CN114262733A - Micro-drop digital PCR (polymerase chain reaction) fluorescent signal processing method - Google Patents
Micro-drop digital PCR (polymerase chain reaction) fluorescent signal processing method Download PDFInfo
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Abstract
The invention discloses a method for processing a droplet type digital PCR fluorescent signal, which comprises the following steps: filtering the original fluorescence signal to obtain a first fluorescence signal; identifying a droplet parameter based on the first fluorescent signal; filtering the droplets based on the droplet parameters and preset droplet filtration conditions; clustering the filtered droplets to obtain a first threshold; counting the number of the negative droplets and the number of the positive droplets based on the first threshold value, and calculating the concentration of the analyte. The method for processing the micro-drop digital PCR fluorescent signal can accurately extract the micro-drop signal from the fluorescent signal and accurately distinguish the negative micro-drop from the positive micro-drop, thereby realizing the accurate calculation of the concentration of the sample.
Description
Technical Field
The invention relates to a PCR analysis technology, in particular to a micro-drop digital PCR fluorescent signal processing method.
Background
Polymerase Chain Reaction (PCR) is an in vitro nucleic acid amplification technology developed in the middle of the 80 s, and has the characteristics of strong specificity, high sensitivity, simple and convenient operation, time saving and the like. It can be used for basic research of gene separation, cloning and nucleic acid sequence analysis. The digital PCR is a new generation quantitative PCR analysis technology which develops rapidly in recent years, and a micro-fluidic technology is utilized to disperse a large amount of diluted nucleic acid solution into micro-reactors of a chip, wherein the number of nucleic acid templates in each micro-reactor is less than or equal to 1. Thus, after PCR cycling, a reactor with at least one nucleic acid molecule template will give a fluorescent signal, and a reactor without a template will have no fluorescent signal. Based on the relative proportions and the volume of the reactor, the nucleic acid concentration of the original solution can be deduced. Compared with the traditional fluorescent quantitative PCR, the method has the advantages of high sensitivity, high specificity, no need of a standard quantitative curve and the like. The droplet microfluidic technology is applied to the field of biochemical analysis, cells or molecules of interest are often labeled with fluorescence, and the concentration of an object to be detected in a sample is detected by identifying droplets with fluorescence. It is therefore important to accurately extract the droplet signal from the fluorescence signal and accurately distinguish between negative and positive droplets.
Disclosure of Invention
The invention provides a micro-drop digital PCR (polymerase chain reaction) fluorescence signal processing method, which can accurately extract a micro-drop signal from a fluorescence signal and accurately distinguish a negative micro-drop from a positive micro-drop, thereby realizing accurate calculation of sample concentration.
The technical scheme adopted by the invention for overcoming the technical problems is as follows: the invention provides a method for processing a droplet type digital PCR (polymerase chain reaction) fluorescent signal, which comprises the steps of filtering an original fluorescent signal to obtain a first fluorescent signal; identifying a droplet parameter based on the first fluorescent signal; filtering the droplets based on the droplet parameters and preset droplet filtration conditions; clustering the filtered droplets to obtain a first threshold; counting the number of the negative droplets and the number of the positive droplets based on the first threshold value, and calculating the concentration of the analyte.
Further, the filtering the original fluorescence signal to obtain a first fluorescence signal specifically includes: the method comprises the steps of collecting original fluorescent signals based on a preset sampling number, filtering the original fluorescent signals by using a wavelet filter to obtain first fluorescent signals if the standard deviation of the collected signals is larger than a preset standard deviation threshold value, and filtering the original fluorescent signals by using a Gaussian filter to obtain the first fluorescent signals if the standard deviation of the collected signals is smaller than or equal to the preset standard deviation threshold value.
Further, the identifying droplet parameters based on the first fluorescent signal specifically includes: performing background estimation on the first fluorescence signal to obtain a background value; performing first fluorescence signal peak positioning based on a preset deviation and a background value; the droplet fluorescence intensity and the peak distance are determined based on the peaks, resulting in droplet parameters including at least droplet volume and droplet pitch.
