴时，可待定量流体体系的总体积将划分为各100个1nL，2nL，3nL和4nL的液滴，共400个多体积液滴。
步骤403，根据n个反应分区或液滴各自对应的体积值，计算出待定量流体体系的总体积。
步骤404，根据待定量流体体系的总体积，随机产生m组坐标数值集合，其中，坐标数值集合中元素的取值范围不超过待定量流体体系的总体积。
其中，坐标数值集合的维度可以为一维，二维或三维，本实施例对此不限定。
需要说明的是，本实施例在模拟时，是以坐标数值集合的维度为一维为例进行描述的。
在本实施例中，可采用随机数或变量生成器产生m组满足某特定分布的坐标数值集合。其中，坐标数值集合满足的某特定分布可以是均匀分布，高斯分布，对数高斯分布等。
步骤405，根据坐标数值集合的维度，将各反应分区或液滴的体积值表示为具有维度的依据预设次序连接的n个数值区间。
也就是说，将各分区或分散液滴体积值表示为具有维度的依据一定次序连接的n个数值区间。(例如一维的情况：可以按照v i的序号i，i＝1，2，3，…，n，来定次序连接，例如长度为v 1的区间在长度为v 2的区间左边，然后长度为v 2的区间的右侧是长度为v 3的区间，以此类推，最右边一个区间长度为v n)。
步骤406，确定n个数值区间中每个数值区间内所包含的坐标数值的数量。
其中，根据每个分区或分散液滴体积表示的数值区间内所包含的坐标数值的数量X i，即对应该分区或分散液滴体积内所包含的分子数量，分别统计包含0个分子、1个分子、2个分子、…、最高直至步骤401中的预设目标分子总数m的分区或分散液滴的数量C k，k＝0，1，2，…，m。
步骤407，统计出包含坐标数值数量为零的数值区间的总个数，并将统计出的总个数，作为不包含目标分子的反应分区或液滴的数量C 0。
步骤408，根据待定量流体体系的总体积
Figure PCTCN2019122068-appb-000020
反应分区或液滴的总数量n、各反应分区或液滴的体积值v i以及不包含目标分子的反应分区或液滴的数量C 0，确定出目标分子总数的估算值M。
步骤409，比较设置的目标分子总数m与目标分子总数的估算值M是否在预设误差范围内，如果在预设误差范围内，则确定模拟系统可用于执行数字绝对定量检测的计算。
根据本申请实施例的用于模拟执行任意尺寸分区或任意体积分散液滴来实现数字绝对定量检测的随机模拟方法。设计并执行数字绝对定量扩增实验，并且根据实验数据分析并获取各分区或液滴的尺寸数值，如各分区或液滴的体积v i，以及分区或液滴总数n。同时通过扩增反应可以使得含有目标分子的分区或液滴引起放大的扩增信号，而不包含目标分子的分区或液滴不会引起放大的扩增信号，从而根据不同反应状态确定每个分区或液滴含有目标分子或者不含有目标分子，统计得到不含目标分子的分区或液滴总数C 0。下面使用模拟系统执行绝对定量检测的计算：
设置预设目标分子总数m为最小值为0的直至让所有分区或分散液滴都至少包含1个目 标分子的某足够大数量的整数数值(假设为M)区间内的每一个整数，设置n个模拟分区或分散液滴尺寸(面积或体积)数值等于根据实验数据分析并获取的各分区或液滴的尺寸数值，如各分区或液滴的体积v i。每当m取定为某一整数值时，重复随机模拟方法步骤401至步骤407若干次，且该重复次数R为某一具有统计意义的足够大数值，如500，1000，10000等，具体次数R需权衡计算精度及计算开销可适当调整。每完成1次随机模拟实验均可得到对应的1个C 0结果(最小为0，最大为n)，并得到一对对应的预设m与C 0数值。当所有从0到M的共(M+1)个m预设取值的的R×(M+1)次随机模拟实验完成时，可得到共R×(M+1)对预设m与C 0数值。将对应同一C 0值的预设m值进行分类统计，计算每个m值的频度及概率，并且通过拟合或插值等方法获得m值的概率密度函数f(x)，由此计算出每个对应C 0值的m值的数学期望E(m)及方差D(m)。将通过扩增反应确定每个分区或液滴反应状态统计得到的不含目标分子的分区或液滴总数C 0的观测值与模拟的对应C 0的m值的概率密度函数f(x)进行比较分析，即可得到模拟计算法的计算结果E(m)以及对应的m取值置信区间[m min，m max]。
其中，需要说明的是，本实施例中后续描述的验证随机模拟方法的实施例均是以液滴为例进行描述。将随机乳化扩增模型通过简化为一维泊松过程来刻画描述的计算原理如图5所示。
验证随机模拟方法的实施例1：该实施例用于验证使用随机模拟方法执行随机乳化分区模拟的可行性，以及评价分区体积变异对绝对定量结果的影响。
1.预设分子数m为500，来模拟体系中目标分子数为500时的情况。
