CN118737291A - Method, system and device for realizing normalization of detection signal of gene analyzer - Google Patents

Abstract

The invention provides a method, a system and equipment for normalizing detection signals of a gene analyzer, wherein the method comprises the following steps: performing internal standard matching on spectrum signals of internal standard substance channels of each capillary tube acquired in the electrophoresis process of the gene analyzer, and calculating a single-channel internal standard average value of the internal standard substance channels of each capillary tube according to an internal standard matching sequence which is successfully matched; dividing a preset internal standard value by a single-channel internal standard average value of an internal standard object channel of each capillary tube respectively, and taking an obtained calculation result as a normalization coefficient of the corresponding capillary tube; and respectively carrying out normalization correction on the spectrum signals of other color channels in each capillary according to the normalization coefficient of each capillary, so as to realize detection signal normalization. The invention realizes the adjustment of the peak height of the sample to be detected according to the peak height of the internal standard substance, can improve the signal difference between different capillaries and the difference of different detection times of the same capillary, and effectively improves the quality of the gene analysis result.

Description

Method, system and device for realizing normalization of detection signal of gene analyzer

Technical Field

The present invention relates to the field of biochemical detection technology, and in particular to a method, system and device for realizing normalization of detection signals of a gene analyzer.

Background Art

Genetic analyzers based on capillary electrophoresis technology can be applied to Sanger sequencing and gene fragment analysis. Capillary electrophoresis uses quartz capillaries as separation channels, high-voltage DC electric fields as driving forces, and porous gels as supporting media. Temperature control is used to ensure that the pore size of the gel is distributed in the DNA conformation. When the size of a DNA molecule is comparable to the pore size of the gel, its mobility is related to its size. Short fragments are less obstructed and flow faster from the capillary, while long fragments are more obstructed and flow slower from the capillary. Because DNA molecules are negatively charged, after adding high-voltage DC electricity to both ends of the capillary, DNA labeled with fluorescent groups will enter the capillary from the cathode port of the capillary through electrical injection, and flow toward the anode. DNA molecules of different lengths will pass through the detection window one after another. When a DNA molecule passes through the optical detection window, the fluorescent group on the DNA is excited by the laser to generate fluorescence, which is then collected by the spectrometer. The spectrometer converts the optical signal into an electrical signal and then into a digital signal. After processing the original digital signal, the base sequence or relative fragment length of the DNA molecule can be obtained through analysis software.

The signals collected by the genetic analyzer are essentially optical signals. Optical signals are divided into fluorescent signals on fluorescent groups on DNA and Raman signals on electrophoresis gels. Raman signals are generally used to characterize the state of the optical system, while DNA fluorescence signals are affected by many factors such as loading time, loading voltage, loading position, sample concentration, optical system, and the state of the capillary itself. Therefore, when using a genetic analyzer for detection, it is inevitable that there will be signal differences between different capillaries and differences in the number of detections of the same capillary, which will lead to deviations in the detection results. How to solve the signal differences between different capillaries and the signal differences in the number of detections of the same capillary is of great significance to improving the quality of genetic analysis results.

Summary of the invention

In view of the above problems, the present invention is proposed to provide a method, system and device for realizing normalization of detection signals of a gene analyzer, which overcomes the above problems or at least partially solves the above problems.

One aspect of the present invention provides a method for normalizing detection signals of a gene analyzer, the method comprising:

Performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, and calculating the single-channel internal standard average value of each capillary internal standard channel according to the successfully matched internal standard matching sequence;

The preset internal standard value is divided by the single channel internal standard average value of the internal standard channel of each capillary, and the calculated result is used as the normalization coefficient of the corresponding capillary;

According to the normalization coefficient of each capillary, the spectral signals of other color channels in each capillary are normalized and corrected respectively to achieve normalization of the detection signal.

Optionally, before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, the method further comprises:

Performing electrophoresis on a multi-channel capillary filled only with an internal standard of a specified concentration to collect the spectral signal of the internal standard channel of each capillary and perform internal standard matching, calculating the average peak value of each internal standard peak in the internal standard matching sequence corresponding to each capillary that meets the internal standard matching requirements, and obtaining the single-channel internal standard average value of each capillary internal standard channel;

The coefficient of variation of the single-channel internal standard average value of each capillary internal standard channel is calculated. If the coefficient of variation is less than a preset variation threshold, the mean of the single-channel internal standard average values of each capillary internal standard channel or a preset multiple of the mean is used as the internal standard standard value.

