WO2020181602A1 - 绝对定量方法、装置、计算机设备和存储介质 - Google Patents

绝对定量方法、装置、计算机设备和存储介质 Download PDF

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WO2020181602A1
WO2020181602A1 PCT/CN2019/081772 CN2019081772W WO2020181602A1 WO 2020181602 A1 WO2020181602 A1 WO 2020181602A1 CN 2019081772 W CN2019081772 W CN 2019081772W WO 2020181602 A1 WO2020181602 A1 WO 2020181602A1
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molecules
preset
total number
value
variable
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French (fr)
Inventor
夏贇
顾大勇
董瑞玲
陈艳
刘笔锋
何建安
徐云庆
谢聪贤
陈枝楠
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SHENZHEN INTERNATIONAL TRAVEL HEALTH CARE CENTER
Shenzhen Institute of Advanced Technology of CAS
Shenzhen Academy of Inspection and Quarantine
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SHENZHEN INTERNATIONAL TRAVEL HEALTH CARE CENTER
Shenzhen Institute of Advanced Technology of CAS
Shenzhen Academy of Inspection and Quarantine
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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
    • C12Q1/6851—Quantitative amplification
    • 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
    • G16B15/00—ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment

Definitions

  • This application relates to the field of computer technology, in particular to an absolute quantitative method, device, computer equipment and computer storage medium.
  • dPCR Digital Polymerase Chain Reaction, Digital Polymerase Chain Reaction
  • dPCR is a biotechnology improvement of conventional polymerase chain reaction methods, which can be used for direct absolute quantification of nucleic acid strands including DNA, cDNA or RNA clones.
  • the traditional dPCR absolute quantification method divides a sample system into hundreds, tens of thousands or even tens of millions, and assigns them to different reaction partitions. Each partition contains 0 or 1 copy of the target molecule, which is divided into each reaction partition.
  • the target molecule is amplified by PCR, and after the amplification is completed, the fluorescent signal of each reaction zone is statistically analyzed to calculate the number of nucleic acid molecules in the solution.
  • the current absolute quantitative methods are prone to random errors and lead to inaccurate absolute quantitative results.
  • the embodiments of the application provide an absolute quantification method, device, computer equipment, and computer storage medium.
  • An absolute quantification method comprising: obtaining a plurality of first preset numbers of molecules; according to the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules corresponding to each second preset number of molecules The number of partitions is processed to obtain a function containing multiple variables of the first preset number of molecules and the total number of molecules; the value of the variable of the total number of molecules is obtained according to the function containing multiple variables of the first preset number of molecules and the total number of molecules.
  • An absolute quantification device comprising: an acquisition module for acquiring a plurality of first preset molecular numbers; a function obtaining module for obtaining a plurality of first preset molecular numbers and a plurality of second preset molecular numbers The number of partitions corresponding to each second preset number of molecules in the number is processed to obtain a function containing multiple variables of the first preset number of molecules and the total number of molecules; The function processing of the variable of the total number of molecules obtains the value of the variable of the total number of molecules.
  • a computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: acquiring a plurality of first preset numbers of molecules; The number of molecules and the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules are processed to obtain a function containing a plurality of variables of the first preset number of molecules and the total number of molecules; The function processing of the number of molecules and the variable of the total number of molecules obtains the value of the variable of the total number of molecules.
  • a computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the following steps are achieved: obtaining a plurality of first preset molecular numbers; The number of partitions corresponding to each second preset number of molecules in the second preset number of molecules is processed to obtain a function containing multiple variables of the first preset number of molecules and the total number of molecules; according to including multiple first preset number of molecules and total number of molecules The function processing of the variable gets the value of the variable of the total number of molecules.
  • Fig. 1 is an application environment diagram of the absolute quantitative method in an embodiment.
  • Figure 2 is an application environment diagram of the absolute quantification method in another embodiment.
  • Figure 3 is a schematic flow chart of an absolute quantification method in an embodiment.
  • Figure 4 is a schematic flow chart of the absolute quantification method in another embodiment.
  • 5 is a schematic diagram of molecular distribution and curves corresponding to multiple arrays with preset area parameters of 1, 5, 25, and 125 in an embodiment.
  • 6 is a schematic diagram of molecular distribution and curves corresponding to multiple arrays with preset area parameters of 1, 5, 25, and 125 in another embodiment.
  • FIG. 7 is a schematic diagram of the molecular distribution of multiple arrays with preset area parameters of 1, 5, 25, and 125 and the verification of the algorithm results in an embodiment.
  • FIG. 8 is a schematic diagram of the molecular distribution of multiple arrays with preset area parameters of 1, 5, 25 and 125 and the verification of the algorithm results in another embodiment.
  • Figure 9 is a schematic diagram of the dynamic range comparison of four absolute quantification methods in an embodiment.
  • Fig. 10 is a schematic diagram of a boundary distribution curve with a 95% quantitative confidence level of monodisperse volume digital PCR in an embodiment.
  • FIG. 11 is a schematic diagram of obtaining the number of partitions corresponding to each second preset number of molecules in a plurality of second preset numbers of molecules in an embodiment.
  • FIG. 12 is a schematic diagram of the results obtained by using the absolute quantitative method and the Poisson single point estimation calculation method in the embodiment of the application in an embodiment.
  • Fig. 13 is a schematic diagram of the known total number of molecules scattered in the array in an embodiment.
  • Fig. 14 is a schematic diagram of scattering molecules with a known total number of molecules in an array in another embodiment.
  • Figure 15 is a structural block diagram of an absolute quantification device in an embodiment.
  • Fig. 16 is an internal structure diagram of a computer device in an embodiment.
  • the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • PCR detection terminal 202 can be, but not limited to, PCR detection chips, PCR multiwell plates, various fluorescent microscope optical sensors, ordinary PCR machines, gradient PCR machines, in-situ PCR machines, real-time Fluorescence quantitative PCR machine or digital PCR machine, etc.
  • the terminal 204 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • an absolute quantification method is provided.
  • the method is applied to the terminal 102 in FIG. 1 or the terminal 204 in FIG. 2 as an example for description, including:
  • Step 302 Acquire multiple first preset numbers of molecules.
  • the first preset number of molecules refers to multiple values corresponding to the first preset molecular parameters pre-stored in the terminal. Wherein, the first preset number of molecules includes at least 0, 1, and 2, and may also include 3, 4, and 5, or more, and the like is not limited thereto.
  • Step 304 processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules, to obtain variables containing a plurality of first preset numbers of molecules and total number of molecules The function.
  • the number of the second predetermined number of molecules is not limited, and the second predetermined number of molecules contains at least 0, and may also include 1, 2, 3, 4, and 5, or more, and the like is not limited thereto.
  • the number of partitions corresponding to each second preset number of molecules includes at least the number of partitions corresponding to the second preset number of molecules being zero. Then the terminal can obtain the number of partitions corresponding to 1 according to the total number of partitions and the number of partitions corresponding to 0.
  • the number of the second preset number of molecules is less than or equal to the number of the first preset number of molecules.
  • the first preset number of molecules is 0, 1, 2, 3, 4, and 5, totaling 6, and the second preset number of molecules can be 0 and 1, totaling 2.
  • the number of partitions corresponding to each second preset number of molecules can be obtained through terminal simulation or through a determination algorithm from a PCR (Polymerase Chain Reaction) detection terminal.
  • the terminal obtains the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules. For example, there are 256 partitions in the array. Among the 256 partitions, the number of partitions corresponding to no molecule is 255, the number of partitions corresponding to 1 molecule is 1, the number of partitions corresponding to 2 molecules is 0, and the number of partitions corresponding to 3 molecules is 0. The number of partitions is 0, etc. but not limited to this.
  • the terminal processes according to the plurality of first preset numbers of molecules and the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules, and obtains a variable containing a plurality of first preset numbers of molecules and total number of molecules function.
  • the function containing the variable of the total number of molecules can be used to characterize the functional relationship between the first preset number of molecules and the number of partitions corresponding to each first preset number of molecules.
  • the terminal uses the first preset number of molecules as a variable in the x-axis, and the number of partitions corresponding to the first preset number of molecules as a variable in the y-axis.
  • the terminal performs fitting or interpolation according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules to obtain a variable containing the total number of molecules function.
  • the interpolation method is also called the interpolation method, which uses the function values of several known points in a certain interval to make an appropriate specific function. For example, if the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules is known, a function including the variables of the first preset number of molecules and the total number of molecules can be made according to the first preset number of molecules.
  • Interpolation can be performed by Lagrangian interpolation, Newton interpolation, Newton iteration, interval dichotomy, chord interception, Jacobi iteration, or Newton-Cortez numerical integration method, etc., to obtain the variable including the total number of molecules.
  • the function can be performed by Lagrangian interpolation, Newton interpolation, Newton iteration, interval dichotomy, chord interception, Jacobi iteration, or Newton-Cortez numerical integration method, etc.
  • the terminal may also be based on the number of partitions corresponding to each second preset number of molecules among the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules, and the number of partitions corresponding to each first preset number of molecules.
  • the weight of is obtained as a function containing the variable of the total number of molecules.
  • the terminal may also perform fitting or interpolation processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules, to obtain the A function of the first preset number of molecules and the total number of molecules.
  • step 306 the value of the variable of the total number of molecules is obtained by processing according to a function including a plurality of variables of the first preset number of molecules and the total number of molecules.
  • the terminal obtains the value of the variable of the total number of molecules by solving a function including a plurality of variables of the first preset number of molecules and the total number of molecules.
  • the relationship between the total number of molecules, and more information corresponding to the number of molecules can be obtained, such as the number of partitions corresponding to each first preset number of molecules, etc., to avoid calculation when only the second preset number of molecules is 0 and 1.
  • the random error caused by the total number of molecules makes the absolute quantitative results more accurate and broadens the dynamic range.
  • processing is performed according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules to obtain a number of first
  • the function of preset number of molecules and total number of molecules including:
  • Step 402 Obtain multiple preset area parameter values or multiple preset volume parameter values.
  • the value of the preset area parameter refers to the value of the area parameter corresponding to each partition in an array.
  • the value of the multiple preset area parameters refers to the value of the area parameter corresponding to each partition in each of the multiple arrays.
  • the value of the preset volume parameter refers to the value of the volume parameter corresponding to each partition in an array.
  • the value of the multiple preset volume parameters refers to the value of the volume parameter corresponding to each partition in each of the multiple arrays.
  • the value of the preset area parameter and the value of the preset volume parameter may be arranged in an equal ratio or in other forms.
  • the terminal acquires the values of multiple preset area parameters or the values of multiple preset volume parameters.
  • the preset area parameter is A
  • the value of the area parameter corresponding to each partition in the first array is 1
  • the value of the area parameter corresponding to each partition in the second array is 5
  • each partition in the third array corresponds to
  • the value of the area parameter of is 25, and the value of the area parameter corresponding to each partition in the fourth array is 125.
  • Step 404 Obtain the value of each preset area parameter or the value of each volume parameter according to the plurality of first preset numbers of molecules, and the values of the plurality of preset area parameters or the values of the plurality of preset volume parameters.
  • the expected value function containing the variable of the total number of molecules corresponding to the first preset number of molecules.
  • the expected value function containing the variable of the total number of molecules corresponding to each first preset number of molecules for each area parameter value or each volume parameter value can be used to characterize the relationship between each first preset number of molecules and the corresponding number of partitions. Functional relationship.
  • the terminal obtains the expected value function containing the total number of molecules variable corresponding to each first preset number of molecules of the value of each preset area parameter according to the values of the multiple first preset numbers of molecules and the multiple preset area parameters .
  • the terminal obtains the expected value function containing the variable of the total number of molecules corresponding to each first preset number of molecules of the value of each preset volume parameter according to the values of the multiple first preset numbers of molecules and multiple preset volume parameters.
  • k is the first preset number of molecules
  • m is the total number of molecules
  • n is the number of partitions
  • the number of partitions is a constant.
  • ⁇ is the average number of molecules, that is, the ratio of the total number of molecules to the number of partitions.
  • f is the model function to be fitted
  • A is the preset area parameter or the preset volume parameter
  • E(C A,k ) is the preset area parameter or the preset volume parameter is the first preset number of molecules in A
  • the expected number of partitions C A,k Table 1 below takes k as 0, 1, 2, and 3 as an example to obtain the expected value function of the variable containing the total number of molecules corresponding to each first preset number of molecules of the value of each preset area parameter.
  • Step 406 According to the expected value function, the number of partitions corresponding to each preset area parameter or each second preset number of molecules of each preset volume parameter is processed to obtain variables containing multiple first preset numbers of molecules and total number of molecules The fitting function.
  • the fitting function is a function obtained by fitting a function containing multiple variables of the first preset number of molecules and the total number of molecules according to the number of partitions corresponding to each second preset number of molecules.
  • the terminal corresponds to the expected value function of the variable containing the total number of molecules corresponding to each first preset number of molecules, and the expected value function corresponding to each second preset number of molecules of each preset area parameter.
  • the number of partitions is obtained, and a fitting function containing multiple variables of the first preset number of molecules and the total number of molecules is obtained.
  • each first preset number of molecules corresponds to the expected value function containing the total number of molecules variable, and the number of partitions corresponding to each second preset number of molecules of each preset volume parameter, A fitting function containing multiple variables of the first preset number of molecules and the total number of molecules is obtained.
  • the fitting function can be used to characterize the functional relationship between a plurality of first preset numbers of molecules and the number of partitions corresponding to each first preset number of molecules.
  • the expression of the fitting function only contains an unknown variable m, and y is the number of partitions corresponding to the second preset number of molecules. Then the fitting function includes 16 summation terms, and a fitting function containing multiple variables of the first preset number of molecules, preset area parameters, and total number of molecules can be obtained.
  • variable of the total number of molecules is obtained by processing the function containing multiple variables of the first preset number of molecules and the total number of molecules, including:
  • Step 408 According to the fitting function processing including a plurality of first preset number of molecules and total number of molecules variables, the values of the total number of molecules variables corresponding to the plurality of preset area parameters or the plurality of preset volume parameters are obtained.
  • the value of the variable of the total number of molecules corresponding to the plurality of preset area parameters or the plurality of preset volume parameters is the value of a variable of the total number of molecules of the multiple arrays.
  • each partition in the array corresponds to a preset area parameter or a preset volume parameter.
  • the terminal processes according to a fitting function including a plurality of first preset number of molecules and total number of molecules variables to obtain values of the total number of molecules variables corresponding to the plurality of preset area parameters or the plurality of preset volume parameters. For example, there are 4 preset area parameters, and each preset area parameter corresponds to an array, array 1, array 2, array 3, and array 4. Then the terminal processes array 1, array 2, array 3, and array 4 according to the fitting function. The sum of the total number of molecules in.
  • the fitting function is obtained, and the value of the variable of the total number of molecules can be processed to obtain different areas.
  • the expected value function corresponding to each preset number of molecules under the volume reducing the error between the expected value function corresponding to different areas or volumes and the number of partitions corresponding to each second preset number of molecules, and reducing the area or volume change
  • the absolute quantitative error caused, and no matter the total number of molecules, that is, regardless of the concentration of the molecules, the total number of molecules can be quantified, which improves the dynamic range of absolute quantification, the accuracy of absolute quantification, and the resolution of absolute quantification.
  • processing is performed according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules to obtain a plurality of first preset numbers of molecules and molecules.
  • the functions of the total variable include:
  • step (a1) the values of multiple preset area parameters or the values of multiple preset volume parameters are obtained.