2.预设随机乳化所生成的分散液滴数量n为256，设置分散液滴体积v i服从均值为4的对数高斯分布(一般情况下随机乳化形成液滴体积都满足对数高斯分布)，分别设置分散液滴体积标准差为0.004、0.04、0.4、4、40与400，对应变异系数分别为0.001、0.01、0.1、1、10以及100。根据各确定参数随机产生256个体积数值。
3.求取256个分散液滴的体积总和
Figure PCTCN2019122068-appb-000021
在区间
Figure PCTCN2019122068-appb-000022
随机生成500个数值点，根据256个分散液滴在区间
Figure PCTCN2019122068-appb-000023
上所对应的子区间
Figure PCTCN2019122068-appb-000024
确定这500个数值点在各子区间的分布情况，分别统计每个子区间所包含的数值点的数量X i，以及统计包含k(k＝0，1，2，…，m)个数值点的分散液滴的数量C k。
4.根据C 0以及的分析计算方法，计算出所有分散液滴中的目标分子总数估算值M。分散液滴体积变异系数分别为0.001、0.01、0.1、1、10以及100时目标分子总数的估算值M分别为516.8、518.15、507.9、526.4、493.7以及522.05。
模拟实验的可视化结果如图6-图9所示，其中图6为变异系数为0.001时的模拟及计算可视化结果，分散液滴总体积为1024.0897，C 0为34，估算值M为516.8，图7为变异系数为0.1时的模拟及计算可视化结果，分散液滴总体积为1022.1397，C 0为36，估算值M为507.9，图8为变异系数为10时的模拟及计算可视化结果，分散液滴总体积为841.1841，C 0为154，估算值M为493.7，图9为变异系数为0.1时对双重目标分子随机乳化的模拟及计算可视化结果，两种目标分子总数分别为25与1000，预设分散液滴总体积为1062.4776，绿色标记的目标分子的C 0为233，估算值M为24.4，蓝色标记的目标分子2的C 0为15，估算值M为1010.8。
评价分区体积变异对绝对定量结果的影响的实施例2：该实施例采用本发明的随机模拟方法用于评价随机乳化所生成的分散液滴的体积变异对绝对定量结果的影响。
1.预设分子数m分别为1、2、5、10、20、50、100、200、500、1000、2000、5000、10000、20000、50000、100000。
2.预设随机乳化所生成的分散液滴数量n为256，设置分散液滴体积v i服从均值为4的对数高斯分布(一般情况下随机乳化形成液滴体积都满足对数高斯分布)，分别设置分散液滴体积标准差为0.004、0.04、0.4、4、40与400，对应变异系数分别为0.001、0.01、0.1、1、10以及100。根据各确定参数随机产生256个体积数值。
3.求取256个分散液滴的体积总和
Figure PCTCN2019122068-appb-000025
在区间
Figure PCTCN2019122068-appb-000026
随机生成上述m个数值点，根据256个分散液滴在区间
Figure PCTCN2019122068-appb-000027
上所对应的子区间
Figure PCTCN2019122068-appb-000028
确定这m个数值点在各子区间的分布情况，分别统计每个子区间所包含的数值点的数量，以及统计包含k(k＝0，1，2，…，m)个数值点的分散液滴的数量C k。
4.根据C 0以及的分析计算方法，计算出所有分散液滴中的目标分子总数估算值M。每次m与对应体积变异系数确定时将产生一个M，将这一过程重复500次，计算出这500个M的均值和标准差。
5.根据以上模拟数据，对同一体积变异系数条件下的所有m值以及对应M均值进行线性拟合，并标记误差线。在同一坐标系中对不同体积变异系数对应拟合曲线进行叠加，分析并评价分散液滴的体积变异对绝对定量精度、准度、动态范围等的影响。
模拟实验的可视化结果如图10所示，图中共有6条拟合曲线，分别对应体积变异系数为0.001、0.01、0.1、1、10以及100时的拟合结果。图中数据表明，体积变异对定量结果有较大影响，体积变异越大时，动态范围越宽。当变异系数为100时，仅需256个分散液滴即可对100000个目标分子完成准确定量。
验证模拟计算法的实施例的实施例3：该实施例采用本发明的随机模拟方法用于分析不含目标分子的分区或液滴总数C 0与目标分子总数m之间的映射关系，并由此计算出可能的m的取值范围以及最有可能取值M。
1.预设分子数m取值为从1到2001的所有整数，即1、2、3、…、2000、2001。