Optionally, after using the obtained calculation result as the normalization coefficient of the corresponding capillary, the method further includes:

Determine whether the normalization coefficient of each capillary is compliant. When the normalization coefficient of the capillary is within the preset normalization coefficient value range, the normalization coefficient of the current capillary is determined to be compliant. Otherwise, the normalization coefficient of the current capillary is determined to be non-compliant.

If the ratio of the number of capillaries with non-compliant normalization coefficients in the current electrophoresis detection data to the total number of capillaries is greater than a preset first ratio threshold, or if the ratio of the number of capillaries with non-compliant normalization coefficients in the electrophoresis detection data for at least three consecutive times to the total number of capillaries is greater than a preset second ratio threshold, the internal standard value is corrected;

The second ratio threshold is smaller than the first ratio threshold.

Optionally, the normalizing and correcting the spectral signals of other color channels in the respective capillaries according to the normalization coefficient of each capillary comprises:

If the normalization coefficient of the capillary is in compliance, the spectral signals of other color channels in the current capillary are multiplied by the normalization coefficient of the corresponding capillary to achieve spectral signal normalization correction;

If the normalization coefficient of the capillary is not compliant, the spectral signals of other color channels in the current capillary are multiplied by the normalization coefficient of the corresponding capillary close to the extreme value within the normalization coefficient value range to achieve spectral signal normalization correction.

Optionally, before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, the method further comprises:

Monitoring the Raman signal during the electrophoresis process, and obtaining the signal value, peak balance data and/or uniformity data of the Raman signal;

When any parameter of the signal value, peak balance data and/or uniformity data of the Raman signal does not meet the corresponding preset optical parameter standard, an optical alignment operation is performed until the signal value, peak balance data and/or uniformity data all meet the corresponding optical parameter standard.

Optionally, the performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer includes:

S10, performing data pre-processing on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer to filter out the background noise of the spectral signal and perform smoothing on the spectral signal;

S11, performing peak recognition on the spectral signal after data pre-processing to screen out candidate peak sequences contained in the current spectral signal;

S12. Perform internal standard matching on the candidate peaks in the candidate peak sequence and the standard peak sequence in the standard spectral signal corresponding to the internal standard selected in the electrophoresis process in sequence according to the sampling order, so as to find the candidate peak combination in the candidate peak sequence that meets the preset internal standard matching conditions, and the number of candidate peaks in the candidate peak combination is greater than or equal to 3; the internal standard matching conditions include that the absolute value of the difference between the first distance and the second distance is less than the preset distance error threshold, and max{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}/min{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}<preset relative height threshold, wherein the first distance is the distance between the peak points of adjacent candidate peaks in the candidate peak sequence, and the second distance is the distance between the peak points of adjacent standard peaks in the standard peak sequence that have the same sampling order as the candidate peak for calculating the first distance currently;

S13, using each candidate peak combination as a matching basis, sequentially performing internal standard matching on other candidate peaks in the candidate peak sequence to obtain a matching result corresponding to each candidate peak combination;

S14, counting the number of candidate peaks included in the matching results corresponding to each candidate peak combination;

S15. When the maximum number of candidate peaks contained in the matching results corresponding to each candidate peak combination is equal to the number of standard peaks contained in the standard peak sequence, the internal standard matching is determined to be successful, and the matching result corresponding to the maximum value is used as the optimal internal standard matching result.

Optionally, after taking the matching result corresponding to the maximum value as the optimal internal standard matching result, the method includes:

Perform curve fitting on the standard peak sequence and the peak sequence of the optimal internal standard matching result, and use the standard deviation, average residual and maximum residual after the curve fitting as characteristic data of the current optimal internal standard matching result;

The characteristic data is input into a preset internal standard matching scoring model for identification to obtain a matching degree score of the current optimal internal standard matching result.