Further, the background estimation on the first fluorescence signal to obtain a background value specifically includes: constructing a first histogram of the first fluorescence signal data; taking the fluorescence intensity value corresponding to the maximum frequency in the histogram as the fluorescence intensity mean value mu of the background signal, and obtaining the deviation R ═ mu-FminWherein, Fmin is the minimum value of all fluorescence intensities; for in [ mu-R, mu + R]Linear fitting is carried out on fluorescence data in the interval to obtain parameters a and b, and the background value at the position T is Fb＝aT+b。
Further, dividing the first fluorescence signal into a plurality of sections based on a preset length, and respectively constructing a histogram for each section of fluorescence signal; carrying out smoothing treatment on each histogram; and setting the fluorescence intensity value corresponding to the maximum frequency in each histogram as the background value of each corresponding section of signal.
Further, the positioning of the peak of the first fluorescence signal based on the preset deviation and the background value specifically includes: based on the background value and the preset deviation, the first fluorescence signal is divided into a plurality of signal segments, and the maximum value in each signal segment is a peak.
Further, the method further comprises the step of performing curve fitting on the peak, and specifically comprises the following steps: and selecting N fluorescence intensity values before and after the peak position based on the preset number N, forming 2N +1 coordinate points by the value sequence and the fluorescence intensity values, and performing quadratic curve fitting or Gaussian curve fitting.
Further, the determining the fluorescence intensity of the droplet and the distance between peaks based on the peaks specifically includes: if curve fitting is carried out on the peak, calculating to obtain the fluorescence intensity of the microdroplet based on the parameters obtained after fitting, otherwise, calculating the fluorescence intensity at the peak to be the fluorescence intensity of the microdroplet, taking the positions before and after the peak, which are smaller than the preset threshold value of the fluorescence intensity of the microdroplet, as two end points, and taking the distance between the two end points as the width of the peak.
Further, the filtered microdroplets are clustered by an Otsu threshold method or Kmeans.
Further, counting the number of negative droplets and positive droplets based on the first threshold, and calculating the concentration of the sample analyte, specifically comprising: droplets with fluorescence intensity greater than a first threshold are positive droplets, droplets with fluorescence intensity less than the first threshold are negative droplets; counting to obtain the number of negative microdroplets and the number of positive microdroplets; modeling the probability of occurrence of N target genes in the droplet, wherein the probability of a target gene obeys a poisson distribution; the number of negative droplets and the number of positive droplets were corrected, and based on the volume of the droplets, the concentration of the sample was calculated.
The invention has the beneficial effects that:
1. filtering the signal, eliminating random error and system error in the signal acquisition process, and selecting a proper filter for filtering according to the actual signal, thereby giving consideration to both performance and filtering effect;
2. by adopting the method of calculating the background value in real time, the problem of baseline drift of the fluorescence signal is solved.
3. In the case that the fluorescence signal is not smooth enough, curve fitting is performed on each peak, so as to obtain the droplet fluorescence intensity and peak width of the calibration.
4. Performing invalid droplet filtration to filter out droplets caused by errors of a droplet generation system, a microfluidic system and a collection system;
5. accurately identifying positive and negative droplets from the droplets by clustering;
6. through multi-step signal processing, the automatic calculation of the number of the negative droplets and the positive droplets and the threshold values of the negative droplets and the positive droplets are realized, so that the accurate calculation of the concentration of the sample is realized.
Drawings
FIG. 1 is a flow chart of the droplet digital PCR fluorescent signal processing according to the embodiment of the present invention;
FIG. 2 is a schematic diagram of fluorescence signals collected by the droplet-based digital PCR according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of filtering raw fluorescence signals according to an embodiment of the present invention;
FIG. 4 is a flow chart of identifying droplet parameters based on a first fluorescent signal according to an embodiment of the present invention;
FIG. 5 is a histogram illustrating background estimation using a linear fitting method according to an embodiment of the present invention;
FIG. 6 is a histogram illustrating background estimation using a piecewise calculation method according to an embodiment of the present invention;
FIG. 7 is a schematic illustration of a peak of an embodiment of the present invention;
FIG. 8 is a schematic illustration of invalid droplet filtration according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of the Otsu (Ostu) thresholding in an embodiment of the invention.
Detailed Description
In order to facilitate a better understanding of the invention for those skilled in the art, the invention will be described in further detail with reference to the accompanying drawings and specific examples, which are given by way of illustration only and do not limit the scope of the invention.
The invention provides a micro-drop type digital PCR (polymerase chain reaction) fluorescent signal processing method which is mainly applied to a micro-drop type digital PCR instrument, wherein the micro-drop type PCR instrument aims to divide a sample into a large number of micro-drops, each micro-drop contains one or zero target genes, after nucleic acid amplification, the fluorescence intensity of the micro-drop containing one target gene is higher, the fluorescence intensity of the micro-drop not containing the target genes is weaker, and the original concentration of the target genes is obtained by respectively counting the two types of micro-drops.