2.预设随机乳化所生成的分散液滴数量n为256，设置分散液滴体积v i服从均值为4的对数高斯分布(一般情况下随机乳化形成液滴体积都满足对数高斯分布)，设置分散液滴体积标准差为4，对应变异系数为1。根据各确定参数随机产生256个体积数值，且后续每次模拟均维持该体积参数不变。
3.求取256个分散液滴的体积总和
Figure PCTCN2019122068-appb-000029
在区间
Figure PCTCN2019122068-appb-000030
随机生成上述m个数值点，根据256个分散液滴在区间
Figure PCTCN2019122068-appb-000031
上所对应的的子区间
Figure PCTCN2019122068-appb-000032
确定这m个数值点在各子区间的分布情况，分别统计每个子区间所包含的数值点的数量，以及统计包含k(0≤k≤500)个数值点的分散液滴的数量C k。
4.每次预设的m值在从1到2001的所有整数中取定为某一数值时(256个体积数值保持不变)重复以上步骤，重复次数为1000次。每次重复均可得到一个的m与结果C 0的一对映射。当所有1000×2001次随机模拟实验完成时，可得到共1000×2001对预设m与C 0数值的对应关 系。将对应同一的C 0数值的预设m值进行分类并排序，统计其中m所有可能的取值、计算每个m取值的频度。将统计结果叠加在同一坐标系中，根据实际实验数据分析计算可能的m的取值范围以及最有可能取值M。
基于模拟计算法的可视化结果如图11的多峰直方图所示，其中坐标系横轴为可能的m取值范围，坐标系纵轴为每个m的取值频度。图中每个峰均代表对应于某一个C 0数值的所有可能的预设m值的数据统计结果，从左至右代表的C 0数值分别为255、240、225、210、195、180、165、150、135、120、105、90、75、60、45、30、15以及0。随着上述C 0数值的减小，所对应的的m的可能取值则在增加，且取值范围也在变大，说明C 0数值的减小，会导致需计算的M值的不确定度变得更大，从而影响绝对定量精度。此外，根据每个单峰所包含的m值频度数据点，可以拟合或插值计算出m值的概率密度函数，从而计算出模拟计算法的结果E(m)及对应m的置信区间[m min，m max]，即为本模拟计算法所得的分子总数计算结果及置信区间。
图12为本申请实施例提供的一种随机乳化数字绝对定量分析装置的结构示意图。
图12所示，该随机乳化数字绝对定量分析装置包括随机乳化处理模块110、扩增处理模块120、图像采集模块130、图像分析模块140和确定模块150，其中：
随机乳化处理模块110，用于对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，其中，待乳化体系包括待检测样本，反应分区或液滴的总数量为随机生成，总数量为大于1的正整数，反应分区或液滴随机形成，各体积为随机产生(或任意产生)，且体积总和不大于乳化体系的体积。
扩增处理模块120，用于对反应分区或液滴进行扩增处理。
图像采集模块130，用于在扩增处理结束时，对反应分区或液滴进行图像采集，得到目标图像。
图像分析模块140，用于对目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量。
确定模块150，用于根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及不包含目标分子的反应分区或液滴的数量，确定待检测样本中目标分子的总数量。
在本申请的一个实施例中，为了方便后续基于目标图像，快速分析出各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况，以及不包含目标分子的反应分区或液滴的数量，在图12所示的装置实施例的基础上，如图13所示，该装置还可以包括：
变形处理模块160，用于对扩增处理后的每个反应分区或液滴进行挤压变形处理。
在本申请的一个实施例中，图像分析模块140，具体用于：对目标图像中各反应分区或液滴对应的图像区域进行特征提取，以得到每个图像区域对应的特征信息。针对每个图像区域，将图像区域的特征信息与预设特征信息进行匹配。如果图像区域的特征信息与预设特征信息不匹配，则确定图像区域对应的反应分区或液滴中不包含目标分子。确定目标图像中与预设特征信息不匹配的图像区域总数，并将图像区域总数作为不包含目标分子的反应分区或液滴的数量。
在本申请一个实施例中，各反应分区或液滴中目标分子的数量服从独立非同分布的泊松分布，不包含目标分子的反应分区或液滴数量服从泊松二项分布，根据以下公式确定待检测 样本中目标分子的总数量：
Figure PCTCN2019122068-appb-000033