Optionally, the method further comprises:

If the maximum number of candidate peaks included in the matching results corresponding to each candidate peak combination is not equal to the number of standard peaks included in the standard peak sequence, the distance error threshold is updated according to the preset first threshold adjustment rule, and the process returns to step S12 until the updated distance error threshold is greater than the maximum value of the distance error threshold;

When the updated distance error threshold is greater than the maximum value of the distance error threshold, the relative height threshold is updated according to the preset second threshold adjustment rule, and the distance error threshold is updated to the corresponding initial value, and the process returns to step S12 until the updated relative height threshold is greater than the maximum value of the relative height threshold;

When the updated relative height threshold is greater than the maximum value of the relative height threshold, it is determined that the internal standard matching fails.

In a second aspect, the present invention further provides a system for realizing normalization of detection signals of a gene analyzer, wherein the system comprises a functional module for realizing the above method for realizing normalization of detection signals of a gene analyzer.

In a third aspect, the present invention further provides a computer device, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for normalizing detection signals of a genetic analyzer when executing the computer program.

The method, system and device for realizing normalization of detection signals of a gene analyzer provided by the embodiments of the present invention calculate the normalization coefficient of the corresponding capillary through the preset internal standard standard value and the single-channel internal standard average value of the internal standard channel of each capillary in the electrophoresis process of the gene analyzer, and perform normalization correction on the spectral signals of other color channels in each capillary according to the normalization coefficient of each capillary, so as to adjust the peak height of the sample to be tested according to the peak height of the internal standard, improve the signal difference between different capillaries, and the difference of different detection times of the same capillary, and effectively improve the quality of gene analysis results.

The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below.

BRIEF DESCRIPTION OF THE DRAWINGS

Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

FIG1 is a flow chart of a method for realizing normalization of detection signals of a gene analyzer provided by an embodiment of the present invention;

FIG2 is a schematic diagram of the implementation principle of the similar distance in the internal standard matching condition provided by an embodiment of the present invention;

3 is a flow chart of an internal standard matching method for a gene analyzer detecting a spectrum provided by an embodiment of the present invention;

FIG. 4 is a structural block diagram of a system for realizing normalization of detection signals of a gene analyzer provided by an embodiment of the present invention.

DETAILED DESCRIPTION

The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.

Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

When using a gene analyzer for detection, it is inevitable that the signal difference between different capillaries and the difference in the number of different detections of the same capillary are always present, which leads to the deviation of the detection result. The factors causing signal deviation include loading time, loading voltage, loading position, sample concentration, optical system, etc. For general instruments, the loading time can be controlled by software, and the loading voltage can be corrected by a test fixture. The loading position can be corrected by a loading station, and the optical system can be corrected by optical alignment. However, the factors causing signal deviation include, in addition to loading time, loading voltage, loading position, sample concentration, optical system, etc., there are also deviations caused by, for example, the inner diameter of the capillary group, resistance deviations caused by poor contact between a certain capillary and the electrode, resistance deviations caused by random entry of bubbles into the capillary, and resistance deviations caused by uneven temperatures of different capillaries, etc., caused by the state of the capillary itself. These factors cannot be completely avoided, so in order to avoid affecting the quality of the gene analysis results due to the signal deviation of the detection signal, the present invention proposes a method for realizing the normalization of the detection signal of a gene analyzer.

FIG1 schematically shows a flow chart of a method for realizing normalization of detection signals of a gene analyzer according to an embodiment of the present invention. Referring to FIG1 , the method for realizing normalization of detection signals of a gene analyzer according to an embodiment of the present invention specifically comprises the following steps:

S1. Perform internal standard matching on the spectral signals of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, and calculate the single-channel internal standard average value of each capillary internal standard channel according to the successfully matched internal standard matching sequence.

In an embodiment of the present invention, before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis of the gene analyzer, the method further includes the step of establishing an internal standard standard value, specifically including: performing electrophoresis on a multi-channel capillary containing only an internal standard of a specified concentration to collect the spectral signal of the internal standard channel of each capillary and perform internal standard matching, calculating the average peak value of each internal standard peak in the internal standard matching sequence corresponding to each capillary that meets the internal standard matching requirements, and obtaining the single-channel internal standard average value of each capillary internal standard channel; calculating the coefficient of variation of the single-channel internal standard average value of each capillary internal standard channel, if the coefficient of variation is less than a preset variation threshold, such as 30%, then the mean of the single-channel internal standard average value of each capillary internal standard channel or a preset multiple of the mean is used as the internal standard standard value. If the coefficient of variation is greater than or equal to the preset variation threshold, the step of establishing the internal standard standard value is re-executed. Wherein, the preset multiple can be selected as 00.1-10 times.