In order to obtain accurate original concentration of a target gene, the flow chart of the method for processing the micro-droplet digital PCR fluorescent signal provided by the invention is shown in FIG. 1, and specifically comprises the following steps: filtering the original fluorescence signal to obtain a first fluorescence signal; identifying a droplet parameter based on the first fluorescent signal; filtering the droplet based on the droplet parameter and a preset droplet threshold; clustering the filtered droplets to obtain a first threshold; counting the number of the negative droplets and the number of the positive droplets based on the first threshold value, and calculating the concentration of the analyte.
The method for processing a droplet-type digital PCR fluorescent signal according to the present invention will be described in detail with reference to examples.
And S1, filtering the original fluorescence signal to obtain a first fluorescence signal.
The fluorescence signals collected by the digital droplet PCR instrument are shown in fig. 2, and include FAM and VIC signals collected from two channels, respectively.
Under the influence of a plurality of aspects such as an acquisition system, a liquid path, a light path and the like, the fluorescence signal acquired by the digital PCR instrument possibly has the phenomena of burrs, baseline drift and the like, and in order to correctly extract the fluorescence intensity of each droplet in the subsequent processing process, the signal needs to be filtered, so that the effective signal is kept, and the random error and the system error in the signal acquisition process are eliminated as much as possible.
The collected background signal and droplet signal are filtered, and various digital filters can be adopted, including a time domain filter, a frequency domain filter and a time frequency filter. Common time-domain filters include a sliding mean filter, a Hampel filter, an SG filter and a Gaussian filter; the frequency domain filter includes a low pass filter, a high pass filter, a band stop filter, and the like. In addition, there is a wavelet filter. The time domain filter is easy to realize and high in speed, the wavelet filter has the best effect, and a proper filter needs to be selected by integrating actually acquired signals.
In some embodiments, if the preset sampling number is N, selecting the first N values of the fluorescence signal of each channel, calculating the standard deviation, and if the standard deviation is greater than the preset standard deviation threshold, indicating that the signal is greatly affected by noise, then using a wavelet filter because the whole filtering effect is better; if the standard deviation is less than or equal to the preset standard deviation threshold, it indicates that the signal fluctuation is small and the influence of noise is small, and at this time, a simple time-domain filter may be used. The signal obtained after filtering the original fluorescence signal is the first fluorescence signal. The original fluorescence signal is shown on the left in FIG. 3, and the first fluorescence signal is shown on the right in FIG. 3.
S2, identifying droplet parameters based on the first fluorescent signal.
The microdroplet corresponds to a peak in the fluorescence signal, the identification of the microdroplet signal is to identify the peak corresponding to the microdroplet from the original fluorescence signal, and obtain the information of the position, the maximum fluorescence intensity, the half-height wave width and the like of each peak, so as to obtain the corresponding microdroplet parameters, and the accurate microdroplet information extraction result is the premise of calculating the original copy number or concentration of the target gene in the final sample. The peak searching process includes the steps of background estimation, peak positioning, peak fitting, parameter calculation, etc., and the flowchart is shown in fig. 4, and each step is specifically described below.
And S21, performing background estimation on the first fluorescence signal to obtain a background value.
Since there may be baseline drift in the fluorescence signal, i.e. the fluorescence intensity of the background signal is different at different times, a fixed background value, but a real-time calculated background value, cannot be used in the whole peak searching process.
Note that, the signal of each channel is calculated separately for calculation of the background value.
In some embodiments, the calculation of the background value is performed using a linear fitting method.