其中，m表示乳化体系中待确定的目标分子总数，n表示反应分区或液滴总数量，j表示不包含目标分子的反应分区或液滴的数量C 0的取值，v i(i＝1，2，3，…，n)表示第i个反应分区或液滴的体积，v p(p＝1，2，3，…，n-j)表示包含有目标分子的第p个反应分区或液滴的体积，v q(q＝1，2，3，…，j)表示不包含目标分子的第q个反应分区或液滴的体积，e为自然常数。以上，n，j，v i，v p，v q皆是通过对目标图像进行分析确定或统计得到的。
在本申请一个实施例中，预设扩增体系包括预设指示剂，在对反应分区或液滴进行扩增处理时，在检测到预设指示剂的指示信号强度不再变化时，确定扩增处理结束。
其中，需要说明的是，上述对随机乳化数字绝对定量分析方法实施例的解释说明也适用于该实施例的随机乳化数字绝对定量分析装置，此处不再赘述。
本申请实施例提供的随机乳化数字绝对定量分析装置，对预设容器中的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，并对反应分区或液滴进行扩增处理，以及在检测到扩增处理结束时，对扩增处理后的反应分区或液滴进行图像采集，得到目标图像；对目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量；根据各反应分区或液滴的体积信息、预设数量以及不包含目标分子的反应分区或液滴的数量，确定待检测样本中目标分子的总数量。由此，准确确定出待检测样本中目标分子的总数量，方便了对任意浓度的待检测样本的绝对定量分析需求。
图14为本申请实施例提供的一种模拟系统的结构示意图。其中，需要说明的是，模拟系统用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算。
图14所示，该模拟系统包括：
第一设置模块210，用于设置目标分子总数m，其中，m为大于或等于0的整数；
数据形成模块220，用于设置的反应分区或液滴的总数量n，根据设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值v i其中，v i表示第i个反应分区或液滴的体积值，i＝1，2，3，…，n，其中，n为大于1的整数；
第一计算模块230，用于根据n个反应分区或n个液滴各自对应的体积值，计算出待定量流体体系的总体积
Figure PCTCN2019122068-appb-000034
生成模块240，用于根据待定量流体体系的总体积，随机产生m组坐标数值集合，其中，坐标数值集合中元素的取值范围不超过待定量流体体系的总体积；
表示模块250，用于根据坐标数值集合的维度，将各反应分区或液滴的体积值表示为具有维度的依据预设次序连接的n个数值区间；
第一确定模块260，用于确定n个数值区间中每个数值区间内所包含的坐标数值的数量X i；
统计模块270，用于统计出包含坐标数值数量为零的数值区间的总个数，并将统计出的总个数，作为不包含目标分子的反应分区或液滴的数量C 0；
第二确定模块280，用于根据待定量流体体系的总体积
Figure PCTCN2019122068-appb-000035
反应分区或液滴的总数量n、各反应分区或液滴的体积值v i以及不包含目标分子的反应分区或液滴的数量C 0，确定出目标分子总数的估算值M；
验证模块290，用于比较设置的目标分子总数m与目标分子总数的估算值M是否在预设误差范围内，如果在预设误差范围内，则确定模拟系统可用于执行数字绝对定量检测的计算。
在本申请一个实施例中，该装置还可以包括：
第二设置模块，用于设置反应分区或液滴体积所服从的预设分布的参数信息；
数据形成模块220，具体用于：根据预设分布的参数信息和设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值。
其中，预设分布可以包括但不限于高斯分布、对数高斯分布、均匀分布，参数信息包括均值、标准差和变异系数。
其中，需要说明的是，前述对方法实施例的解释说明也适用于该实施例的模拟系统，该实施例对此不再赘述。
图15为本申请实施例提供的一种电子设备的结构示意图。该电子设备包括：
存储器1001、处理器1002及存储在存储器1001上并可在处理器1002上运行的计算机程序。
处理器1002执行程序时实现上述实施例中提供的随机乳化数字绝对定量分析方法，或上述实施例中提供的模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法。
进一步地，电子设备还包括：
通信接口1003，用于存储器1001和处理器1002之间的通信。
存储器1001，用于存放可在处理器1002上运行的计算机程序。