Specifically, the specific calculation model of the coefficient of variation CV is: CV = standard deviation/mean;

in: n is the number of capillary channels, and _xi is the single-channel internal standard average value of the i-th capillary channel.

Specifically, when establishing internal standard value, use a certain concentration of internal standard to carry out electrophoresis, through data pre-processing, peak identification, internal standard matching, scoring and other steps for realizing internal standard matching, select qualified internal standard matching sequence data for peak normalization. If unqualified, i.e., the matching success of internal standard is not realized, then the secondary data is not used. In an optional embodiment, for a single-channel instrument, the average peak height of all internal standards in a single channel is recorded as the single-channel internal standard average value. For example, there are 10 peaks in an internal standard, then the average value of 10 peaks is calculated, and the single-channel internal standard average value is obtained, which is used as the internal standard value. In another optional embodiment, for an 8-channel instrument, if the data of 8 channels are all qualified, the single-channel internal standard average value of 8 channels is calculated, and then the average value of the average value of 8 channel peak values, i.e., the average value of 8 single-channel internal standard average values, is taken to obtain the internal standard value.

Among them, the DNA sample electrophoresed by the gene analyzer is usually divided into three components, formamide or pure water as a solvent, an internal standard as a ruler, and a sample to be tested labeled with a fluorescent group. Among them, the concentration of the internal standard is generally fixed. When establishing the internal standard standard value, the concentration of the internal standard is the same as the concentration of the internal standard in the sample to be tested during detection, and the concentration of the sample to be tested is unknown. Therefore, adjusting the peak height of the sample to be tested according to the peak height of the internal standard can improve the signal deviation within a certain range.

S2. Divide the preset internal standard value by the single-channel internal standard average value of the internal standard channel of each capillary, and use the calculated result as the normalization coefficient of the corresponding capillary.

S3. According to the normalization coefficient of each capillary, the spectral signals of other color channels in each capillary are normalized and corrected to achieve normalization of the detection signal.

The method for realizing normalization of detection signals of a gene analyzer provided in an embodiment of the present invention calculates the normalization coefficient of the corresponding capillary through a preset internal standard standard value and a single-channel internal standard average value of the internal standard channel of each capillary during electrophoresis of the gene analyzer, and performs normalization correction on the spectral signals of other color channels in each capillary according to the normalization coefficient of each capillary, thereby adjusting the peak height of the sample to be tested according to the peak height of the internal standard, improving the signal difference between different capillaries, and the difference of different detection times of the same capillary, and effectively improving the quality of the gene analysis result.

In an embodiment of the present invention, after using the obtained calculation result as the normalization coefficient of the corresponding capillary, the method also includes: judging whether the normalization coefficient of each capillary is compliant, and when the normalization coefficient of the capillary is within the preset normalization coefficient value range, the normalization coefficient of the current capillary is judged to be compliant, otherwise the normalization coefficient of the current capillary is judged to be non-compliant; if the proportion of the number of capillaries with non-compliant normalization coefficients in the current electrophoresis detection data to the total number of capillaries is greater than a preset first ratio threshold, or, the proportion of the number of capillaries with non-compliant normalization coefficients in the electrophoresis detection data for at least three consecutive times to the total number of capillaries is greater than a preset second ratio threshold, then the internal standard value is corrected; wherein the second ratio threshold is less than the first ratio threshold.

Specifically, the normalization coefficient of the capillary should be within a certain range of values. If it exceeds the range, the normalization coefficient of the current capillary is determined to be non-compliant. Optionally, the normalization coefficient value range can be between 0.3-3. If during the detection process, it is found that the proportion of the number of capillaries with non-compliant normalization coefficients in the current electrophoresis detection data to the total number of capillaries is greater than the preset first ratio threshold, or, the proportion of the number of capillaries with non-compliant normalization coefficients in the electrophoresis detection data for at least three consecutive times is greater than the preset second ratio threshold, it is determined that the internal standard value needs to be corrected. Among them, the first ratio threshold can be selected as 50%, and the second ratio threshold can be selected as 30%.