  • Step (a2) according to the number of first preset molecules, and the values of multiple preset area parameters or the values of multiple preset volume parameters, and the value of the same preset area parameter or the value of the same preset volume parameter.
  • the number of partitions corresponding to each second preset number of molecules is processed to obtain the value of each preset area parameter or the value of each preset volume parameter corresponding to the function containing the first preset number of molecules and the variable of the total number of molecules.
  • each preset area parameter or the value of each preset volume parameter corresponds to the function containing the variable of the first preset number of molecules and the total number of molecules refers to the value of each preset area parameter or the value of the preset volume parameter
  • the value corresponds to a function, that is, the value of each preset area parameter corresponds to a function containing the variables of the first preset number of molecules and the total number of molecules; the value of each preset volume parameter corresponds to one containing the first preset number of molecules and the total number of molecules Variable functions.
  • the terminal processes according to the values of the plurality of first preset numbers of molecules and the plurality of preset area parameters, as well as the number of partitions corresponding to each second preset number of molecules, and obtains that the value of each preset area parameter contains The fitting function or interpolation function of the first preset number of molecules and the total number of molecules.
  • the terminal processes according to the values of the multiple first preset numbers of molecules and multiple preset volume parameters, as well as the number of partitions corresponding to each second preset number of molecules, and obtains that the value of each preset volume parameter contains the first A fitting function or interpolation function for the variables of the preset number of molecules and the total number of molecules.
  • the terminal performs interpolation based on the values of multiple first preset numbers of molecules and multiple preset area parameters or multiple preset volume parameters, and the number of partitions corresponding to each second preset number of molecules
  • an interpolation function containing the variables of the first preset number of molecules and the total number of molecules corresponding to the value of each preset area parameter or the value of each preset volume parameter is obtained.
  • the terminal only obtains the number of partitions corresponding to the second preset number of molecules of 0, 1, 2, and 3, and performs interpolation calculations to obtain the first preset number of molecules of 0, 1, 2, 3, 4,..., 100 etc. and the interpolation function f(k) of the total number of molecules.
  • the terminal can determine an interpolation function based on the 3 numerical points in the number of partitions corresponding to any second preset number of molecules, and then evaluate the uncertainty of the fourth point fitting, etc., to determine the fourth point and the arbitrary 3
  • the error between points, then 4 interpolation results can be obtained.
  • the terminal selects the result with the smallest accidental error from the 4 interpolation results as the fourth point data.
  • variable of the total number of molecules is obtained by processing the function containing multiple variables of the first preset number of molecules and the total number of molecules, including:
  • Step (a3) According to the value of each preset area parameter or the value of each preset volume parameter, the function containing the total number of molecules is processed to obtain the value of multiple preset area parameters or the value of multiple preset volume parameters. The value of the variable of the total number of molecules corresponding to the value.
  • the terminal processes to obtain values of the total number of molecules variable corresponding to the values of the multiple preset area parameters.
  • the terminal processes the value of the total number of molecules variable corresponding to the values of multiple preset volume parameters according to the fitting function or interpolation function containing the total number of molecules variable corresponding to the value of each preset volume parameter.
  • the terminal can process the value of each preset area parameter or the fitting function corresponding to the value of each preset volume including the variable of the total number of molecules to obtain the values of multiple preset area parameters or multiple presets.
  • the value of the volume parameter corresponds to the value of the variable of the total number of molecules.
  • the terminal calculates the fitting function containing the variable of the total number of molecules corresponding to the value of each preset area parameter or the value of each preset volume parameter by a Newton iteration method or the like.
  • the value of each preset area parameter or the value of each preset volume parameter corresponds to the minimum value
  • the value of the variable of the total number of molecules corresponding to the minimum value is determined.
  • the terminal sums the value of each preset area parameter or the value of the total number of molecules corresponding to the value of each preset volume parameter to obtain the value of multiple preset area parameters or the value of multiple preset volume parameters.
  • the terminal can obtain the values of multiple preset area parameters or multiple preset volume parameters according to the value of each preset area parameter or the interpolation function containing the total number of molecules corresponding to the value of each preset volume.
  • the value of corresponds to the value of the variable of the total number of molecules.
  • the interpolation function is f(k)
  • the value of the variable of the total number of molecules corresponding to each preset area parameter or each preset volume parameter can be obtained.
  • the terminal sums the values of the total number of molecules corresponding to each preset area parameter or each preset volume parameter to obtain the value of multiple preset area parameters or the total number of molecules corresponding to the value of multiple preset volume parameters The value of the variable.
  • the function including the variables of the first preset number of molecules and the total number of molecules is obtained by processing, and the values of the multiple preset area parameters
  • the value or the value of the total number of molecules corresponding to multiple preset volume parameters can obtain the value of the total number of molecules under different areas or different volumes, reducing the absolute quantitative error caused by changes in area or volume, and whether it is a molecule
  • the total number that is, regardless of the concentration of the molecule, the total number of molecules can be quantified, which improves the dynamic range of absolute quantification, the resolution of absolute quantification, and the accuracy of absolute quantification.
  • FIG. 5 it is a schematic diagram of molecular distribution and curves corresponding to multiple arrays with preset area parameters of 1, 5, 25, and 125 in one embodiment.
  • FIG. 6 it is a schematic diagram of molecular distribution and curves corresponding to multiple arrays with preset area parameters of 1, 5, 25, and 125 in another embodiment.
  • there are 256 partitions in each array that is, the total number of partitions is 256.
  • the total number of molecules in the picture A is 1, the total number of molecules in the B picture is 2, and the total number of molecules in the C picture is 5; the total number of molecules in the D picture is 10, the total number of molecules in the E picture is 20, and the total number of molecules in the F picture is 50; The total number of molecules is 100, the total number of molecules in the H picture is 200, and the total number of molecules in the I picture is 500.
  • the total number of molecules in the J picture is 1000, the total number of molecules in the K picture is 2000, and the total number of molecules in the L picture is 5000; the total number of molecules in the M picture is 10,000, the total number of molecules in the N picture is 20,000, and the total number of molecules in the O picture is 50,000; Is 100,000, the total number of molecules in the Q picture is 200,000, and the total number of molecules in the R picture is 500,000.
  • the terminal uses the point-in-space process method to simulate the distribution of nucleic acid molecules in the solution space, and uses the preset area parameters of equal proportions, that is, the arrays corresponding to the four preset area parameters of 1, 5, 25 and 125 to simulate the two under the condition of different numbers of molecules.
  • Dimension Poisson process At the same time, the number of partitions corresponding to the number of molecules in the array is recorded from 0 to 15, and the continuous curve of the expected value of the Poisson binomial distribution random variable theory of the array corresponding to each preset area parameter is drawn and compared with the results of the statistical simulation experiment.
  • m(A) is the total number of molecules in the array per area.
  • nucleic acid molecules with high diversity and specificity can be used as biomarkers to identify and detect different biological species, individuals, and even single genes.
  • molecular testing will not only be used for disease prediction and early or even ultra-early diagnosis, but will also be used to guide routine physical examinations to make routine physical examinations more targeted, thereby changing the concept of health management.
  • it can also be used for specific populations, special items and specific environments, such as high-sensitivity qualitative or quantitative detection of pathogens in specific locations and time windows, to guide disease management, and then guide safe medication, and help doctors customize personalized treatment plans , In order to more effectively control and treat diseases or epidemics.
  • qPCR Real-time Quantitative PCR
  • RT-qPCR reverse transcription PCR, reverse transcription PCR
  • nucleic acid detection of infectious disease pathogens is the current global diagnostic and detection technology for infectious diseases.
  • qPCR technology has good detection sensitivity and specificity, its detection performance is still severely restricted by the inhibitor tolerance of the amplification enzyme, and the minimum detection limit does not meet the requirements of single molecule detection. And once non-specific amplification or contamination of the test substance occurs in the system, the entire test result will be false positive, leading to a serious diagnosis accident.
  • the qPCR detection process relies on more sophisticated fluorescence monitoring equipment, which requires real-time feedback of the fluorescence signal after each cycle—at least 40 cycles later. The detection is time-consuming and the hardware cost is high. Therefore, from the perspective of the current detection performance and the future development direction of molecular diagnosis of infectious disease pathogens, qPCR technology has not reached the goal of "detection, rapid detection, and accurate detection”.
  • digital PCR digital nucleic acid amplification methods represented by dPCR have been used in precision medicine, microbial detection, food Security and other fields have been widely used.
  • the digital nucleic acid amplification method uses absolute quantification, without standard products as a reference, and uses molecular counting to achieve precise quantification of nucleic acids.
  • the detection system applied on qPCR can be seamlessly transferred to the dPCR instrument system to meet the laboratory's requirements for detection results.
  • the digital nucleic acid amplification method developed from the single-molecule nucleic acid amplification technology, by dividing the micro-upgraded reaction solution into nanoliter or pico-upgraded uniformly, eliminating the number of empty partitions that can be known and occupying a considerable proportion, while increasing The local nucleic acid sample concentration in the remaining effective reaction zone.
  • the relative concentration ratios of nucleic acid molecules and reaction reagents in these partitions have been greatly increased, so the probability of intermolecular collisions has risen sharply, which makes the interaction between primers, templates and polymerases. The effect becomes more rapid, frequent, direct and stable.
  • the digital nucleic acid amplification method can provide more reliable pathogen load determination results than qPCR.
  • FIG. 7 is a schematic diagram of the molecular distribution of multiple arrays with preset area parameters of 1, 5, 25, and 125 and the verification of the algorithm results in an embodiment.
  • FIG. 8 is a schematic diagram of the molecular distribution of multiple arrays with preset area parameters of 1, 5, 25 and 125 and the verification of the algorithm results in another embodiment.
  • Fig. 9 is a comparison diagram of the dynamic range of four absolute quantitative methods in an embodiment.
  • input m represents the total number of known preset molecules.
  • SVEstSum M represents the calculation result of the single volume Poisson estimation and summation.
  • MVEst M represents the calculation result of multiple volume Poisson joint estimation.
  • SVFitSum M means that in this application, the value of each preset area parameter or the value of each preset volume parameter corresponding to the function containing the total number of molecules is processed to obtain the values of multiple preset area parameters or multiple preset volume parameters.
  • MVFit M means that the values of the total number of molecules corresponding to multiple preset area parameters or multiple preset volume parameters are obtained by processing according to a fitting function including multiple first preset number of molecules and total number of molecules in the embodiments of the application The calculation result.
  • Dynamic range refers to the range in which the object to be measured has a measurable response.
  • MVEst M NaN, where NaN indicates that the calculation result is invalid data, that is, the corresponding method of MVEst M cannot calculate the corresponding total number of molecules.
  • the corresponding method of MVFit M can still be used for absolute quantification.
  • MVFit M 440922.3.
  • the error of the calculation result of MVFit M in the total number of known molecules of 5 ⁇ 500060 are all within a small range. For example, when the total number of known molecules is 50, the total number of molecules obtained by MVFit M is 51.0047. When the total number of known molecules is 500, the total number of molecules obtained by MVFit M is 524.9766.
  • the upper limit of the dynamic range of the SVEstSum M method is 10 3 ⁇ 10 4
  • the upper limit of the dynamic range of the SVFitSum M method is about 10 4
  • the upper limit of the dynamic range of the MVEst M method is 10 4 ⁇ 10 5
  • the upper limit of the dynamic range of the MVFit M method The upper limit of the dynamic range is about 10 6 .
  • the dynamic range of MVFit M method can be improved by at least 1.5 orders of magnitude.
  • the dynamic range of the method in the embodiment of the present application can be increased by at least 1.5 orders of magnitude.
  • FIG. 10 it is a schematic diagram of a boundary distribution curve with a 95% quantitative confidence level of the monodisperse volume digital PCR in an embodiment.
  • the horizontal axis is the total proportion of the negative partitions, the highest is 1, that is, the number of molecules in all partitions is 0; the lowest is 0, that is, each partition contains at least 1 molecule.
  • the boundary distance will increase rapidly.
  • the accuracy of the digital absolute quantitative method will be greatly reduced when the total number of molecules in a single-volume array is particularly low, that is, when the proportion of negatives is close to 1, and when the proportion of negatives is very high, that is, the proportion of negatives is close to 0.
  • This reduction in accuracy will also bring about a reduction in quantitative resolution. It is more difficult to determine the concentration of two different molecular numbers that are closer, and two types of determination errors are more likely to occur.
  • the solution of obtaining the values of multiple preset area parameters or the values of multiple preset volume parameters in the embodiments of the present application can overcome the above technical defects. For example, the proportion of negatives in a certain area array is close to 1 or 0, but the proportion of negatives in a larger area array cannot be close to 1, or the proportion of negatives in a smaller area array cannot be close to 0.
  • the absolute quantification method before acquiring the plurality of first preset numbers of molecules, further includes: scattering the molecules in each preset area parameter or the array corresponding to each preset volume parameter; and obtaining each The number of molecules contained in the partitions in the array; according to the number of molecules contained in the partitions in each array, count the partitions corresponding to each second preset number of molecules in the multiple second preset numbers of molecules in each array Quantity.
  • the array is composed of partitions, and there can be multiple partitions in an array.
  • the number of arrays is not limited, and each array corresponds to a preset area parameter or a preset volume parameter.
  • the molecule can be a simulated molecule or a real nucleic acid molecule.
  • the terminal acquires at least one array, and at the terminal, the molecules are scattered in the array corresponding to each preset area parameter by means of Poisson distribution or spatial point process. Or, the terminal scatters the molecules in the array corresponding to each preset volume parameter by means of Poisson distribution or spatial point process.
  • the terminal obtains the number of molecules contained in the partition in each array. That is, the terminal obtains the number of molecules contained in the partition in each array in at least one array.
  • the terminal counts the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules in each array according to the number of molecules contained in the partitions in each array.
  • the number of molecules in partition 1 of array 1 is 0, the number of molecules in partition 2 of array 1 is 1, the number of molecules in partition 3 of array 1 is 1, and the number of molecules in partition 4 of array 1 is 2. Then the terminal obtains, according to the number of molecules contained in the partitions in the array 1, that the number of partitions corresponding to the preset number of molecules of 0 is 1, the number of partitions corresponding to the preset number of molecules of 1 is 2, and the preset number of molecules is 2 corresponds to The number of partitions is 1.
  • the molecules can be randomly scattered in a PCR (Polymerase Chain Reaction) detection terminal for each preset area parameter or an array corresponding to each preset volume parameter.
  • the PCR detection terminal detects the number of molecules contained in each partition in the array.
  • the terminal obtains the number of molecules contained in the partitions in each array from the PCR detection terminal, and counts the number of molecules in each of the second preset numbers of molecules in each array according to the number of molecules contained in the partitions in each array. The number of partitions corresponding to the second preset number of molecules.
  • the molecules are scattered in the array corresponding to each preset area parameter, and the number of molecules contained in the partition in each array is obtained, and the number of partitions corresponding to the preset number of molecules in each array is counted , Can get the number of partitions corresponding to each preset number of molecules in different areas or different volumes, and get the expected value function corresponding to each preset number of molecules to obtain the residual square sum, reducing the absolute quantification caused by changes in area or volume Error, improve the accuracy of absolute quantification.
  • the absolute quantification method before obtaining multiple preset numbers of molecules, further includes: scattering molecules in the array; obtaining the number of molecules contained in each partition in the array; The number of contained molecules counts the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules.
  • arrays are not limited.