存储器1001可能包含高速RAM存储器，也可能还包括非易失性存储器(non-volatile memory)，例如至少一个磁盘存储器。
处理器1002，用于执行程序时实现上述实施例的随机乳化数字绝对定量分析方法。
如果存储器1001、处理器1002和通信接口1003独立实现，则通信接口1003、存储器1001和处理器1002可以通过总线相互连接并完成相互间的通信。总线可以是工业标准体系结构(Industry Standard Architecture，简称为ISA)总线、外部设备互连(Peripheral Component，简称为PCI)总线或扩展工业标准体系结构(Extended Industry Standard Architecture，简称为EISA)总线等。总线可以分为地址总线、数据总线、控制总线等。为便于表示，图15中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
可选的，在具体实现上，如果存储器1001、处理器1002及通信接口1003，集成在一块芯片上实现，则存储器1001、处理器1002及通信接口1003可以通过内部接口完成相互间的通信。
处理器1002可能是一个中央处理器(Central Processing Unit，简称为CPU)，或是特定集成电路(Application Specific Integrated Circuit，简称为ASIC)，或是被配置成实施本申 请实施例的一个或多个集成电路。
本实施例还提供一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如上的随机乳化数字绝对定量分析方法，或模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或隐含地包括至少一个该特征。在本申请的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该 程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本申请各实施例中的各功能单元可以集成在一个处理模块中，也可以是各单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
上述提到的存储介质可以是只读存储器，磁盘或光盘等。尽管上面已经示出和描述了本申请的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (18)

  1. 一种随机乳化数字绝对定量分析方法，其特征在于，包括：
    对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，其中，所述待乳化体系包括待检测样本，所述反应分区或液滴的总数量为随机生成，所述总数量为大于1的正整数，所述反应分区或所述液滴随机形成，各体积为随机产生，且体积总和不大于所述乳化体系的体积；
    对所述反应分区或液滴进行扩增处理；
    在扩增处理结束后，对所述反应分区或液滴进行图像采集，得到目标图像；
    对所述目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量；
    根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及所述不包含目标分子的反应分区或液滴的数量，确定所述待检测样本中目标分子的总数量。
  2. 如权利要求1所述的方法，其特征在于，在所述对所述反应分区或液滴进行图像采集，得到待分析目标图像之前，还包括：
    对扩增处理后的每个所述反应分区或液滴进行挤压变形处理。
  3. 如权利要求1所述的方法，其特征在于，对所述目标图像中各反应分区或液滴对应的图像区域进行分析，统计不包含目标分子的反应分区或液滴的数量，包括：
    对所述目标图像中各反应分区或液滴对应的图像区域进行特征提取，以得到每个图像区域对应的特征信息；
    针对每个图像区域，将所述图像区域的特征信息与预设特征信息进行匹配；如果所述图像区域的特征信息与预设特征信息不匹配，则确定所述图像区域对应的反应分区或液滴中不包含目标分子；
    确定所述目标图像中与所述预设特征信息不匹配的图像区域总数，并将所述图像区域总数作为不包含目标分子的反应分区或液滴的数量。
  4. 如权利要求1所述的方法，其特征在于，所述不包含目标分子的反应分区或液滴的数量服从泊松二项分布，根据以下公式确定所述待检测样本中目标分子的总数量：
    Figure PCTCN2019122068-appb-100001