First, a histogram of the first fluorescence signal data is calculated, and as shown in fig. 5, the abscissa of the frequency distribution histogram is the fluorescence intensity, and the ordinate is the frequency. In some embodiments, the histogram toolThe volume calculation method comprises the following steps of equally dividing the range between the minimum fluorescence intensity and the maximum fluorescence intensity into M parts, wherein each part corresponds to a smaller fluorescence intensity range, the abscissa in the histogram is obtained by sequencing from small to large, and the frequency corresponding to each part is the number of fluorescence data of which the fluorescence intensity is in the range. Taking the fluorescence intensity value corresponding to the maximum frequency in the histogram as the fluorescence intensity mean value mu of the background signal, and obtaining the deviation R ═ mu-FminWherein F isminIs the minimum of all fluorescence intensities; for in [ mu-R, mu + R]Linear fitting is carried out on fluorescence data in the interval to obtain parameters a and b, and the background fluorescence intensity at the position T, namely the background value is Fb＝aT+b。
In some embodiments, the calculation of the background value is performed using a piecewise calculation method. Dividing the first fluorescence signal into a plurality of segments according to a preset fixed length, calculating a histogram for all data in each segment, and taking the fluorescence intensity corresponding to the maximum frequency number in the histogram as the background value of the segment as shown in fig. 6. In some embodiments, the histogram is selected to be smoothed by a sliding mean filtering, wherein the right histogram of fig. 6 is smoothed by the left histogram.
And S22, performing first fluorescence signal peak positioning based on the preset deviation and the background value.
Peak location is to find the segment of the fluorescence signal with a preset deviation delta larger than the background value.
In some embodiments, the preset deviation Δ may be a fixed value or a dynamically adjusted value according to the fluorescence intensity of the background.
In one embodiment of the invention, all fluorescence intensities within an interval range are taken based on the background value Fb, e.g. [ F ]b-100，Fb+100]All fluorescence intensities within the range were calculated and the standard deviation σ was calculated. The preset deviation Δ ═ α × σ, where α is a specified multiple of the standard deviation, and may be 1, 2, 3, 3.5. As shown in fig. 7, the segments with a certain preset deviation Δ larger than the background value are respectively taken, each segment corresponds to one peak, and the position corresponding to the maximum value in the segments is the peak position.
S23, determining the fluorescence intensity of the droplets and the distance between the peaks based on the peaks, thereby obtaining droplet parameters at least including droplet volume and droplet spacing.
In some embodiments, the droplet fluorescence intensity is the fluorescence intensity at a peak, positions before and after the peak that are less than a predetermined threshold of the droplet fluorescence intensity are taken as two endpoints, and the distance between the two endpoints is taken as the peak width. For example, if a position is found where the fluorescence intensity is equal to half the fluorescence intensity of the droplet, the width of the peak is defined as the distance between the left and right positions.
In some embodiments, when the fluorescence signal is not smooth enough, a curve fit is performed on each peak after determination, so as to obtain more accurate droplet fluorescence intensity and peak width.
Firstly, taking N fluorescence intensity values (for example, N is 10) before and after the peak position, making the value sequence be an x value and the fluorescence intensity value be a y value, forming 2N +1 (x, y) coordinate points in total, then fitting the coordinate points according to a specific curve type to obtain the parameters of the curve, and obtaining the final droplet fluorescence intensity and the wave width corresponding to the peak according to the fitted parameters.
In some embodiments, curve fitting the peak may employ a quadratic curve, the expression of which is shown in equation (1).
y＝a(x-b)2+c (1)
Wherein the maximum amplitude of the peak is c, a is related to the width of the peak, and b is related to the number of points of fitting, i.e. related to the value of N.
In some embodiments, curve fitting the peaks may employ a gaussian, the expression of which is shown in equation (2).
Where a represents the maximum amplitude of the peak, σ is related to the peak width, and μ is related to the number of points fitted.
In embodiments of the invention, the width of the peaks corresponds to the droplet volume and the spacing between the peaks corresponds to the droplet spacing, thereby obtaining droplet parameters.
S3, filtering the droplets based on the droplet parameters and the preset droplet filtering conditions.
The droplets are filtered to filter out the non-effective droplets. By analyzing the peak of the droplet wave, including the statistical rules of the amplitude and the half-wave width of the droplet, a certain filtering rule is designed, and reasonable droplets are screened out for further processing.
In some embodiments, the predetermined droplet filtering conditions include irregular peaks, too wide half-wave width, too small fluorescence amplitude values, etc., and the invalid droplets are respectively filtered from left to right in a rectangular box as shown in fig. 8, so as to filter out the case that the peak value of one wave is too large, the half-wave width is too wide, and the fluorescence amplitude value is too small.
In general, the half-wave width of the droplet corresponding to the peak follows a Gaussian distribution, so that the 3Sigma principle can be used for filtering the wave width. Amplitude values since the difference in amplitude values between negative and positive droplets is generally large, it cannot be directly assumed that the amplitude values of all droplets obey a gaussian distribution. Droplets with smaller amplitude values are usually filtered out, since this part of the droplets may be due to errors in the acquisition system.