  • An array can contain multiple partitions.
  • the terminal scatters molecules in an array, and obtains the number of molecules contained in each partition in the array.
  • the terminal counts the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules according to the number of molecules contained in each partition.
  • the molecules can be randomly scattered in the array of the PCR detection terminal.
  • the PCR detection terminal detects the number of molecules contained in each partition in the array.
  • the terminal obtains the number of molecules contained in the partitions in each array from the PCR detection terminal, and counts the number of molecules in each array according to the number of molecules contained in the partitions in each array. Let the number of molecules correspond to the number of partitions.
  • the molecules are scattered in the array, the number of molecules contained in each partition is obtained, and the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules is counted, and the molecules can be obtained.
  • the number of a variety of molecular information is not limited to the number of molecules, thereby improving the accuracy of absolute quantification.
  • the examples of this application make full use of the high sensitivity and high specificity of digital nucleic acid amplification at the single-molecule level to establish a model and method to further subdivide the positive partitions, which will lose the previous single all or no end point determination.
  • the method of subdividing positive partitions can be achieved by using digital nucleic acid amplification combined with an amplification curve determination method. For example, the concentration of 1 molecule and 2 molecules differ by 100%, and the concentration of 2 molecules and 3 molecules differ by 50%.
  • the specific principles and results of subdividing the positive partitions are shown in Figure 11.
  • FIG. 11 it is a schematic diagram of obtaining the number of partitions corresponding to each second preset number of molecules in a plurality of second preset numbers of molecules in an embodiment.
  • the large square represents an array
  • the small square represents a partition.
  • the first picture in Figure 11 is unknown, which is the random distribution of molecules in the array.
  • the black dots in the picture represent molecules.
  • the second picture in Figure 11 is a traditional detection method, that is, it can only judge whether there is a molecule in the partition, with a white square representing 0 and a black square representing 1.
  • the third diagram in FIG. 11 is the method used in the embodiment of the application.
  • the number of partitions corresponding to the second preset number of molecules can be obtained by checking the squares, that is, counting the number of molecules in the partitions.
  • FIG. 12 it is a schematic diagram of the results obtained by using the absolute quantitative method and the Poisson single point estimation calculation method in the embodiment of the present application in an embodiment.
  • Figure 12 is obtained by obtaining a plurality of first preset numbers of molecules; processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules A function of the variable of the first preset number of molecules and the total number of molecules; a fitting curve obtained by a solution process for obtaining the value of the variable of the total number of molecules according to a function containing multiple variables of the first preset number of molecules and the total number of molecules.
  • the terminal uses a 16 ⁇ 16 256-bit two-dimensional square array to simulate the two-dimensional Poisson process of different total numbers of molecules, that is, the two-dimensional Poisson process of the actual value shown in Figure 12.
  • the terminal randomly distributes the predicted total number of molecules in the two-dimensional array, and records the second preset number of molecules contained in each square in the array, and then counts that the second preset number of molecules in the array at this time is 0
  • the total number of squares of ⁇ 3 is the number of partitions.
  • the terminal obtains the estimated value according to the single-point Poisson estimation and the fitted value according to the absolute quantitative method in the embodiment of the present application respectively, and calculates the total number of molecules.
  • the data point is the point of the number of partitions corresponding to the second preset number of molecules.
  • the second preset number of molecules is 0, 1, 2, and 3 and the first preset number of molecules is 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11. , 12, 13, 14, and 15.
  • Table 3 the corresponding relationship between the first preset number of molecules and the number of partitions is listed.
  • n is the total number of partitions
  • i is the number of partitions corresponding to the second preset number of molecules being 0.
  • the calculation formula of Poisson single point estimation is The estimated number of molecules is:
  • the number of partitions 0 is 246, and the number of partitions 1 is 10. Therefore, the calculation formula for Poisson single point estimation, the estimated number of molecules is:
  • the estimated number of molecules is:
  • the calculation method of the fitted value is the method shown in the embodiment of this application.
  • the corresponding partition number is 255, when the second preset number of molecules is 1, the corresponding partition number is 1, and when the second preset number of molecules is 2, it corresponds to The number of partitions is 0, and the number of partitions corresponding to the second preset number of molecules is 3 is 0.
  • E(C k ) n*(m/n) k *e -m/n /k!
  • the total number of molecules that can be quantified by single-point estimation is about 1 to 1200, which is about 3. Orders of magnitude. It can be seen from Fig. 6 that when the true value of the number of molecules is 1615, the number of partitions corresponding to the second preset number of molecules being 0 is 0, and the calculation result suddenly changes to infinity, causing a large quantitative error.
  • the absolute quantification method in the embodiment of the present application can still calculate the total number of molecules by using the obtained 1, 2, and 3 molecules corresponding to the number of partitions as 2, 8, and 23 respectively.
  • FIG. 13 it is a schematic diagram of an embodiment where molecules with a known total number of molecules are scattered in the array.
  • FIG. 14 it is a schematic diagram of the known total number of molecules scattered in the array in another embodiment.
  • the abscissas in Fig. 13 and Fig. 14 both represent the number of first preset molecules, and the ordinates both represent the number of partitions.
  • the processing obtains the number of first preset molecules and the number of molecules.
  • the fitting function of the total variable includes:
  • the fitting function may be a residual sum-of-squares function, or may be a fitting function obtained by other fitting methods, etc. are not limited to this.
  • the sum term refers to the sum term in the fitting function.
  • the uncertainty reflects the reliability of the estimated result of the fitting function, and can be converted into a percentage form. When a certain estimation result has a high uncertainty, such as 50%, etc., it indicates that the fitting function is not reasonable.
  • the terminal determines the summation item in the fitting function according to the expected value function and the number of partitions corresponding to each second preset number of molecules.
  • the terminal obtains a fitting function including a weight and a plurality of first preset number of molecules and a variable of the total number of molecules according to the summation term and the corresponding weight, where the weight is negatively related to the uncertainty of the variable of the total number of molecules. That is, the greater the uncertainty of the total number of molecules, the smaller the weight; the less the uncertainty of the total number of molecules, the greater the weight.
  • the absolute quantification method further includes: calculating the uncertainty value according to the value of the total number of molecules; determining the sum term according to the expected value function and the number of partitions corresponding to each second preset number of molecules; The value of the degree is used as the weight corresponding to the summation item; the value of the variable of the total number of molecules is obtained by processing the fitting function including the weight and a plurality of variables of the first preset number of molecules and the total number of molecules.
  • the summation term is determined according to the expected value function and the number of partitions corresponding to each second preset number of molecules, and then the fitting function is obtained according to the summation term and the corresponding weight, which can reduce the random error to the total number of molecules.
  • the impact of the value of improve the accuracy of absolute quantification.
  • the absolute quantification method further includes:
  • step (b1) the goodness of fit is obtained according to the fitting function.
  • the goodness of fit can be used to reflect the accuracy of a certain statistical model describing the data set.
  • the terminal obtains the goodness of fit according to the fitting function.
  • the goodness of fit can be calculated by the following formula:
  • the weighting matrix refers to the weighting of the original model to make it a new matrix without heteroscedasticity.
  • the Jacobian matrix is a matrix in which the first-order partial derivatives are arranged in a certain way.
  • the total number of partitions refers to the total number of partitions in the array. When there are multiple arrays, it is the sum of the number of partitions in the multiple arrays.
  • the terminal obtains a preset weight matrix and a Jacobian matrix obtained according to the average number of molecules.
  • the average number of molecules is obtained based on the ratio of the total number of molecules to the total number of partitions.
  • the weighting matrix is the following formula:
  • ⁇ is the average number of molecules.
  • step (b3) the estimated variance is obtained according to the Jacobian matrix and the weighting matrix.
  • the terminal calculates the estimated variance according to the Jacobian matrix and the weighting matrix.
  • the uncertainty C of the average number of molecules ⁇ can be calculated by the following matrix
  • J is the Jacobian matrix
  • W is the weighting matrix
  • the diagonal element c 11 of the uncertainty C matrix is the estimated variance of the average number of molecules ⁇ :
  • step (b4) the uncertainty corresponding to the average number of molecules is obtained according to the goodness of fit and the estimated variance.
  • the terminal calculates the uncertainty corresponding to the average molecular number variable according to the goodness of fit and the estimated variance.
  • step (b5) the weight corresponding to the summation item is determined according to the uncertainty corresponding to the average number of molecules.
  • the terminal determines the weight corresponding to the summation item according to the uncertainty corresponding to the average number of molecules variable.
  • the terminal can obtain the value of the average number of molecules, calculate the value of the uncertainty corresponding to the value of the average number of molecules, and process the value of the uncertainty to obtain the weight corresponding to the summation term. .
  • the weight is negatively related to the value of uncertainty.
  • the absolute quantitative method further includes: obtaining a confidence interval according to the uncertainty.
  • the confidence interval refers to the estimated interval of the parameter to be fitted, such as the estimated interval of the variable of the total number of molecules.
  • the confidence interval shows the degree to which the true value of this parameter has a certain probability of falling around the measurement result, and it gives the degree of credibility of the measured value of the measured parameter.
  • the terminal can obtain a t-score layout confidence interval with a confidence level of 1- ⁇ about the average number of molecules ⁇ as:
  • the value of t 15, 0.025 with 15 degrees of freedom can be obtained from books related to probability theory, or the t distribution function in MATLAB (Matrix Laboratory) can also be obtained directly.
  • the goodness of fit is obtained according to the fitting function, the weighting matrix and the Jacobian matrix are obtained, and the estimated variance is obtained, thereby determining the uncertainty of the average molecular number variable, and determining the weight according to the uncertainty.
  • Combining goodness and uncertainty to judge the reliability of the fitting results, adding weight to the fitting function, can reduce the influence of random errors on the value of the total number of molecules, and improve the accuracy of absolute quantification.
  • the processing obtains the number of first preset molecules and the number of molecules.
  • the fitting function of the total variable also includes:
  • step (c1) the sum term is determined according to the expected value function and the number of partitions corresponding to each second preset number of molecules.
  • step (c2) a variance function corresponding to each first preset number of molecules is obtained according to a plurality of preset numbers of molecules.
  • the terminal obtains the value of each preset area parameter or the value of each preset volume parameter and the variance function corresponding to each preset number of molecules according to the multiple preset numbers of molecules.
  • the variance function changes accordingly.
  • the variance D(C A,1 ) A ⁇ m ⁇ e -A ⁇ ⁇ (1-A ⁇ m/n ⁇ e -A ⁇ m/n );
  • D(C A,2 ) n ⁇ (A ⁇ m/n) 2 ⁇ e -A ⁇ m/n ⁇ [2-(A ⁇ m/n) 2 ⁇ e -A ⁇ m/n ]/4;
  • D(C A,2 ) n ⁇ (A ⁇ m/n) 2 ⁇ e -A ⁇ m/n ⁇ [2-(A ⁇ m/n) 2 ⁇ e -A ⁇ m/n ]/4, Wait but not limited to this.
  • Step (c3) processing the variance function to obtain the weight corresponding to each first preset number of molecules.
  • the terminal uses the reciprocal of the variance function as the weight corresponding to each first preset number of numerators. Or, the terminal multiplies the variance function by a constant, etc. but not limited to this.
  • step (c4) the fitting function including the weight is obtained according to the summation term and the corresponding weight.
  • the terminal obtains a fitting function including the weight and a plurality of variables of the first preset number of molecules and the total number of molecules according to the summation item and the corresponding weight.
  • the sum term is determined according to the expected value function and the number of partitions corresponding to the second preset number of molecules, and the corresponding weight is obtained after the variance function is obtained.
  • the weight is assigned to the sum term, which can reduce the fitting function and the second prediction. Set the error between the number of molecules corresponding to the number of partitions to improve the accuracy of absolute quantification.
  • the absolute quantification method further includes: for the same test object, detecting a preset number of times to obtain the value of the preset number of molecular total variables; determining the average value of the preset number of values; The value is the total number of molecules in the same test object.
  • the same test object refers to the same test sample.
  • the same sample detected in the PCR detection terminal, or the same spatial point process simulated in the terminal may be 1 time, 2 times,..., 100 times, etc., and is not limited thereto.
  • the number of partitions corresponding to the second preset number of molecules changes a little during each detection process.
  • the terminal detects the preset number of times for the same test object, and obtains the value of the variable of the total number of molecules each time.
  • the terminal determines the average value of the preset times and takes the average value as the total number of molecules of the same test object.
  • the terminal may eliminate the total number of molecules variable corresponding to the preset number of times. The value of.
  • the value of the variable of the total number of molecules of the preset number is obtained, and the average value is determined.
  • the average value is regarded as the total number of molecules of the same test object, which can reduce the molecular motion.
  • the random error of the system improves the accuracy of absolute quantification.
  • an absolute quantitative method includes the following steps:
  • step (d1) the molecules are scattered in the array.
  • step (d2) the number of molecules contained in each partition in the array is obtained.
  • Step (d3) counting the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules according to the number of molecules contained in each partition.
  • Step (d4) obtaining a plurality of first preset numbers of molecules.
  • Step (d5) processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules to obtain a plurality of first preset numbers of molecules and a total number of molecules Variable functions.
  • step (d6) the value of the variable of the total number of molecules is obtained by processing according to a function including a plurality of variables of the first preset number of molecules and the total number of molecules.
  • step (d7) for the same test object, the preset number of times is detected, and the value of the total number of molecules of the preset number of times is obtained.
  • step (d8) the average value of the preset times is determined.
  • step (d9) the average value is used as the total number of molecules of the same test object.
  • the absolute quantification method by acquiring a plurality of preset molecular numbers, and processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules,
  • the function of the variable of the first preset number of molecules and the total number of molecules, the value of the variable of the total number of molecules can be obtained according to the function containing multiple variables of the first preset number of molecules and the total number of molecules, and multiple first preset numbers of molecules and molecules can be obtained
  • the relationship between the total number, and more information corresponding to the number of molecules can be obtained, such as the number of partitions, etc., to avoid random errors caused by only using 0 and 1 to calculate the number of molecules, and make the absolute quantitative results more accurate.
  • an absolute quantitative method includes the following steps:
  • step (e1) the molecules are scattered in the array corresponding to each preset area parameter or each preset volume parameter.
  • step (e2) the number of molecules contained in the partition in each array is obtained.
  • Step (e3) Count the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules in each array according to the number of molecules contained in the partitions in each array.
  • Step (e4) obtaining a plurality of first preset numbers of molecules.
  • Step (e6) Obtain the value of each preset area parameter or the value of each volume parameter according to the values of the plurality of first preset numbers of molecules and the plurality of preset area parameters or the values of the plurality of preset volume parameters The expected value function containing the variable of the total number of molecules corresponding to each first preset number of molecules.
  • Step (e7) Determine the summation item according to the expected value function and the number of partitions corresponding to each second preset number of molecules;
  • Step (e8) obtaining a variance function corresponding to each first preset number of molecules according to a plurality of preset numbers of molecules;
  • Step (e9) processing the variance function to obtain the weight corresponding to each first preset number of numerators
  • step (e10) the fitting function including the weight is obtained according to the summation term and the corresponding weight.
  • step (e11) the value of the variable of the total number of molecules corresponding to the plurality of preset area parameters or the plurality of preset volume parameters is obtained by processing according to a fitting function including a plurality of first preset number of molecules and total number of molecules.
  • step (e12) for the same test object, the preset number of times is detected, and the value of the variable of the total number of molecules of the preset number of times is obtained.