    其中，m表示乳化体系中待确定的目标分子总数，n表示反应分区或液滴总数量，j表示不包含目标分子的反应分区或液滴的数量C 0的取值，v i(i＝1，2，3，…，n)表示第i个反应分区或液滴的体积，v p(p＝1，2，3，…，n-j)表示包含有目标分子的第p个反应分区或液滴的体积，v q(q＝1，2，3，…，j)表示不包含目标分子的第q个反应分区或液滴的体积， e为自然常数。
  5. 如权利要求1-4任一项所述的方法，其特征在于，所述预设扩增体系包括预设指示剂，在对所述反应分区或液滴进行扩增处理时，在检测到所述预设指示剂的指示信号强度不再变化时，确定扩增处理结束。
  6. 一种用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的目标分子总数计算方法，所述方法应用在模拟系统中，其特征在于，包括：
    设置目标分子总数m，其中，m为大于或等于0的整数；
    设置反应分区或液滴的总数量n，根据设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值v i，其中，v i表示第i个反应分区或液滴的体积值，I＝1，2，3，…，n，其中，n为大于1的整数；
    根据n个反应分区或n个液滴各自对应的体积值，计算出待定量流体体系的总体积
    Figure PCTCN2019122068-appb-100002
    根据所述待定量流体体系的总体积，随机产生m组坐标数值集合，其中，所述坐标数值集合中元素的取值范围不超过所述待定量流体体系的总体积
    Figure PCTCN2019122068-appb-100003
    根据所述坐标数值集合的维度，将各反应分区或液滴的体积值表示为具有所述维度的依据预设次序连接的n个数值区间；
    确定所述n个数值区间中每个数值区间内所包含的坐标数值的数量；
    统计出包含坐标数值数量为零的数值区间的总个数，并将统计出的总个数，作为不包含目标分子的反应分区或液滴的数量C 0；
    根据所述待定量流体体系的总体积
    Figure PCTCN2019122068-appb-100004
    所述反应分区或液滴的总数量n、所述各反应分区或液滴的体积值v i以及不包含目标分子的反应分区或液滴的数量C 0，确定出所述目标分子总数的估算值M；
    比较设置的目标分子总数m与所述目标分子总数的估算值M是否在预设误差范围内，如果在预设误差范围内，则确定所述模拟系统可用于执行数字绝对定量检测的计算。
  7. 如权利要求6所述的方法，其特征在于，还包括：
    设置反应分区或液滴体积所服从的预设分布的参数信息；
    所述根据设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值，包括：
    根据所述预设分布的参数信息和设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值。
  8. 如权利要求7所述的方法，其特征在于，所述预设分布包括高斯分布、对数高斯分布、均匀分布，所述参数信息包括均值、标准差和变异系数。
  9. 一种随机乳化数字绝对定量分析装置，其特征在于，包括：
    随机乳化处理模块，用于对预设容器内的待乳化体系进行随机乳化处理，得到若干隔离的反应分区或液滴，其中，所述待乳化体系包括待检测样本，所述反应分区或液滴的总数量为随机生成，所述总数量为大于1的正整数，所述反应分区或所述液滴随机形成，各体积为随机产生，且体积总和不大于所述乳化体系的体积；
    扩增处理模块，用于对所述反应分区或液滴进行扩增处理；
    图像采集模块，用于在检测到扩增处理结束时，对所述反应分区或液滴进行图像采集，得到目标图像；
    图像分析模块，用于对所述目标图像中各反应分区或液滴对应的图像区域进行分析，得到各反应分区或液滴的体积信息、确定其内部待检测目标分子的存在情况，统计不包含目标分子的反应分区或液滴的数量；
    确定模块，用于根据反应分区或液滴总数量、各反应分区或液滴的体积信息及其内部待检测目标分子的存在情况、以及所述不包含目标分子的反应分区或液滴的数量，确定所述待检测样本中目标分子的总数量。
  10. 如权利要求9所述的装置，其特征在于，还包括：
    变形处理模块，用于对扩增处理后的每个所述反应分区或液滴进行挤压变形处理。
  11. 如权利要求9所述的装置，其特征在于，所述图像分析模块，具体用于：
    对所述目标图像中各反应分区或液滴对应的图像区域进行特征提取，以得到每个图像区域对应的特征信息；