S4, clustering the filtered microdroplets to obtain a first threshold.
Clustering is primarily to distinguish between positive and negative droplets. The distinction of negative/positive droplets is mainly based on the fluorescence intensity of the droplets, the negative droplets having a lower fluorescence intensity and the positive droplets having a relatively higher fluorescence intensity.
In some embodiments, an Otsu (Ostu) thresholding method is employed. The idea of Otsu's algorithm is to select a threshold T to maximize the inter-class variance of the fluorescence intensity of the segmented negative droplets and the fluorescence intensity of the segmented positive droplets. The inter-class variance is defined as shown in equation (3):
σ2＝ω0(μ0-μ)2+ω1(μ1-μ)2＝ω0ω1(μ1-μ0)2 (3)
wherein: mu.s0、μ1Mu indicates negative, positive and allMean value of the droplets, ω0、ω1The percentage of all droplets that were negative and positive, respectively. In specific implementation, all possible T values are traversed, then the inter-class variance is calculated according to the formula, and finally the T value with the maximum inter-class variance is taken. As shown in FIG. 9, when a threshold value T is given, those droplets having fluorescence intensities equal to or less than the threshold value are regarded as negative droplets, and the number of the droplets is N0Calculating the mean value mu of the fluorescence intensity of the negative liquid drop0Those greater than the threshold are considered to be positive droplets, and the number is N1Calculating the mean value mu of the fluorescence intensity of the positive droplets1Then the inter-class variance at this time is as shown by company (4):
and taking the T corresponding to the maximum time between classes as a final threshold value.
In some embodiments, using Kmeans clustering, such as in multiplex gene detection, it is desirable to classify droplets into K classes, rather than just the negative and positive classes described above. To divide the droplets into K groups, the fluorescence intensities of K droplets are randomly selected as initial cluster centers, and then the distance between the fluorescence intensity of each droplet and each seed cluster center, i.e., the absolute value of the difference in fluorescence intensity, is calculated and assigned to the cluster center closest to it. The cluster centers and the droplets assigned to them form a class. After all droplets are dispensed, one iteration is complete. After each iteration, the cluster centers are recalculated based on the existing droplets in each category, see equation (5). The iterations are repeated until some termination condition is met, such as droplets in each category no longer changing, or the maxIter iterations are repeated and terminated.
The cluster centers are generally represented by the mean value shown in equation (5).
And S5, counting the number of the negative microdroplets and the positive microdroplets based on the first threshold value, and calculating the concentration of the analyte.
The objective of the droplet PCR instrument is to obtain the original concentration of target gene by dividing the sample into a large number of droplets, each of which contains one or zero target gene, and after nucleic acid amplification, the fluorescence intensity of the droplet containing one target gene is higher, while the fluorescence intensity of the droplet not containing the target gene is weaker, and counting the two types of droplets separately.
However, in practical cases, it cannot be guaranteed that each droplet contains at most one target gene, i.e. there is a certain probability that some droplets contain more than one target gene. In order to accurately quantify the copy number of the target gene, it is necessary to model the probability that N (N ═ 0, 1, 2, …) target genes appear in the droplet. When the number of droplets is large, it can be assumed that the probability obeys the poisson distribution, and a certain correction is made on the basis of the statistics of the number of negative/positive droplets to obtain the final copy number of the target gene, thereby obtaining the concentration, wherein the concentration is equal to the copy number/droplet volume.
If the first threshold value obtained in step S4 is greater than the first threshold value, the droplets are positive droplets, and if the threshold value is less than the first threshold value, the droplets are negative droplets, so that the number N of the droplets that are negative is countednAnd the number of positive droplets NpAnd the volume of the droplet is VnL, the concentration c of the final target gene is shown in formula (6), wherein the unit of c is copy number/nL.
It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily performed in the order shown and described herein. In some other embodiments, the method may include more or fewer steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps for description in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.
The foregoing merely illustrates the principles and preferred embodiments of the invention and many variations and modifications may be made by those skilled in the art in light of the foregoing description, which are within the scope of the invention.