  • step (e14) the average value is used as the total number of molecules of the same test object.
  • the function including the variables of the first preset number of molecules and the total number of molecules is obtained by processing, and the values of the multiple preset area parameters
  • the value or the value of the total number of molecules corresponding to multiple preset volume parameters can obtain the value of the total number of molecules under different areas or different volumes, reducing the absolute quantitative error caused by changes in area or volume, and whether it is a molecule
  • the total number that is, regardless of the concentration of the molecule, the total number of molecules can be quantified, which improves the dynamic range of absolute quantification, the resolution of absolute quantification, and the accuracy of absolute quantification.
  • an absolute quantitative method includes the following steps:
  • step (f1) the molecules are scattered in the array corresponding to each preset area parameter or each preset volume parameter.
  • step (f2) the number of molecules contained in the partition in each array is obtained.
  • Step (f3) Count the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules in each array according to the number of molecules contained in the partitions in each array.
  • Step (f4) obtaining a plurality of first preset numbers of molecules.
  • Step (f6) corresponding to the values of multiple first preset numbers of molecules and multiple preset area parameters or multiple preset volume parameters, and the same preset area parameter value or the same preset volume parameter value
  • the number of partitions corresponding to each second preset number of molecules is processed to obtain the value of each preset area parameter or the value of each preset volume parameter corresponding to a function containing the variables of the first preset number of molecules and the total number of molecules.
  • step (f8) for the same test object, the preset times are detected, and the values of the variables of the total number of molecules of the preset times are obtained.
  • Step (f9) Determine the average value of the preset times.
  • step (f10) the average value is used as the total number of molecules of the same test object.
  • the function including the variables of the first preset number of molecules and the total number of molecules is obtained by processing, and the values of the multiple preset area parameters
  • the value or the value of the total number of molecules corresponding to multiple preset volume parameters can obtain the value of the total number of molecules under different areas or different volumes, reducing the absolute quantitative error caused by changes in area or volume, and whether it is a molecule
  • the total number that is, regardless of the concentration of the molecule, the total number of molecules can be quantified, which improves the dynamic range of absolute quantification, the resolution of absolute quantification, and the accuracy of absolute quantification.
  • steps in the flowcharts of FIGS. 3-4 are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least part of the steps in Figure 3-4 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. These sub-steps or stages The execution order of is not necessarily performed sequentially, but may be performed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.
  • an absolute quantification device including: an acquisition module 1502, a function calculation module 1504, and a total number of molecules determination module 1506, wherein:
  • the obtaining module 1502 is used to obtain multiple first preset numbers of molecules.
  • the function obtaining module 1504 is used for processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules to obtain the number of first preset molecules. And a function of the variable of the total number of molecules.
  • the total number of molecules determining module 1506 is configured to obtain the value of the variable of the total number of molecules according to a function including a plurality of first preset numbers of molecules and variables of the total number of molecules.
  • the above absolute quantification device by acquiring a plurality of preset molecular numbers, and processing according to the number of partitions corresponding to each second preset number of molecules in the plurality of first preset numbers of molecules and the plurality of second preset numbers of molecules,
  • the function of the variable of the first preset number of molecules and the total number of molecules, the value of the variable of the total number of molecules can be obtained according to the function containing multiple variables of the first preset number of molecules and the total number of molecules, and multiple first preset numbers of molecules and molecules can be obtained
  • the relationship between the total number, and more information corresponding to the number of molecules can be obtained, such as the number of partitions, etc., to avoid random errors caused by only using 0 and 1 to calculate the number of molecules, and make the absolute quantitative results more accurate.
  • the obtaining module 1502 is used to obtain the values of multiple preset area parameters or the values of multiple preset volume parameters.
  • the function obtaining module 1504 is used to obtain the value of each preset area parameter or the value of each volume parameter according to the values of the plurality of first preset numbers of molecules and the plurality of preset area parameters or the values of the preset volume parameters.
  • the expected value function containing the variable of the total number of molecules corresponding to each first preset number of molecules of the value; the number of partitions corresponding to each second preset number of molecules of each preset area parameter or each preset volume parameter according to the expected value function , Processing to obtain a fitting function containing multiple variables of the first preset number of molecules and the total number of molecules.
  • the total number of molecules determining module 1506 is configured to process the value of the total number of molecules variable corresponding to the plurality of preset area parameters or the plurality of preset volume parameters according to the fitting function processing including the plurality of first preset number of molecules and the total number of molecules variable.
  • the fitting function is obtained, and the value of the variable of the total number of molecules can be processed to obtain different areas.
  • the expected value function corresponding to each preset number of molecules under the volume reducing the error between the expected value function corresponding to different areas or volumes and the number of partitions corresponding to each second preset number of molecules, and reducing the area or volume change
  • the absolute quantitative error caused, and no matter the total number of molecules, that is, regardless of the concentration of the molecules, the total number of molecules can be quantified, which improves the dynamic range of absolute quantification, the resolution of absolute quantification and the accuracy of absolute quantification.
  • the obtaining module 1502 is used to obtain the values of multiple preset area parameters or the values of multiple preset volume parameters.
  • the function obtaining module 1504 is used to determine the value of the same preset area parameter or the same preset volume parameter according to the values of the plurality of first preset numbers of molecules and the plurality of preset area parameters or the values of the plurality of preset volume parameters.
  • the number of partitions corresponding to each second preset number of molecules corresponding to the value is processed to obtain the value of each preset area parameter or the value of each preset volume parameter corresponding to the function containing the variables of the first preset number of molecules and the total number of molecules .
  • the total number of molecules determination module 1506 is used to process the function containing the total number of molecules variable corresponding to the value of each preset area parameter or the value of each preset volume parameter to obtain the values of multiple preset area parameters or multiple preset volume parameters The value of corresponds to the value of the variable of the total number of molecules.
  • the function including the variables of the first preset number of molecules and the total number of molecules is obtained by processing, and the values of the multiple preset area parameters are obtained.
  • the value or the value of the total number of molecules corresponding to multiple preset volume parameters can obtain the value of the total number of molecules under different areas or different volumes, reducing the absolute quantitative error caused by changes in area or volume, and whether it is a molecule
  • the total number that is, regardless of the concentration of the molecule, the total number of molecules can be quantified, which improves the dynamic range of absolute quantification, the resolution of absolute quantification, and the accuracy of absolute quantification.
  • the absolute quantification device further includes a statistics module.
  • the obtaining module 1502 is used to scatter molecules in the array corresponding to each preset area parameter or each preset volume parameter; and obtain the number of molecules contained in the partition in each array.