    针对每个图像区域，将所述图像区域的特征信息与预设特征信息进行匹配；如果所述图像区域的特征信息与预设特征信息不匹配，则确定所述图像区域对应的反应分区或液滴中不包含目标分子；
    确定所述目标图像中与所述预设特征信息不匹配的图像区域总数，并将所述图像区域总数作为不包含目标分子的反应分区或液滴的数量。
  12. 如权利要求9所述的装置，其特征在于，所述不包含目标分子的反应分区或液滴的数量服从泊松二项分布，根据以下公式确定所述待检测样本中目标分子的总数量：
    Figure PCTCN2019122068-appb-100005
    其中，m表示乳化体系中待确定的目标分子总数，n表示反应分区或液滴总数量，j表示不包含目标分子的反应分区或液滴的数量C 0的取值，v i(i＝1，2，3，…，n)表示第i个反应分区或液滴的体积，v p(p＝1，2，3，…，n-j)表示包含有目标分子的第p个反应分区或液滴的体积，v q(q＝1，2，3，…，j)表示不包含目标分子的第q个反应分区或液滴的体积，e为自然常数。
  13. 如权利要求9-12任一项所述的装置，其特征在于，所述预设扩增体系包括预设指示剂，在对所述反应分区或液滴进行扩增处理时，在检测到所述预设指示剂的指示信号强度不再变化时，确定扩增处理结束。
  14. 一种模拟系统，其特征在于，所述模拟系统用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算，包括：
    第一设置模块，用于设置目标分子总数m，其中，m为大于或等于0的整数；
    数据形成模块，用于设置反应分区或液滴的总数量n，根据设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值v i，其中，v i表示第i个反应分区或液滴的体积值，i＝1，2，3，…，n，其中，n为大于1的整数；
    第一计算模块，用于根据n个反应分区或n个液滴各自对应的体积值，计算出待定量流体体系的总体积
    Figure PCTCN2019122068-appb-100006
    生成模块，用于根据所述待定量流体体系的总体积
    Figure PCTCN2019122068-appb-100007
    随机产生m组坐标数值集合，其中，所述坐标数值集合中元素的取值范围不超过所述待定量流体体系的总体积；
    表示模块，用于根据所述坐标数值集合的维度，将各反应分区或液滴的体积值表示为具有所述维度的依据预设次序连接的n个数值区间；
    第一确定模块，用于确定所述n个数值区间中每个数值区间内所包含的坐标数值的数量；
    统计模块，用于统计出包含坐标数值数量为零的数值区间的总个数，并将统计出的总个数，作为不包含目标分子的反应分区或液滴的数量C 0；
    第二确定模块，用于根据所述待定量流体体系的总体积
    Figure PCTCN2019122068-appb-100008
    所述反应分区或液滴的总数量n、所述各反应分区或液滴的体积值v i以及不包含目标分子的反应分区或液滴的数量C 0，计算出所述目标分子总数的估算值M；
    验证模块，用于比较设置的目标分子总数m与所述目标分子总数的估算值M是否在预设误差范围内，如果在预设误差范围内，则确定所述模拟系统可用于执行数字绝对定量检测的计算。
  15. 如权利要求14所述的模型系统，其特征在于，还包括：
    第二设置模块，用于设置反应分区或液滴体积所服从的预设分布的参数信息；
    所述数据形成模块，具体用于：
    根据所述预设分布的参数信息和设置的反应分区或液滴的总数量n，形成n个反应分区或n个液滴各自对应的体积值。
  16. 如权利要求15所述的模型系统，其特征在于，所述预设分布包括但不限于高斯分布、对数高斯分布、均匀分布，所述参数信息包括均值、标准差和变异系数。
  17. 一种电子设备，其特征在于，包括：
    存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1-5中任一所述的随机乳化数字绝对定量分析方法，或权利6-8中任一项所述的用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法。
  18. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1-5中任一所述的随机乳化数字绝对定量分析方法，或权利6-8中任一项所述的用于模拟执行任意尺寸分区或任意体积的分散液滴来实现数字绝对定量检测的计算方法。
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