Claims (10)
1. A method for processing a droplet-type digital PCR fluorescent signal is characterized by comprising the following steps:
filtering the original fluorescence signal to obtain a first fluorescence signal;
identifying a droplet parameter based on the first fluorescent signal;
filtering the droplets based on the droplet parameters and preset droplet filtration conditions;
clustering the filtered droplets to obtain a first threshold;
counting the number of the negative droplets and the number of the positive droplets based on the first threshold value, and calculating the concentration of the analyte.
2. The method for processing the micro-droplet digital PCR fluorescent signal according to claim 1, wherein the filtering the original fluorescent signal to obtain the first fluorescent signal specifically comprises: the method comprises the steps of collecting original fluorescent signals based on a preset sampling number, filtering the original fluorescent signals by using a wavelet filter to obtain first fluorescent signals if the standard deviation of the collected signals is larger than a preset standard deviation threshold value, and filtering the original fluorescent signals by using a Gaussian filter to obtain the first fluorescent signals if the standard deviation of the collected signals is smaller than or equal to the preset standard deviation threshold value.
3. The method of claim 1, wherein the PCR signal processing method is a digital PCR signal processing method,
the identifying droplet parameters based on the first fluorescent signal specifically includes:
performing background estimation on the first fluorescence signal to obtain a background value;
performing first fluorescence signal peak positioning based on a preset deviation and a background value;
the droplet fluorescence intensity and the peak distance are determined based on the peaks, resulting in droplet parameters including at least droplet volume and droplet pitch.
4. The method for processing the micro-droplet digital PCR fluorescent signal according to claim 3, wherein the background estimation of the first fluorescent signal to obtain the background value specifically comprises:
constructing a first histogram of the first fluorescence signal data;
taking the fluorescence intensity value corresponding to the maximum frequency in the histogram as the fluorescence intensity mean value mu of the background signal, and obtaining the deviation R ═ mu-FminWherein, Fmin is the minimum value of all fluorescence intensities;
for in [ mu-R, mu + R]Linear fitting is carried out on fluorescence data in the interval to obtain parameters a and b, and the background value at the position T is Fb＝aT+b。
5. The method for processing the micro-droplet digital PCR fluorescent signal according to claim 3, wherein the background estimation of the first fluorescent signal to obtain the background value specifically comprises:
dividing the first fluorescence signal into a plurality of sections based on a preset length, and respectively constructing a histogram for each section of fluorescence signal;
carrying out smoothing treatment on each histogram;
and setting the fluorescence intensity value corresponding to the maximum frequency in each histogram as the background value of each corresponding section of signal.
6. The method for processing the droplet-type digital PCR fluorescence signal according to claim 4 or 5, wherein the positioning of the first fluorescence signal peak based on the preset deviation and the background value specifically comprises: based on the background value and the preset deviation, the first fluorescence signal is divided into a plurality of signal segments, and the maximum value in each signal segment is a peak.
7. The method for processing the droplet-type digital PCR fluorescent signal according to claim 6, further comprising curve fitting the peak, specifically comprising: and selecting N fluorescence intensity values before and after the peak position based on the preset number N, forming 2N +1 coordinate points by the value sequence and the fluorescence intensity values, and performing quadratic curve fitting or Gaussian curve fitting.
8. The method for processing the droplet-type digital PCR fluorescent signal according to claim 7, wherein the determining the droplet fluorescent intensity and the distance between peaks based on the peaks comprises:
if the peak is subjected to curve fitting, calculating to obtain the fluorescence intensity of the microdroplet based on the parameters obtained after fitting, otherwise, the fluorescence intensity at the peak is the fluorescence intensity of the microdroplet,
positions before and after the peak are smaller than a preset threshold value of the fluorescence intensity of the droplet are used as two end points, and the distance between the two end points is used as the width of the peak.
9. The method for processing the micro-droplet digital PCR fluorescent signal according to claim 7, wherein the filtered micro-droplets are clustered by Otsu threshold or Kmeans.
10. The method for processing the digital PCR fluorescence signal in the droplet form according to claim 9, wherein the counting the number of the negative droplets and the positive droplets based on the first threshold and calculating the concentration of the sample analyte specifically comprises:
droplets with fluorescence intensity greater than a first threshold are positive droplets, droplets with fluorescence intensity less than the first threshold are negative droplets;
counting to obtain the number of negative microdroplets and the number of positive microdroplets;
modeling the probability of occurrence of N target genes in the droplet, wherein the probability of a target gene obeys a poisson distribution;
the number of negative droplets and the number of positive droplets were corrected, and based on the volume of the droplets, the concentration of the sample was calculated.
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