  • the statistics module is used to count the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules in each array according to the number of molecules contained in the partitions in each array.
  • the molecules are scattered in the array corresponding to each preset area parameter, and the number of molecules contained in the partition in each array is obtained, and the number of partitions corresponding to the preset number of molecules in each array is counted.
  • the absolute quantification device further includes a statistics module.
  • the obtaining module 1502 is used to scatter the molecules in the array; obtain the number of molecules contained in each partition in the array.
  • the statistics module is used to count the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules according to the number of molecules contained in each partition.
  • the molecules are scattered in the array, the number of molecules contained in each partition is obtained, and the number of partitions corresponding to each second preset number of molecules in the plurality of second preset numbers of molecules is counted, and the molecules can be obtained
  • the number of a variety of molecular information is not limited to the number of molecules, thereby improving the accuracy of absolute quantification.
  • the function obtaining module 1504 is further configured to determine a summation item according to the expected value function and the number of partitions corresponding to each second preset number of molecules; according to the summation item and the corresponding weight, obtain the summation item including the weight and the multiple A preset fitting function of the number of molecules and the variable of the total number of molecules, where the weight is negatively correlated with the uncertainty of the variable of the total number of molecules.
  • the summation term is determined according to the expected value function and the number of partitions corresponding to each second preset number of molecules, and then the fitting function is obtained according to the summation term and the corresponding weight, which can reduce the random error to the total number of molecules.
  • the impact of the value of improve the accuracy of absolute quantification.
  • the function obtaining module 1504 is also used to obtain the goodness of fit according to the fitting function; obtain the weighting matrix and the Jacobian matrix corresponding to the average number of molecules, where the average number of molecules is variable based on the total number of molecules and the partition The ratio of the total number is obtained; the estimated variance is obtained according to the Jacobian matrix and the weighting matrix; the uncertainty corresponding to the average number of molecules is obtained according to the goodness of fit and the estimated variance.
  • the goodness of fit is obtained according to the fitting function, the weighting matrix and the Jacobian matrix are obtained, and the estimated variance is obtained, thereby determining the uncertainty of the average molecular number variable, and determining the weight according to the uncertainty.
  • Combining goodness and uncertainty to judge the reliability of the fitting results, adding weight to the fitting function, can reduce the influence of random errors on the value of the total number of molecules, and improve the accuracy of absolute quantification.
  • the function obtaining module 1504 is further configured to determine a summation item according to the expected value function and the number of partitions corresponding to each second preset number of molecules; to obtain each first preset number of molecules according to a plurality of preset numbers of molecules The variance function corresponding to the number; the variance function is processed to obtain the weight corresponding to each first preset number of molecules; the fitting function including the weight is obtained according to the summation term and the corresponding weight.
  • the summation term is determined according to the expected value function and the number of partitions corresponding to the second preset number of molecules, and the corresponding weight is obtained after the variance function is obtained.
  • the weight is assigned to the summation term, which can reduce the fitting function and the second preset number. Set the error between the number of molecules corresponding to the number of partitions to improve the accuracy of absolute quantification.
  • the absolute quantitative device further includes a repeat detection module.
  • the repeated detection module is used to detect the preset number of times for the same test object to obtain the value of the total number of molecules of the preset number of times; determine the average value of the preset number of values; use the average value as the molecule of the same test object total.
  • the value of the variable of the total number of molecules is obtained for the preset number of times, and the average value is determined.
  • the random error of the system improves the accuracy of absolute quantification.
  • Each module in the above absolute quantitative device can be implemented in whole or in part by software, hardware and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the steps corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 16.
  • the computer equipment includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores a step system and a computer program.
  • the internal memory provides an environment for the operation of the step system and the computer program in the non-volatile storage medium.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer program is executed by the processor to realize an absolute quantitative method.
  • the display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen
  • the input device of the computer equipment can be a touch layer covered on the display screen, or it can be a button, a trackball or a touchpad set on the housing of the computer equipment , It can also be an external keyboard, touchpad, or mouse.
  • FIG. 16 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device including a memory and a processor, and a computer program is stored in the memory, and the processor implements the steps in the foregoing method embodiments when the computer program is executed.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink), DRAM (SLDRAM), memory bus (Rambus), direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

一种绝对定量方法，包括：获取多个第一预设分子数；根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数；根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。

Description

绝对定量方法、装置、计算机设备和存储介质
相关申请的交叉引用
本申请要求于2019年03月14日提交中国专利局、申请号为201910192256.X、发明名称为“绝对定量方法、装置、计算机设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及计算机技术领域，特别是涉及一种绝对定量方法、装置、计算机设备和计算机存储介质。
背景技术
随着分子生物学技术的发展，出现了dPCR(Digital Polymerase Chain Reaction，数字多聚酶链式反应)技术，它是一种核酸分子绝对定量技术。dPCR是一种常规聚合酶链反应方法的生物技术改进，可用于包括DNA，cDNA或RNA等克隆扩增核酸链的直接绝对定量。传统的dPCR绝对定量方法通过将一个样本体系分成几百、几万甚至上千万份，分配到不同的反应分区，每个分区包含0或1个拷贝的目标分子，在每个反应分区中分别对目标分子进行PCR扩增，扩增结束后对各个反应分区的荧光信号进行统计学分析，从而计算出溶液中的核酸分子数。然而目前的绝对定量方法，容易出现随机误差而导致绝对定量结果不准确。
发明内容
本申请实施例提供一种绝对定量方法、装置、计算机设备和计算机存储介质。
一种绝对定量方法，所述方法包括：获取多个第一预设分子数；根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数；根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。
一种绝对定量装置，所述装置包括：获取模块，用于获取多个第一预设分子数；函数求取模块，用于根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数；分子总数确定模块，用于根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。
一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现以下步骤：获取多个第一预设分子数；根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数；根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。
一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现以下步骤：获取多个第一预设分子数；根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数；根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。
本发明的一个或多个实施例的细节在下面的附图和描述中提出。本发明的其它特征、目的和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为一个实施例中绝对定量方法的应用环境图。
图2为另一个实施例中绝对定量方法的应用环境图。
图3为一个实施例中绝对定量方法的流程示意图。
图4为另一个实施例中绝对定量方法的流程示意图。
图5为一个实施例中预设面积参数为1、5、25和125的多重阵列对应的分子分布以及曲线示意图。
图6为另一个实施例中预设面积参数为1、5、25和125的多重阵列对应的分子分布以及曲线示意图。
图7为一个实施例中预设面积参数为1、5、25和125的多重阵列分子分布以及算法结果验证示意图。
图8为另一个实施例中预设面积参数为1、5、25和125的多重阵列分子分布以及算法结果验证示意图。
图9为一个实施例中四种绝对定量方法的动态范围对比示意图。
图10为一个实施例中单分散体积数字PCR的定量置信水平为95％的边界分布曲线示意图。
图11为一个实施例中获取多个第二预设分子数中每个第二预设分子数对应的分区数量的示意图。
图12为一个实施例中通过本申请实施例中的绝对定量方法和泊松单点估计计算方法的结果示意图。
图13为一个实施例中将已知分子总数的分子散落在阵列中的示意图。
图14为另一个实施例中将已知分子总数的分子散落在阵列中的示意图。
图15为一个实施例中绝对定量装置的结构框图。
图16为一个实施例中计算机设备的内部结构图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例中提供的绝对定量方法，可以应用于如图1所示的应用环境中。其中，终端102可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备。
本申请实施例中提供的绝对定量方法，还可以应用于如图2所示的应用环境中。其中，PCR(Polymerase Chain Reaction，多聚酶链式反应)检测终端202可以但不限于是PCR检测芯片、PCR多孔板、各类荧光显微镜光学传感器、普通PCR仪、梯度PCR仪、原位PCR仪、实时荧光定量PCR仪或数字PCR仪等。终端204可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备。
在一个实施例中，如图3所示，提供了一种绝对定量方法，以该方法应用于图1中的终端102或图2中的终端204为例进行说明，包括：
步骤302，获取多个第一预设分子数。
其中，第一预设分子数是指在终端中预存储的第一预设分子参数对应的多个数值。其中，第一预设分子数中最少包含0个、1个和2个，还可以包含3个、4个和5个，或者更多，等等不限于此。
具体地，终端获取多个第一预设分子数。例如第一预设分子数为k，则获取k＝0、k＝1、k＝2、…、k＝100等等但不限于此。
步骤304，根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理，得到包含多个第一预设分子数和分子总数变量的函数。
其中，第二预设分子数的数量不限，第二预设分子数中至少包含0，还可以包含1、2、3、4和5，或者更多，等等不限于此。每个第二预设分子数对应的分区数量至少包括第二预设分子数为0时对应的分区数量。则终端根据分区总数和0对应的分区数量，可得到1对应的分区数量。第二预设分子数的个数少于或等于第一预设分子数的个数。例如第一预设分子数为0、1、2、3、4和5，共6个，那么第二预设分子数可以为0和1，共2个。每个第二预设分子数对应的分区数量可以通过终端模拟或者从PCR(Polymerase Chain Reaction，多聚酶链式反应)检测终端中通过判定算法得到。
具体地，终端获取多个第二预设分子数中每个第二预设分子数对应的分区数量。例如，在阵列中 有256个分区，在256个分区中无分子对应的分区数量为255个，1个分子对应的分区数量为1，2个分子对应的分区数量为0，3个分子对应的分区数量为0等但不限于此。终端根据多个第一预设分子数，以及多个第二预设分子数中每个第二预设分子数对应的分区数量处理，得到包含多个第一预设分子数和分子总数变量的函数。
该包含分子总数变量的函数可用于表征多个第一预设分子数与每个第一预设分子数对应的分区数量的函数关系。例如终端将第一预设分子数作为x轴中的变量，第一预设分子数对应的分区数量作为y轴中的变量。
本实施例中，终端根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量，进行拟合或者插值处理得到包含分子总数变量的函数。其中，插值法又称内插法，是利用在某区间中已知的若干点的函数值作出适当的特定函数的方法。例如已知多个第二预设分子数中每个第二预设分子数对应的分区数量，则可再根据第一预设分子数作出包含第一预设分子数和分子总数变量的函数。插值可以是通过拉格朗日插值法、牛顿插值法、牛顿迭代法、区间二分法、弦截法、雅克比迭代法或牛顿科特斯数值积分法等方法，进行插值计算得到包含分子总数变量的函数。
本实施例中，终端还可以根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量，以及每个第一预设分子数对应的权重，得到包含分子总数变量的函数。
本实施例中，终端还可以根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量进行拟合或插值处理，得到包含多个第一预设分子数和分子总数变量的函数。
步骤306，根据包含多个第一预设分子数和分子总数变量的函数处理，得到分子总数变量的取值。
具体地，终端根据包含多个第一预设分子数和分子总数变量的函数求解得到分子总数变量的取值。
上述绝对定量方法中，通过获取多个预设分子数，并根据多个第一预设分子数、和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含第一预设分子数和分子总数变量的函数，根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值，能得到多个第一预设分子数和分子总数之间的关系，且能得到较多的分子数对应的信息，例如每个第一预设分子数对应的分区数量等，避免因仅用第二预设分子数为0和1时计算分子总数而导致的随机误差，使绝对定量结果更加准确，并且拓宽了动态范围。
在一个实施例中，如图4所示，根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数，包括：
步骤402，获取多个预设面积参数的值或多个预设体积参数的值。
其中，预设面积参数的值是指一个阵列中的每个分区对应的面积参数的值。多个预设面积参数的值是指多个阵列中的每个阵列中的每个分区对应的面积参数的值。预设体积参数的值是指一个阵列中的每个分区对应的体积参数的值。多个预设体积参数的值是指多个阵列中的每个阵列中的每个分区对应的体积参数的值。其中，预设面积参数的值和预设体积参数的值可呈等比排列，也可呈其他形式。
具体地，终端获取多个预设面积参数的值或者多个预设体积参数的值。例如，预设面积参数为A，第一阵列中的每个分区对应的面积参数的值为1，第二阵列中每个分区对应的面积参数的值为5,第三阵列中每个分区对应的面积参数的值为25，第四阵列中每个分区对应的面积参数的值为125。
步骤404，根据多个第一预设分子数，和多个预设面积参数的值或多个预设体积参数的值，得到每个预设面积参数的值或每个体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数。
其中，每个面积参数的值或每个体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数可用于表征每个第一预设分子数与对应的分区数量的函数关系。
具体地，终端根据多个第一预设分子数和多个预设面积参数的值，得到每个预设面积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数。或者，终端根据多个第一预设分子数和多个预设体积参数的值，得到每个预设体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数。
本实施例中，例如多重体积多元泊松分布模型函数，即期望值函数为 f(A,k,λ)＝E(C A,k)＝n×(A×m/n) k×e -A×m/n/k！＝n×(A×λ) k×e -A×λ/k！，其中k为第一预设分子数，m为分子总数，n为分区数，分区数为一个常量。λ为平均分子数，即分子总数和分区数的比值。f为待拟合模型函数，A为预设面积参数或预设体积参数，E(C A,k)为预设面积参数或预设体积参数为A中的第一预设分子数为k的分区数量C A,k的期望。下表1以k取0、1、2和3为例，得到每个预设面积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数。
表1
Figure PCTCN2019081772-appb-000001
步骤406，根据期望值函数，和每个预设面积参数或每个预设体积参数的每个第二预设分子数对应的分区数量，处理得到包含多个第一预设分子数和分子总数变量的拟合函数。
其中，拟合函数是根据每个第二预设分子数对应的分区数量对包含多个第一预设分子数和分子总数变量的函数进行拟合得到的函数。
具体地，终端根据每个预设面积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数，和每个预设面积参数的每个第二预设分子数对应的分区数量，得到包含多个第一预设分子数和分子总数变量的拟合函数。
终端根据每个预设体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数，和每个预设体积参数的每个第二预设分子数对应的分区数量，得到包含多个第一预设分子数和分子总数变量的拟合函数。该拟合函数可用于表征多个第一预设分子数与每个第一预设分子数对应的分区数量的函数关系。
本实施例中，例如第二预设分子数对应的分区数量如表2：
表2
Figure PCTCN2019081772-appb-000002
则根据表2的数据以及表1的期望值函数，可得到拟合函数的表达式SSE(f)＝Σ[f-y] 2，拟合函数的形式不限于此。拟合函数的表达式中仅包含m一个未知变量，y为第二预设分子数对应的分区 数量。那么该拟合函数中包括16个求和项，能得到包含多个第一预设分子数、预设面积参数和分子总数变量的拟合函数。
根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值，包括：
步骤408，根据包含多个第一预设分子数和分子总数变量的拟合函数处理，得到多个预设面积参数或多个预设体积参数对应的分子总数变量的取值。
其中，多个预设面积参数或多个预设体积参数对应的分子总数变量的取值即为多个阵列的一个分子总数变量的取值。其中每个阵列中的分区对应一个预设面积参数或者预设体积参数。
具体地，终端根据包含多个第一预设分子数和分子总数变量的拟合函数处理得到多个预设面积参数或多个预设体积参数对应的分子总数变量的取值。例如共有4个预设面积参数，每个预设面积参数对应一个阵列，阵列1、阵列2、阵列3和阵列4，那么终端根据拟合函数处理得到阵列1、阵列2、阵列3和阵列4中的分子总数的总和。
本实施例中，终端可通过牛顿迭代法等计算方法对包含多个第一预设分子数和分子总数变量的拟合函数进行计算，则当拟合函数SSE(f)＝Σ[f-y] 2的值达到最小值时，确定最小值对应的分子总数变量的取值。
上述绝对定量方法中，通过获取多个预设面积参数的值或者多个预设体积参数的值，并得到期望值函数，从而得到拟合函数，处理得到分子总数变量的取值，能得到不同面积或体积下的每个预设分子数对应的期望值函数，减少不同面积或体积对应的期望值函数和每个第二预设分子数对应的分区数之间的误差，并减少因面积或体积变化而引起的绝对定量误差，且无论是分子总数多少，即无论分子的浓度多少，都能对分子总数进行定量，提高了绝对定量的动态范围、绝对定量的准确性以及绝对定量的分辨率。
在一个实施例中，根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数，包括：
步骤(a1)，获取多个预设面积参数的值或多个预设体积参数的值。
步骤(a2)，根据多个第一预设分子数，和多个预设面积参数的值或多个预设体积参数的值，以及同一预设面积参数的值或同一预设体积参数的值对应的每个第二预设分子数对应的分区数量，处理得到每个预设面积参数的值或每个预设体积参数的值对应的包含第一预设分子数和分子总数变量的函数。
其中，每个预设面积参数的值或每个预设体积参数的值对应的包含第一预设分子数和分子总数变量的函数是指每一个预设面积参数的值或预设体积参数的值对应一个函数，即每一个预设面积参数的值对应一个包含第一预设分子数和分子总数变量的函数；每一个预设体积参数的值对应一个包含第一预设分子数和分子总数变量的函数。
具体地，终端根据多个第一预设分子数和多个预设面积参数的值，以及每个第二预设分子数对应的分区数量，处理得到每个预设面积参数的值对应的包含第一预设分子数和分子总数变量的拟合函数或者插值函数。或者，终端根据多个第一预设分子数和多个预设体积参数的值，以及每个第二预设分子数对应的分区数量，处理得到每个预设体积参数的值对应的包含第一预设分子数和分子总数变量的拟合函数或者插值函数。
本实施例中，如表1和表2，终端根据多个第一预设分子数k和多个预设面积参数的值或多个预设体积参数的值A，得到函数f(A,k,λ)＝n×(A×m/n) k×e -A×m/n/k！＝n×(A×λ) k×e -A×λ/k！。再根据每个第二预设分子数对应的分区数量y，处理得到拟合函数SSE(f)＝Σ[f-y] 2。例如当A＝1时，拟合函数为：
SSE(f)＝[(n×e -m/n-251) 2+(m×e -m/n-5) 2+(n×(m/n) 2×e -m/n/2-0) 2+(n×(m/n) 3×e -m/n/6-0) 2] 可依次得到A＝5、A＝25和A＝125时的拟合函数。
本实施例中，终端根据多个第一预设分子数和多个预设面积参数的值或多个预设体积参数的值，以及每个第二预设分子数对应的分区数量，进行插值计算，得到每个预设面积参数的值或每个预设体积参数的值对应的包含第一预设分子数和分子总数变量的插值函数。例如终端只获取到第二预设分子数为0、1、2和3对应的分区数量，进行插值计算，可以得到包含第一预设分子数为0、1、2、3、4、…、100等等和分子总量的插值函数f(k)。或者终端可根据任意第二预设分子数对应的分区数量中的3个数值点确定一个插值函数，然后评估第4个点拟合的不确定度等，以确定第4个点与该任意3个点之间的误差，那么可得到4个插值结果。终端从4个插值结果中选取偶然误差最小的结果作为第4个点的数据。
根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值，包括：
步骤(a3)，根据每个预设面积参数的值或每个预设体积参数的值对应的包含分子总数变量的函数处理，得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
具体地，终端根据每个预设面积参数的值对应的包含分子总数变量的拟合函数或者插值函数，处理得到多个预设面积参数的值对应的分子总数变量的取值。或者，终端根据每个预设体积参数的值对应的包含分子总数变量的拟合函数或者插值函数，处理得到多个预设体积参数的值对应的分子总数变量的取值。
本实施例中，终端可根据每个预设面积参数的值或每个预设体积的值对应的包含分子总数变量的拟合函数，处理得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。具体地，终端通过牛顿迭代法等方式对每个预设面积参数的值或每个预设体积参数的值对应的包含分子总数变量的拟合函数进行计算。当每个预设面积参数的值或每个预设体积参数的值对应的拟合函数达到最小值时，确定最小值对应的分子总数变量的取值。终端对每个预设面积参数的值或每个预设体积参数的值对应的分子总数变量的取值进行求和，得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
本实施例中，终端可根据每个预设面积参数的值或每个预设体积的值对应的包含分子总数变量的插值函数处理得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。具体地，设插值函数为f(k)，则有
Figure PCTCN2019081772-appb-000003
由于式子中仅有m一个未知数，则可得到每个预设面积参数或每个预设体积参数对应的分子总数变量的取值。终端对每个预设面积参数或每个预设体积参数对应的分子总数变量的取值进行求和，则得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
上述绝对定量方法中，通过获取多个预设面积参数的值或者多个预设体积参数的值，处理得到包含第一预设分子数和分子总数变量的函数，得到多个预设面积参数的值或者多个预设体积参数对应的分子总数变量的取值，能得到不同面积或不同体积下的分子总数变量的取值，减少因面积或体积变化而引起的绝对定量误差，并且无论是分子总数多少，即无论分子的浓度多少，都能对分子总数进行定量，提高了绝对定量的动态范围、绝对定量的分辨率以及绝对定量的准确性。
在一个实施例中，如图5所示，为一个实施例中预设面积参数为1、5、25和125的多重阵列对应的分子分布以及曲线示意图。其中，502是指面积＝256对应的曲线，504是指面积＝1280对应的曲线，506是指面积＝6400对应的曲线，508是指面积＝32000对应的曲线。如图6所示，为另一个实施例中预设面积参数为1、5、25和125的多重阵列对应的分子分布以及曲线示意图。其中，602是指面积＝256对应的曲线，604是指面积＝1280对应的曲线，606是指面积＝6400对应的曲线，608是指面积＝32000对应的曲线。其中，以四个阵列为例，每个阵列中有256个分区，即分区总数为256。面积＝256是当预设面积参数为1时的阵列面积，即阵列面积＝1×256＝256。面积＝1280是当预设面积参数为5时的阵列面积，即阵列面积＝5×256＝1280。面积＝6400是当预设面积参数为25时的阵列面积，即阵列面积＝25×256＝6400。面积＝32000是当预设面积参数为125时的阵列面积，即阵列面积＝125×256＝32000。 图5中A图已知的分子总数为1，B图分子总数为2，C图分子总数为5；D图分子总数为10，E图分子总数为20，F图分子总数为50；G图分子总数为100，H图分子总数为200，I图分子总数为500。图6中的J图分子总数为1000，K图分子总数为2000，L图分子总数为5000；M图分子总数为10000，N图分子总数为20000，O图分子总数为50000；P图分子总数为100000，Q图分子总数为200000，R图分子总数为500000。
终端采用空间点过程方法模拟核酸分子在溶液空间中的分布，采用等比例的预设面积参数，即1，5，25和125四个预设面积参数对应的阵列模拟不同分子数条件下的二维泊松过程。同时，记录阵列中分子数为0～15时对应的分区数量，并绘制每个预设面积参数对应的阵列的泊松二项分布随机变量理论期望值的连续曲线并与统计模拟实验结果进行对比。其中，统计模拟实验结果即为图5和图6中的柱形图，连续曲线是E(C A,k)＝n*[m(A)/n] k*e -m(A)/n/k！，它是分区数量的理论期望值。其中m(A)是每重面积阵列中的分子总数。图5中的A～I图曲线从左到右依次为面积＝256、面积＝1280、面积＝6400和面积＝32000对应的曲线。根据图5和图6中所示的模拟结果，可以发现，四重面积阵列中，方格分区面积越大，平均分子数λ也越大，不同面积阵列间λ的期望比与面积比相同。当分子总数较小时，例如1～500，小面积阵列中几乎无分子，其λ也较小，因此难以对低浓度分子进行分辨。但是如图5所示的方格阴影部分，仍有少量分子可以被大面积阵列所捕捉，提供浓度反馈。反之，如图6，当分子总数较大时，例如10000～500000大面积阵列中每个分区中的分子数，即k值远大于0，λ期望值也较大，无法有效体现泊松分布的分布特征，因此难以对高浓度分子进行分辨。如图6中的P、Q和R图，仅存在面积＝256和面积＝1280的曲线，而不存在面积＝6400和面积＝32000的曲线。但是在小面积阵列中仍然随机存在分子数接近于0的分区，因此图6中的P、Q和R图中均有面积＝256和面积＝1280的曲线，因此可对浓度进行反馈。故阵列面积或体积大小会对分子总数的计算造成较大的影响。
上述绝对定量方法中，通过模拟分子在溶液中的分布，以及利用算法得到分区数和预设分子数之间的函数关系，可得知当分子总数较小时，预设面积参数较小的阵列中几乎无分子，则无法对分子总数进行精确的计算，此时需要预设面积参数较大的阵列进行分子总数的计算；当分子总数较大时，预设面积参数较大的阵列中难以存在分子数为0的分区，则无法对分子总数进行计算，此时需要预设面积参数较小的阵列进行分子总数的计算；故阵列面积或体积大小对绝对定量的范围产生较大的影响。
在一个实施例中，有高度多样性与特异性的核酸分子作为生物标志物能对不同种类生物物种、个体乃至单个基因进行识别与检测。未来分子检测除了用于疾病预测及早期甚至超早期诊断外，还将用来指导常规体检，让常规体检更加有的放矢，从而改变健康管理观念。此外，它还可以用于特定人群、特殊物品及特定环境，例如特定地点及时间窗口中病原体的高灵敏定性或定量检测，用以来指导疾病管理，进而指导安全用药，帮助医生定制个性化治疗方案，以便更加有效地控制及治疗疾病或疫情。
目前针对传染病病原体核酸检测的qPCR(Real-time Quantitative PCR，实时荧光定量核酸PCR)或RT-qPCR(reverse transcription PCR，逆转录PCR)技术，是目前全球通用的传染病的诊断检测技术。qPCR技术虽然具有较好检测灵敏度和特异性，但是其检测性能依然严重受制于扩增酶的抑制剂耐受性，最低检测极限还达不到单分子检测的需求。并且一旦体系中产生非特异性扩增或检测物污染，那么整个检测结果将呈现假阳性，导致严重的诊断事故。此外，qPCR检测过程依赖于较精密的荧光监测设备，需实时反馈每轮循环——至少40轮后的荧光信号，检测耗时且硬件成本较高。因此，从目前的检测性能现状以及传染病病原分子诊断的未来发展方向的角度来看，qPCR技术并未达到“检得出，检得快，检得准”的目标。
自从1999年以来，为了更好地鉴定稀有癌症突变，有人提出了以单分子检测为目的的“数字PCR”概念以来，以dPCR为代表的数字核酸扩增方法已经在精准医疗、微生物检测、食品安全等多个领域获得广泛应用。数字核酸扩增方法采用绝对定量的手段，无需标准品作为参照，采用分子计数的方法实现核酸精确定量。另外，伴随着dPCR商品化产品的发展，在qPCR上应用的检测体系可以无缝地转移到dPCR仪器系统上，从而满足实验室对于检测结果的要求。由单分子核酸扩增技术发展而来的数字核酸扩增方法，通过将微升级的反应溶液进行纳升或皮升级的均一化分割，排除了数目可知并占据可观比例的空分区，同时增加了剩余有效反应分区中的局部核酸样本浓度。相较于宏观体积中的单分子扩 增反应，这些分区中的核酸分子与反应试剂的相对浓度比得到了大幅提高，因此分子间碰撞概率急剧上升从而使得引物、模板及聚合酶之间的相互作用变得更加快速、频繁、直接且稳定。在此种特殊反应条件下，扩增效率与检测灵敏度可以得到相应的潜在提升。除此之外，由于模板核酸分子被限定在有限的局部空间中，因此扩增反应受到外部环境带来的干扰因素，例如酶抑制剂或非特异性扩增模板等将会降到最低程度，扩增反应稳健性与特异性得到有效保证，从而提高了检测准确性。综合以上特点，相比于qPCR，数字核酸扩增方法能提供更加可靠的病原体载量测定结果。
传统的dPCR平台往往采用具有良好一致性的反应分区进行检测分析，而对于单一体积的dPCR，其定量检测的上限主要受到单个反应分区的体积及反应分区数量的影响，检测下限则取决待检样品的总体积。因此，为保证较宽的动态检测范围，往往需要数万到数百万个微小的反应分区，这就使得检测的精度和动态范围无法独立调节，提高了检测分析的成本与难度，也降低了实验设计的效率。因此，传统dPCR技术动态范围严重受限于反应分区的数量，并且也无法如qPCR一样将样品浓度转化为扩增结果的指数函数进行计算，因此对跨度大的未知浓度样品难以实现精准检测。目前主流dPCR平台的动态范围为5左右，远低于qPCR平台的9～10。
在一个实施例中，如图7、图8和图9所示，图7为一个实施例中预设面积参数为1、5、25和125的多重阵列分子分布以及算法结果验证示意图。其中，702是指面积＝256对应的曲线，704是指面积＝1280对应的曲线，706是指面积＝6400对应的曲线，708是指面积＝32000对应的曲线。图8为另一个实施例中预设面积参数为1、5、25和125的多重阵列分子分布以及算法结果验证示意图。其中，802是指面积＝256对应的曲线，804是指面积＝1280对应的曲线，806是指面积＝6400对应的曲线，808是指面积＝32000对应的曲线。图9为一个实施例中四种绝对定量方法的动态范围对比图。其中，input m表示已知的预设分子总数。SVEstSum M表示单重体积泊松估计求和计算结果。MVEst M表示多重体积泊松联合估计计算结果。SVFitSum M表示本申请中根据每个预设面积参数的值或每个预设体积参数的值对应的包含分子总数变量的函数处理得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值的计算结果。MVFit M表示通过本申请实施例中根据包含多个第一预设分子数和分子总数变量的拟合函数处理得到多个预设面积参数或多个预设体积参数对应的分子总数变量的取值的计算结果。
动态范围是指对待测对象有可测量响应的范围。在本实施例中，动态范围也即通过某种方式能测量出分子总数的数值范围。由图7和图8可知，当已知的分子总数大于或等于5000时，SVFitSum M＝Inf，其中Inf表示无穷大，故SVFitSum M对应的方法无法求出对应的分子总数。而当已知的分子总数为5000时，MVEst M＝4387.835，MVFit M＝5078.2875，其中MVFit M对应的方法误差更小。当已知的分子总数为500060时，MVEst M＝NaN，其中NaN表示计算结果为无效数据，即MVEst M对应的方式无法求出对应的分子总数。而MVFit M对应的方式仍旧可以进行绝对定量，此时MVFit M＝440922.3。且MVFit M在已知分子总数为5～500060中的计算结果误差均在一个较小的范围内。例如当已知的分子总数为50时，通过MVFit M的方式得出的分子总数为51.0047。当已知的分子总数为500时，通过MVFit M的方式得出的分子总数为524.9766。当已知的分子总数为5000时，通过MVFit M的方式得出的分子总数为5078.2875。当已知的分子总数为50000时，通过MVFit M的方式得出的分子总数为48371.7。对每组同分子数测试重复10次但不限于此，可得到不同算法结果的线性回归数据，根据数据结果，可以比较不同算法的动态范围及定量能力。如图9所示，SVEstSum M方法的动态范围上限为10 3～10 4，SVFitSum M方法的动态范围上限约为10 4，MVEst M方法的动态范围上限为10 4～10 5，MVFit M方法的动态范围上限约为10 6。相对于其他结果，MVFit M方法的动态范围至少可提高1.5个数量级。
上述绝对定量方法中，能摆脱分区总数对动态范围的限制，且相对于其他结果，本申请实施例中的方法的动态范围至少可提高1.5个数量级。
在一个实施例中，如图10所示，为一个实施例中单分散体积数字PCR的定量置信水平为95％的边界分布曲线示意图。横轴坐标是阴性分区的总占比，最高是1，即全部分区含有分子数为0；最低是0，即每个分区都最少含有1个分子。图10中的4种不同的线型表示的是不同分区数n＝120000，12000，1200，120对应的定量置信边界。由图可知随着n数量级的增加，该边界的上下距离越来越小，趋近于 中间的0线。然而在坐标横轴两端1和0处，会出现边界距离急速增大的情况。这说明单一体积阵列在分子总数特别低，即阴性占比接近1，以及特别高，即阴性占比接近0的两种极端情况下，数字绝对定量方法的精度会大幅下降。这种精度的降低也会带来定量分辨率的降低，要判定较为接近的两个不同分子数浓度较为困难，较容易产生两类判定错误。本申请实施例中获取多个预设面积参数的值或多个预设体积参数的值的方案则可以克服以上的技术缺陷。比如在某一面积阵列中阴性占比已经接近于1或0，但是在更大面积阵列中阴性占比不可能也是接近1，或者更小面积的阵列中阴性占比不可能也是接近0。
上述绝对定量方法中，能消除各单一阵列定量的边缘效应影响，提高绝对定量的分辨率与精准度。
在一个实施例中，在获取多个第一预设分子数之前，该绝对定量方法还包括：将分子散落在每个预设面积参数或每个预设体积参数对应的阵列中；获取每个阵列中的分区中所含的分子数；根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
其中，阵列是由分区构成的，一个阵列中可以有多个分区。阵列的数量不限，每个阵列对应一个预设面积参数或者一个预设体积参数。分子可以是模拟的分子，也可以是真正的核酸分子等不限于此。
具体地，终端获取至少一个阵列，在终端通过泊松分布或者空间点过程的方式将分子散落在每个预设面积参数对应的阵列中。或者，终端通过泊松分布或者空间点过程的方式将分子散落在每个预设体积参数对应的阵列中。终端获取每个阵列中的分区中所含的分子数量。也即终端获取至少一个阵列中的每个阵列中的分区中所含的分子数量。终端根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
例如阵列1的分区1的分子数量为0，阵列1的分区2的分子数量为1，阵列1的分区3的分子数量为1，阵列1的分区4的分子数量为2。则终端根据阵列1中的分区中所含的分子数得到预设分子数为0对应的分区数量为1个，预设分子数为1对应的分区数量为2个，预设分子数为2对应的分区数量为1个。
本实施例中，可将分子随机散落在PCR(Polymerase Chain Reaction，多聚酶链式反应)检测终端中每个预设面积参数或者每个预设体积参数对应的阵列中。PCR检测终端检测出每个阵列中的分区中所含的分子数。终端从PCR检测终端中获取每个阵列中的分区中所含的分子数，并根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
上述绝对定量方法中，通过将分子散落在每个预设面积参数对应的阵列中，并获取每个阵列中的分区中所含的分子数，统计每个阵列中预设分子数对应的分区数量，能得到不同面积或不同体积下的每个预设分子数对应的分区数量，并和每个预设分子数对应的期望值函数得到残差平方和，减少因面积或体积变化而引起的绝对定量误差，提高绝对定量的准确性。
在一个实施例中，在获取多个预设分子数之前，该绝对定量方法还包括：将分子散落在阵列中；获取阵列中的每个分区中所含的分子数；根据每个分区中所含的分子数统计多个第二预设分子数中每个第二预设分子数对应的分区数量。
其中，阵列的数量不限。一个阵列中可以包含多个分区。
具体地，终端将分子散落在一个阵列中，并获取阵列中每个分区中所含的分子数。终端根据每个分区中所含的分子数统计多个第二预设分子数中每个第二预设分子数对应的分区数量。
本实施例中，可将分子随机散落在PCR检测终端的阵列中。PCR检测终端检测出每个阵列中的分区中所含的分子数。终端从PCR检测终端中获取每个阵列中的分区中所含的分子数，并根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中预设分子数对应的分区数量。
上述绝对定量方法中，将分子散落在阵列中，获取每个分区所含的分子数，并统计多个第二预设分子数中每个第二预设分子数对应的分区数量，能得到分子数的多种分子信息，而不局限于有无分子数，从而提高绝对定量的准确性。
在一个实施例中，本申请实施例中充分利用数字核酸扩增单分子水平高灵敏度与高特异性，建立进一步细分阳性分区的模型与方法，将把以往单一的全或无终点判定时丢失的信息重新加以利用，将 反应终点的状态从两种变为三种、四种甚至更多，即第二预设分子数从0和1变为多个。细分阳性分区的方法可采用数字核酸扩增结合扩增曲线判定法来实现，例如1个分子与2个分子浓度相差100％，2个分子与3个分子浓度相差50％。细分阳性分区的具体原理及结果如图11所示。其中，如图11所示，为一个实施例中获取多个第二预设分子数中每个第二预设分子数对应的分区数量的示意图。其中大方格代表一个阵列，小方格代表一个分区。图11的第一个图为未知，即为分子在阵列中的随机分布，图中的黑点表示分子。图11中的第二个图为传统的检测方式，即仅仅能判断是否有分子在分区中，以白方格表示0，黑方格表示1。图11中的第三个图为本申请实施例中所采用的方式，通过检测方格即分区中的分子数统计可得到第二预设分子数对应的分区数量。
在一个实施例中，如图12所示，为一个实施例中通过本申请实施例中的绝对定量方法和泊松单点估计计算方法的结果示意图。图12为通过获取多个第一预设分子数；根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数；根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值的方案处理得到的拟合曲线。终端采用16×16的256位二维方形阵列，模拟了不同分子总数的二维泊松过程，即图12中所示的实际值的二维泊松过程。终端将已预知的总数的分子随机散布于该二维阵列中，同时记录阵列中每个方格所含的第二预设分子数，随后统计出此时阵列中第二预设分子数为0～3的方格总数，即分区数。终端分别根据单点泊松估计得到估计值以及根据本申请实施例中的绝对定量方法得到拟合值两种模式，对分子总数进行计算。数据点为第二预设分子数对应的分区数量的点。在图12中，第二预设分子数为0、1、2和3，第一预设分子数为0、1、2、3、4、5、6、7、8、9、10、11、12、13、14和15。如表3，列举了第一预设分子数与分区数的对应关系。
表3
Figure PCTCN2019081772-appb-000004
其中，单点泊松分布的公式为M＝-n*ln(i/n)。n为分区总数，i为第二预设分子数为0时对应的分区数量。对于实际值为1的情况，第二预设分子数为0时对应的分区数为255,第二预设分子数为1时对应的分区数量为1，所以泊松单点估计的计算公式，分子数估计值为：
M＝-n*ln(i/n)＝-256*ln(255/256)＝1.0019582262109001
对于实际值10的情况，0分区数为246，1分区数为10，所以泊松单点估计的计算公式，分子数估计值为：
M＝-n*ln(i/n)＝-256*ln(246/256)＝10.200552588083116
对于实际值60的情况，0分区数为202,1分区数为49，所以泊松单点估计的计算公式，分子数估计值为：
M＝-n*ln(i/n)＝-256*ln(202/256)＝60.648895252059575
其他情况依次类推。
而拟合值的计算方法则为本申请实施例中所示的方法。对于实际值为1的情况，第二预设分子数为0时对应的分区数量为255，第二预设分子数为1时对应的分区数量为1，第二预设分子数为2时对应的分区数量为0，第二预设分子数为3时对应的分区数量为0。而期望值函数E(C k)＝n*(m/n) k*e -m/n/k！，那么E(C 0)＝n*e -m/n，E(C 1)＝n*(m/n)*e -m/n，E(C 2)＝n*(m/n) 2*e -m/n/2，E(C 3)＝n*(m/n) 3*e -m/n/6，从而拟合函数SSE(f)＝Σ[E(C k)-y] 2＝(n*e -m/n-255) 2+[n*(m/n)*e -m/n-1] 2+[n*(m/n) 2*e -m/n/2-0] 2+[n*(m/n) 3*e -m/n/6-0] 2，该式中n为256，e为自然常数，于是式子仅含有分子总数变量m一个未知参数，因此可以通过牛顿迭代法计算出SSE(f)取得 最小极值时的取值，即分子总数的拟合值。其他情况依次类推。
由于该阵列中的分区数量较少，也由于第二预设分子数为0时对应的分区数量逐渐为0，因此单点估计所能定量的分子总数大约在1～1200之间，约为3个数量级。由图6可知，当分子数真实值为1615时，第二预设分子数为0对应的分区数量为0，计算结果突变到无穷大，造成较大的定量误差。与此对比，本申请实施例中的绝对定量方法，此时仍可通过已获取的1、2、3分子分别对应分区数量为2、8、23，对分子总数进行计算。终端得到的拟合结果为1575.175，计算误差为(1615-1575.175)/1615＝2.477％，仍然保证了较好的准确性，且定量范围超过单点估计结果。
如图13所示，为一个实施例将已知分子总数的分子散落在阵列中的示意图。如图14所示，为另一个实施例中将已知分子总数的分子散落在阵列中的示意图。其中图13和图14中的横坐标均表示第一预设分子数，纵坐标均表示分区数。由图14可知，当给定m≥1500时，其中m为分子总数，0分区的数下降到0，即256个方格全部包含有分子(至少1个)，此时采用单点估计的公式M＝-n*ln(i/n)，计算所得的定量结果为∞，因此单点估计的定量范围约为0～1300。一般情况下，从统计学上考虑，要达到至少1个0分区(n*P{X＝0}＝n*e -m/n≥1)，目标分子数应不低于n*ln(n)。这个数值一般就是定量动态范围的上限。而如上表所示，拟合方法的定量能力在给定m为1615时，依然可以计算出定量结果为1575.175，而此时估计值结果为∞，因此本申请实施例中采用的方法的定量动态范围超过了单点估计方法。
在一个实施例中，根据期望值函数和每个预设面积参数或每个预设体积参数的每个第二预设分子数对应的分区数量，处理得到包含多个第一预设分子数和分子总数变量的拟合函数，包括：
根据期望值函数和每个第二预设分子数对应的分区数量确定求和项；
根据求和项及对应的权重，得到包含权重以及多个第一预设分子数和分子总数变量的拟合函数，其中，权重与分子总数变量的不确定度呈负相关。
其中，该拟合函数可以是残差平方和函数，也可以是以其他拟合方式求得的拟合函数等不限于此。求和项是指拟合函数中的求和项。不确定度反映了拟合函数的估计结果的可靠性，可转化为百分比形式。当某个估计结果具有高不确定度，例如50％等，说明该拟合函数并不合理。
具体地，终端根据期望值函数和每个第二预设分子数对应的分区数量确定拟合函数中的求和项。例如SSE(f)＝Σ[f-y] 2中的求和项[f-y] 2，即在每个求和项前乘以对应的权重。终端根据求和项及对应的权重得到包含权重以及多个第一预设分子数和分子总数变量的拟合函数，其中，权重与分子总数变量的不确定度呈负相关。即，分子总数变量的不确定度越大，权重越小；分子总数变量的不确定越小，权重越大。
本实施例中，该绝对定量方法还包括：根据分子总数的取值计算得到不确定度的值；根据期望值函数和每个第二预设分子数对应的分区数量确定求和项；将不确定度的值作为求和项对应的权重；根据包含权重以及多个第一预设分子数和分子总数变量的拟合函数处理得到分子总数变量的取值。
上述绝对定量方法中，通过根据期望值函数和每个第二预设分子数对应的分区数量确定求和项，再根据求和项和对应的权重得到拟合函数，能减少随机误差对分子总数变量的值带来的影响，提高绝对定量的准确性。
在一个实施例中，该绝对定量方法还包括：
步骤(b1)，根据拟合函数得到拟合优度。
其中，拟合优度可用于反映某个统计模型刻画该数据集的准确度。
具体地，终端根据拟合函数得到拟合优度。
本实施例中，拟合优度可由下式计算：
Figure PCTCN2019081772-appb-000005
其中，λ为平均分子数，即分子总数m和分区总数n之比；y为第二预设分子数对应的分区数量， i为第二预设分子数对应的分区数量的标号，f为期望值函数，N为预设分子数的个数。
步骤(b2)，获取加权矩阵和平均分子数对应的雅可比矩阵，其中，平均分子数变量是根据分子总数变量和分区总数之比得到的。
其中，加权矩阵是指原模型进行加权，使之成为一个新的不存在异方差性的模型的矩阵。雅可比矩阵是指是一阶偏导数以一定方式排列成的矩阵。分区总数是指阵列中总的分区数量。当存在多个阵列时，则为多个阵列中的分区数量的总和。
具体地，终端获取预设的加权矩阵，以及根据平均分子数获取的雅可比矩阵。其中，平均分子数是根据分子总数和分区总数之比得到的。
本实施例中，例如加权矩阵为下式：
Figure PCTCN2019081772-appb-000006
平均分子数对应的雅可比矩阵为
Figure PCTCN2019081772-appb-000007
其中，λ为平均分子数。
步骤(b3)，根据雅可比矩阵和加权矩阵得到估计方差。
具体地，终端根据雅可比矩阵和加权矩阵计算得到估计方差。
本实施例中，平均分子数λ的不确定度C可由以下矩阵计算得到
C＝(J TWJ) -1
其中，J为雅可比矩阵，W为加权矩阵。
那么可得
Figure PCTCN2019081772-appb-000008
那么不确定度为
Figure PCTCN2019081772-appb-000009
此时不确定度C矩阵的对角元素c 11即为平均分子数λ的估计方差：
Figure PCTCN2019081772-appb-000010
步骤(b4)，根据拟合优度和估计方差得到平均分子数变量对应的不确定度。
具体地，终端根据拟合优度和估计方差计算得到平均分子数变量对应的不确定度。
本实施例中，得到平均分子数λ的不确定度
Figure PCTCN2019081772-appb-000011
如下式：
Figure PCTCN2019081772-appb-000012
其中只有一个未知数λ。
步骤(b5)，根据平均分子数变量对应的不确定度确定求和项对应的权重。
具体地，终端根据平均分子数变量对应的不确定度确定求和项对应的权重。
本实施例中，终端可求取平均分子数变量的取值后，计算出平均分子数变量的取值对应的不确定度的数值，将不确定度的数值处理后得到求和项对应的权重。其中，权重与不确定度的数值呈负相关。
在一个实施例中，该绝对定量方法还包括：根据不确定度得到置信区间。其中，置信区间是指待拟合参数的估计区间，例如分子总数变量的估计区间。置信区间展现的是这个参数的真实值有一定概率落在测量结果的周围的程度，其给出的是被测量参数的测量值的可信程度。具体地，终端可得到一个关于平均分子数λ的置信水平为1-α的t分布置信区间为：
Figure PCTCN2019081772-appb-000013
即，
Figure PCTCN2019081772-appb-000014
其中，t df,α/2为t分布双侧分位数，对于95％置信水平，有t 15,0.025＝2.131(α＝0.05)。自由度为15的t 15,0.025的值可由概率论相关书籍得出，或者MATLAB(矩阵实验室)中的t分布函数也可以直接得到。具体函数为tinv(1-alpha/2,15)，alpha＝0.05。
上述绝对定量方法中，根据拟合函数得到拟合优度，获取加权矩阵和雅可比矩阵，并得到估计方差，从而确定平均分子数变量的不确定度，根据不确定度确定权重，能根据拟合优度和不确定度等评判拟合结果的可靠性，为拟合函数增加权重，能减少随机误差对分子总数变量的取值带来的影响，提高绝对定量的准确性。
在一个实施例中，根据期望值函数和每个预设面积参数或每个预设体积参数的每个第二预设分子数对应的分区数量，处理得到包含多个第一预设分子数和分子总数变量的拟合函数，还包括：
步骤(c1)，根据期望值函数和每个第二预设分子数对应的分区数量确定求和项。
步骤(c2)，根据多个预设分子数得到每个第一预设分子数对应的方差函数。
具体地，终端根据多个预设分子数得到每个预设面积参数的值或每个预设体积参数的值对应的每个预设分子数对应的方差函数。当预设面积参数的值或者预设体积参数的值发生变化时，方差函数相应发生变化。
本实施例中，例如当第一预设分子数k＝0时，方差为
D(C A,0)＝n×e -A×m/n×(1-e -A×m/n)；
当第一预设分子数k＝1时，方差D(C A,1)＝A×m×e -A×λ×(1-A×m/n×e -A×m/n)；
当第一预设分子数k＝2时，方差为
D(C A,2)＝n×(A×m/n) 2×e -A×m/n×[2-(A×m/n) 2×e -A×m/n]/4；
当第一预设分子数k＝3时，方差为
D(C A,2)＝n×(A×m/n) 2×e -A×m/n×[2-(A×m/n) 2×e -A×m/n]/4,等等但不限于此。
步骤(c3)，将方差函数处理得到每个第一预设分子数对应的权重。
具体地，终端将方差函数的倒数作为每个第一预设分子数对应的权重。或者，终端将方差函数乘以一个常数等但不限于此。
步骤(c4)，根据求和项及对应的权重得到包含权重的拟合函数。
具体地，终端根据求和项及对应的权重得到包含权重以及多个第一预设分子数和分子总数变量的拟合函数。例如SSE(f)＝Σ[f-y] 2中的求和项[f-y] 2，即在每个求和项[f-y] 2前乘以对应的权重。
上述绝对定量方法中，根据期望值函数和第二预设分子数对应的分区数量确定求和项，在得到方差函数后得到对应权重，将权重赋予求和项，能减少拟合函数和第二预设分子数对应的分区数量之间的误差，从而提高绝对定量的准确性。
在一个实施例中，该绝对定量方法，还包括：针对同一份检测对象，检测预设次数，得到预设次数个分子总数变量的取值；确定预设次数个取值的平均值；将平均值作为同一份检测对象的分子总数。
其中，同一份检测对象是指同一份检测样本。例如同一份在PCR检测终端中检测的样品，或者同一份在终端中模拟的空间点过程。预设次数可以为1次、2次、…、100次等不限于此。
具体地，由于分子进行无规则运动，所以在每次检测过程中，第二预设分子数对应的分区数量有一些变化。终端针对同一份检测对象，检测预设次数，每次都得到分子总数变量的取值。终端确定预设次数个取值的平均值，将该平均值作为同一份检测对象的分子总数。
本实施例中，当某一预设次数对应的分子总数变量的取值与其他分子总数变量的取值之间的数值差距大于预设值时，终端可剔除该预设次数对应的分子总数变量的取值。
上述绝对定量方法中，通过对同一份检测对象检测预设次数，得到预设次数个分子总数变量的取值，确定平均值，将平均值作为同一份检测对象的分子总数，能减少分子运动导致的随机误差，提高绝对定量的准确性。
在一个实施例中，一种绝对定量方法，包括以下步骤：
步骤(d1)，将分子散落在阵列中。
步骤(d2)，获取阵列中的每个分区中所含的分子数。
步骤(d3)，根据每个分区中所含的分子数统计多个第二预设分子数中每个第二预设分子数对应的分区数量。
步骤(d4)，获取多个第一预设分子数。
步骤(d5)，根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数。
步骤(d6)，根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。
步骤(d7)，针对同一份检测对象，检测预设次数，得到预设次数个分子总数变量的取值。
步骤(d8)，确定预设次数个取值的平均值。
步骤(d9)，将平均值作为同一份检测对象的分子总数。
上述绝对定量方法中，通过获取多个预设分子数，并根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含第一预设分子数和分子总数变量的函数，根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值，能得到多个第一预设分子数和分子总数之间的关系，且能得到较多的分子数对应的信息，例如分区数量等，避免因仅用0和1计算分子数而导致的随机误差，使绝对定量结果更加准确。
在一个实施例中，一种绝对定量方法，包括以下步骤：
步骤(e1)，将分子散落在每个预设面积参数或每个预设体积参数对应的阵列中。
步骤(e2)，获取每个阵列中的分区中所含的分子数。
步骤(e3)，根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
步骤(e4)，获取多个第一预设分子数。
步骤(e5)，获取多个预设面积参数的值或多个预设体积参数的值。
步骤(e6)，根据多个第一预设分子数和多个预设面积参数的值或多个预设体积参数的值，得到每个预设面积参数的值或每个体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数。
步骤(e7)，根据期望值函数和每个第二预设分子数对应的分区数量确定求和项；
步骤(e8)，根据多个预设分子数得到每个第一预设分子数对应的方差函数；
步骤(e9)，将方差函数处理得到每个第一预设分子数对应的权重；
步骤(e10)，根据求和项及对应的权重得到包含权重的拟合函数。
步骤(e11)，根据包含多个第一预设分子数和分子总数变量的拟合函数处理得到多个预设面积参数或多个预设体积参数对应的分子总数变量的取值。
步骤(e12)，针对同一份检测对象，检测预设次数，得到预设次数个分子总数变量的取值。
步骤(e13)，确定预设次数个取值的平均值。
步骤(e14)，将平均值作为同一份检测对象的分子总数。
上述绝对定量方法中，通过获取多个预设面积参数的值或者多个预设体积参数的值，处理得到包含第一预设分子数和分子总数变量的函数，得到多个预设面积参数的值或者多个预设体积参数对应的分子总数变量的取值，能得到不同面积或不同体积下的分子总数变量的取值，减少因面积或体积变化而引起的绝对定量误差，并且无论是分子总数多少，即无论分子的浓度多少，都能对分子总数进行定量，提高了绝对定量的动态范围、绝对定量的分辨率以及绝对定量的准确性。
在一个实施例中，一种绝对定量方法，包括以下步骤：
步骤(f1)，将分子散落在每个预设面积参数或每个预设体积参数对应的阵列中。
步骤(f2)，获取每个阵列中的分区中所含的分子数。
步骤(f3)，根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
步骤(f4)，获取多个第一预设分子数。
步骤(f5)，获取多个预设面积参数的值或多个预设体积参数的值。
步骤(f6)，根据多个第一预设分子数和多个预设面积参数的值或多个预设体积参数的值，以及同一预设面积参数的值或同一预设体积参数的值对应的每个第二预设分子数对应的分区数量，处理得到每个预设面积参数的值或每个预设体积参数的值对应的包含第一预设分子数和分子总数变量的函数。
步骤(f7)，根据每个预设面积参数的值或每个预设体积参数的值对应的包含分子总数变量的函数处理得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
步骤(f8)，针对同一份检测对象，检测预设次数，得到预设次数个分子总数变量的取值。
步骤(f9)，确定预设次数个取值的平均值。
步骤(f10)，将平均值作为同一份检测对象的分子总数。
上述绝对定量方法中，通过获取多个预设面积参数的值或者多个预设体积参数的值，处理得到包含第一预设分子数和分子总数变量的函数，得到多个预设面积参数的值或者多个预设体积参数对应的分子总数变量的取值，能得到不同面积或不同体积下的分子总数变量的取值，减少因面积或体积变化而引起的绝对定量误差，并且无论是分子总数多少，即无论分子的浓度多少，都能对分子总数进行定量，提高了绝对定量的动态范围、绝对定量的分辨率以及绝对定量的准确性。
应该理解的是，虽然图3-4的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图3-4中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在一个实施例中，如图15所示，提供了一种绝对定量装置，包括：获取模块1502、函数求取模块1504和分子总数确定模块1506，其中：
获取模块1502，用于获取多个第一预设分子数。
函数求取模块1504，用于根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含多个第一预设分子数和分子总数变量的函数。
分子总数确定模块1506，用于根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值。
上述绝对定量装置中，通过获取多个预设分子数，并根据多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含第一预设分子数和分子总数变量的函数，根据包含多个第一预设分子数和分子总数变量的函数处理得到分子总数变量的取值，能得到多个第一预设分子数和分子总数之间的关系，且能得到较多的分子数对应的信息，例如分区数量等，避免因仅用0和1计算分子数而导致的随机误差，使绝对定量结果更加准确。
在一个实施例中，获取模块1502用于获取多个预设面积参数的值或多个预设体积参数的值。函数求取模块1504用于根据多个第一预设分子数和多个预设面积参数的值或多个预设体积参数的值，得到每个预设面积参数的值或每个体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数；根据期望值函数和每个预设面积参数或每个预设体积参数的每个第二预设分子数对应的分区数量，处理得到包含多个第一预设分子数和分子总数变量的拟合函数。分子总数确定模块1506用于根据包含多个第一预设分子数和分子总数变量的拟合函数处理得到多个预设面积参数或多个预设体积参数对应的分子总数变量的取值。
上述绝对定量装置中，通过获取多个预设面积参数的值或者多个预设体积参数的值，并得到期望值函数，从而得到拟合函数，处理得到分子总数变量的取值，能得到不同面积或体积下的每个预设分子数对应的期望值函数，减少不同面积或体积对应的期望值函数和每个第二预设分子数对应的分区数之间的误差，并减少因面积或体积变化而引起的绝对定量误差，且无论是分子总数多少，即无论分子的浓度多少，都能对分子总数进行定量，提高了绝对定量的动态范围、绝对定量的分辨率以及绝对定量的准确性。
在一个实施例中，获取模块1502用于获取多个预设面积参数的值或多个预设体积参数的值。函数求取模块1504用于根据多个第一预设分子数和多个预设面积参数的值或多个预设体积参数的值，以及同一预设面积参数的值或同一预设体积参数的值对应的每个第二预设分子数对应的分区数量，处理得到每个预设面积参数的值或每个预设体积参数的值对应的包含第一预设分子数和分子总数变量的函数。分子总数确定模块1506用于根据每个预设面积参数的值或每个预设体积参数的值对应的包含分子总数变量的函数处理得到多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
上述绝对定量装置中，通过获取多个预设面积参数的值或者多个预设体积参数的值，处理得到包 含第一预设分子数和分子总数变量的函数，得到多个预设面积参数的值或者多个预设体积参数对应的分子总数变量的取值，能得到不同面积或不同体积下的分子总数变量的取值，减少因面积或体积变化而引起的绝对定量误差，并且无论是分子总数多少，即无论分子的浓度多少，都能对分子总数进行定量，提高了绝对定量的动态范围、绝对定量的分辨率以及绝对定量的准确性。
在一个实施例中，该绝对定量装置还包括统计模块。获取模块1502用于将分子散落在每个预设面积参数或每个预设体积参数对应的阵列中；获取每个阵列中的分区中所含的分子数。统计模块用于根据每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
上述绝对定量装置中，通过将分子散落在每个预设面积参数对应的阵列中，并获取每个阵列中的分区中所含的分子数，统计每个阵列中预设分子数对应的分区数量，能得到不同面积或不同体积下的每个预设分子数对应的分区数量，并和每个预设分子数对应的期望值函数得到残差平方和，减少因面积或体积变化而引起的绝对定量误差，提高绝对定量的准确性。
在一个实施例中，该绝对定量装置还包括统计模块。获取模块1502用于将分子散落在阵列中；获取阵列中的每个分区中所含的分子数。统计模块用于根据每个分区中所含的分子数统计多个第二预设分子数中每个第二预设分子数对应的分区数量。
上述绝对定量装置中，将分子散落在阵列中，获取每个分区所含的分子数，并统计多个第二预设分子数中每个第二预设分子数对应的分区数量，能得到分子数的多种分子信息，而不局限于有无分子数，从而提高绝对定量的准确性。
在一个实施例中，函数求取模块1504还用于根据期望值函数和每个第二预设分子数对应的分区数量确定求和项；根据求和项及对应的权重得到包含权重以及多个第一预设分子数和分子总数变量的拟合函数，其中，权重与分子总数变量的不确定度呈负相关。
上述绝对定量装置中，通过根据期望值函数和每个第二预设分子数对应的分区数量确定求和项，再根据求和项和对应的权重得到拟合函数，能减少随机误差对分子总数变量的值带来的影响，提高绝对定量的准确性。
在一个实施例中，函数求取模块1504还用于根据拟合函数得到拟合优度；获取加权矩阵和平均分子数对应的雅可比矩阵，其中，平均分子数变量是根据分子总数变量和分区总数之比得到的；根据雅可比矩阵和加权矩阵得到估计方差；根据拟合优度和估计方差得到平均分子数变量对应的不确定度。
上述绝对定量装置中，根据拟合函数得到拟合优度，获取加权矩阵和雅可比矩阵，并得到估计方差，从而确定平均分子数变量的不确定度，根据不确定度确定权重，能根据拟合优度和不确定度等评判拟合结果的可靠性，为拟合函数增加权重，能减少随机误差对分子总数变量的取值带来的影响，提高绝对定量的准确性。
在一个实施例中，函数求取模块1504还用于根据期望值函数和每个第二预设分子数对应的分区数量确定求和项；根据多个预设分子数得到每个第一预设分子数对应的方差函数；将方差函数处理得到每个第一预设分子数对应的权重；根据求和项及对应的权重得到包含权重的拟合函数。
上述绝对定量装置中，根据期望值函数和第二预设分子数对应的分区数量确定求和项，在得到方差函数后得到对应权重，将权重赋予求和项，能减少拟合函数和第二预设分子数对应的分区数量之间的误差，从而提高绝对定量的准确性。
在一个实施例中，该绝对定量装置还包括重复检测模块。该重复检测模块用于针对同一份检测对象，检测预设次数，得到预设次数个分子总数变量的取值；确定预设次数个取值的平均值；将平均值作为同一份检测对象的分子总数。
上述绝对定量装置中，通过对同一份检测对象检测预设次数，得到预设次数个分子总数变量的取值，确定平均值，将平均值作为同一份检测对象的分子总数，能减少分子运动导致的随机误差，提高绝对定量的准确性。
关于绝对定量装置的具体限定可以参见上文中对于绝对定量装置的限定，在此不再赘述。上述绝对定量装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内 嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的步骤。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是终端，其内部结构图可以如图16所示。该计算机设备包括通过系统总线连接的处理器、存储器、网络接口、显示屏和输入装置。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有步骤系统和计算机程序。该内存储器为非易失性存储介质中的步骤系统和计算机程序的运行提供环境。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现一种绝对定量方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图16中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种计算机设备，包括存储器和处理器，存储器中存储有计算机程序，该处理器执行计算机程序时实现上述各个方法实施例中的步骤。
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现上述各个方法实施例中的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限，RAM以多种形式可得，诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)、DRAM(SLDRAM)、存储器总线(Rambus)、直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种绝对定量方法，所述方法包括：
    获取多个第一预设分子数；
    根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理，得到包含所述多个第一预设分子数和分子总数变量的函数；及
    根据所述包含所述多个第一预设分子数和分子总数变量的函数处理，得到所述分子总数变量的取值。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数，包括：
    获取多个预设面积参数的值或多个预设体积参数的值；
    根据所述多个第一预设分子数，和多个所述预设面积参数的值或多个预设体积参数的值，得到每个预设面积参数的值或每个体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数；
    根据所述期望值函数，和所述每个预设面积参数或每个预设体积参数的所述每个第二预设分子数对应的分区数量，处理得到包含所述多个第一预设分子数和所述分子总数变量的拟合函数；
    所述根据所述包含多个第一预设分子数和分子总数变量的函数处理得到所述分子总数变量的取值，包括:
    根据所述包含所述多个第一预设分子数和所述分子总数变量的拟合函数处理，得到所述多个预设面积参数或多个预设体积参数对应的分子总数变量的取值。
  3. 根据权利要求1所述的方法，其特征在于，所述根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数，包括：
    获取多个预设面积参数的值或多个预设体积参数的值；
    根据所述多个第一预设分子数，和所述多个预设面积参数的值或所述多个预设体积参数的值，以及同一预设面积参数的值或同一预设体积参数的值对应的每个第二预设分子数对应的分区数量，处理得到每个预设面积参数的值或每个预设体积参数的值对应的包含所述第一预设分子数和所述分子总数变量的函数；
    所述根据所述包含所述多个第一预设分子数和分子总数变量的函数处理得到所述分子总数变量的取值，包括：
    根据所述每个预设面积参数的值或所述每个预设体积参数的值对应的包含分子总数变量的函数处理，得到所述多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
  4. 根据权利要求2所述的方法，其特征在于，在所述获取多个第一预设分子数之前，还包括：
    将分子散落在每个预设面积参数或每个预设体积参数对应的阵列中；
    获取每个阵列中的分区中所含的分子数；
    根据所述每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
  5. 根据权利要求1所述的方法，其特征在于，在所述获取多个第一预设分子数之前，还包括：
    将分子散落在阵列中；
    获取所述阵列中的每个分区中所含的分子数；
    根据所述每个分区中所含的分子数统计多个第二预设分子数中每个第二预设分子数对应的分区数量。
  6. 根据权利要求2所述的方法，其特征在于，所述根据所述期望值函数和所述每个预设面积参数或每个预设体积参数的所述每个第二预设分子数对应的分区数量，处理得到包含所述多个第一预设分子数和所述分子总数变量的拟合函数，包括：
    根据所述期望值函数和所述每个第二预设分子数对应的分区数量确定求和项；
    根据所述求和项及对应的权重，得到包含所述权重以及多个第一预设分子数和所述分子总数变量的拟合函数，其中，所述权重与分子总数变量的不确定度呈负相关。
  7. 根据权利要求6所述的方法，其特征在于，所述方法还包括：
    根据所述拟合函数得到拟合优度；
    获取加权矩阵和平均分子数变量对应的雅可比矩阵，其中，所述平均分子数变量是根据所述分子总数变量和分区总数之比得到的；
    根据所述雅可比矩阵和所述加权矩阵得到估计方差；
    根据所述拟合优度和所述估计方差得到所述平均分子数变量对应的不确定度；
    根据所述平均分子数变量对应的不确定度确定所述求和项对应的权重。
  8. 根据权利要求2所述的方法，其特征在于，所述根据所述期望值函数和所述每个预设面积参数或每个预设体积参数的所述每个第二预设分子数对应的分区数量，处理得到包含所述多个第一预设分子数和所述分子总数变量的拟合函数，还包括：
    根据所述期望值函数和所述每个第二预设分子数对应的分区数量确定求和项；
    根据所述多个预设分子数得到每个第一预设分子数对应的方差函数；
    将所述方差函数处理得到每个第一预设分子数对应的权重；
    根据所述求和项及对应的权重得到包含所述权重的拟合函数。
  9. 根据权利要求1至8任意一项所述的方法，其特征在于，所述方法还包括：
    针对同一份检测对象，检测预设次数，得到所述预设次数个分子总数变量的取值；
    确定所述预设次数个取值的平均值；
    将所述平均值作为所述同一份检测对象的分子总数。
  10. 根据权利要求1所述的方法，其特征在于，所述根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数，包括：
    根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数中每个第二预设分子数对应的分区数量，进行拟合或者插值处理得到包含分子总数变量的函数。
  11. 根据权利要求1所述的方法，其特征在于，所述包含所述多个第一预设分子数和分子总数变量的函数用于表征多个第一预设分子数与每个第一预设分子数对应的分区数量的函数关系。
  12. 一种绝对定量装置，其特征在于，包括：
    获取模块，用于获取多个第一预设分子数；
    函数求取模块，用于根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数；
    分子总数确定模块，用于根据所述包含所述多个第一预设分子数和分子总数变量的函数处理得到所述分子总数变量的取值。
  13. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行如下步骤：
    获取多个第一预设分子数；
    根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数；
    根据所述包含所述多个第一预设分子数和分子总数变量的函数处理得到所述分子总数变量的取值。
  14. 根据权利要求13所述的计算机设备，其特征在于，所述处理器执行根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数，还执行以下步骤：
    获取多个预设面积参数的值或多个预设体积参数的值；
    根据所述多个第一预设分子数和多个所述预设面积参数的值或多个预设体积参数的值，得到每个预设面积参数的值或每个体积参数的值的每个第一预设分子数对应的包含分子总数变量的期望值函数；
    根据所述期望值函数和所述每个预设面积参数或每个预设体积参数的所述每个第二预设分子数对应的分区数量，处理得到包含所述多个第一预设分子数和所述分子总数变量的拟合函数；
    所述处理器执行所述根据所述包含多个第一预设分子数和分子总数变量的函数处理得到所述分子总数变量的取值，还执行以下步骤：
    根据所述包含所述多个第一预设分子数和所述分子总数变量的拟合函数处理得到所述多个预设面积参数或多个预设体积参数对应的分子总数变量的取值。
  15. 根据权利要求13所述的计算机设备，其特征在于，所述处理器执行所述根据所述多个第一预设分子数和多个第二预设分子数中每个第二预设分子数对应的分区数量处理得到包含所述多个第一预设分子数和分子总数变量的函数，还执行以下步骤：
    获取多个预设面积参数的值或多个预设体积参数的值；
    根据所述多个第一预设分子数和所述多个预设面积参数的值或所述多个预设体积参数的值，以及同一预设面积参数的值或同一预设体积参数的值对应的每个第二预设分子数对应的分区数量，处理得到每个预设面积参数的值或每个预设体积参数的值对应的包含所述第一预设分子数和所述分子总数变量的函数；
    所述处理器执行根据所述包含所述多个第一预设分子数和分子总数变量的函数处理得到所述分子总数变量的取值，还执行以下步骤：
    根据所述每个预设面积参数的值或所述每个预设体积参数的值对应的包含分子总数变量的函数处理得到所述多个预设面积参数的值或多个预设体积参数的值对应的分子总数变量的取值。
  16. 根据权利要求14所述的计算机设备，其特征在于，所述处理器执行获取多个第一预设分子数之前，还执行以下步骤：
    将分子散落在每个预设面积参数或每个预设体积参数对应的阵列中；
    获取每个阵列中的分区中所含的分子数；
    根据所述每个阵列中的分区中所含的分子数统计每个阵列中的多个第二预设分子数中每个第二预设分子数对应的分区数量。
  17. 根据权利要求13所述的计算机设备，其特征在于，所述处理器执行获取多个第一预设分子数之前，还执行以下步骤：
    将分子散落在阵列中；
    获取所述阵列中的每个分区中所含的分子数；
    根据所述每个分区中所含的分子数统计多个第二预设分子数中每个第二预设分子数对应的分区数量。
  18. 根据权利要求14所述的计算机设备，其特征在于，所述处理器执行所述根据所述期望值函数和所述每个预设面积参数或每个预设体积参数的所述每个第二预设分子数对应的分区数量，处理得到包含所述多个第一预设分子数和所述分子总数变量的拟合函数，还执行以下步骤：
    根据所述期望值函数和所述每个第二预设分子数对应的分区数量确定求和项；
    根据所述求和项及对应的权重得到包含所述权重以及多个第一预设分子数和所述分子总数变量的拟合函数，其中，所述权重与分子总数变量的不确定度呈负相关。
  19. 根据权利要求13-18所述的计算机设备，其特征在于，所述处理器还执行以下步骤：
    针对同一份检测对象，检测预设次数，得到所述预设次数个分子总数变量的取值；
    确定所述预设次数个取值的平均值；
    将所述平均值作为所述同一份检测对象的分子总数。
  20. 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，所述计算机程序被处理器执行时实现权利要求1至11中任一项所述的方法的步骤。
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