Further, the internal standard value is corrected including calculating the coefficient of variation of each capillary normalization coefficient. If the coefficient of variation of each capillary normalization coefficient is less than a preset second variation threshold, such as 30%, the mean of the single-channel internal standard average value of each capillary internal standard channel of all capillaries or a preset multiple of the mean is used as a new internal standard value. If the coefficient of variation is greater than or equal to the preset second variation threshold, it is prompted to re-establish the internal standard value by electrophoresis. The preset multiple can be selected as 00.1-10 times. The coefficient of variation of each capillary normalization coefficient is the ratio of the standard deviation of the current capillary normalization coefficient to the average of all capillary normalization coefficients, and the calculation method refers to the coefficient of variation of the single-channel internal standard average value of the capillary internal standard channel.

In an embodiment of the present invention, the spectral signals of other color channels in each capillary are normalized and corrected according to the normalization coefficient of each capillary, including: if the normalization coefficient of the capillary is compliant, the spectral signals of other color channels in the current capillary are multiplied by the normalization coefficient of the corresponding capillary to achieve normalization correction of the spectral signal; if the normalization coefficient of the capillary is not compliant, the spectral signals of other color channels in the current capillary are multiplied by the normalization coefficient of the corresponding capillary that is close to the extreme value within the normalization coefficient value range to achieve normalization correction of the spectral signal.

Specifically, during the test process, it can be selected whether to perform normalization correction on the spectral signal according to the test needs. If selected, after one electrophoresis is completed, after data pre-processing, peak identification, internal standard matching, scoring and other steps for achieving internal standard matching, select qualified internal standard matching sequence data for peak normalization. If unqualified, the data is not calculated. The specific normalization correction is implemented by using the standard value divided by the single-channel internal standard average value of each capillary to obtain the normalization coefficient of each capillary. In the capillary, the spectral signals of other color channels are multiplied by the coefficient to achieve the purpose of spectral signal normalization. Among them, the normalization coefficient should be within a certain range of values. If there is a capillary that exceeds the range of the normalization coefficient, but the condition for correcting the internal standard value has not been reached, when normalizing the spectral signals of other color channels in the current capillary, the extreme value of the normalization coefficient range close to the normalization coefficient of the current capillary can be used as the normalization coefficient, and it can be multiplied with the spectral signals of other color channels to achieve normalization correction.

In an embodiment of the present invention, before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the genetic analyzer, the method further includes: monitoring the Raman signal during the electrophoresis process to obtain the signal value, peak balance data and/or uniformity data of the Raman signal; when any parameter among the signal value, peak balance data and/or uniformity data of the Raman signal does not meet the corresponding preset optical parameter standard, performing an optical alignment operation until the signal value, peak balance data and/or uniformity data all meet the corresponding optical parameter standard.

Specifically, each time the capillary consumables are replaced, automatic optical alignment is performed to reduce the impact of the optical system caused by the deviation of capillary production and installation. If the capillary consumables are not replaced, the optical system will also be affected by lens contamination, capillary contamination, vibration, etc. At this time, the Raman signal during the electrophoresis process can be monitored to determine whether optical impact occurs. The monitoring of Raman signals specifically includes: spectral signal value judgment of each channel, peak balance (tilt) judgment and uniformity judgment. If the spectral signal value of each channel is greater than the standard value, the standard value can be selected from 100-10000 according to the test scenario, and the peak balance data is less than the balance standard value, the balance standard value can be selected from 100-10000, and the uniformity data is greater than the uniformity standard value, the uniformity standard value can be selected from 0.1-1, uniformity = min/max, and the peak balance data is taken as the absolute value of the difference between the average value of the left baseline (such as 560-590) and the average value of the right baseline (such as 630-660). Wherein, min is the minimum value of the spectral signal value of each channel, and max is the maximum value of the spectral signal value of each channel. During each electrophoresis, if a value is found to be unqualified, the optical alignment operation is automatically performed.

Furthermore, the implementation process of the optical alignment operation includes:

Start the optical automatic alignment process and run the alignment motor to the optocoupler zero position.

The alignment motor moves toward the photocouple limit, and the movement speed is set by the lower computer, which can be selected as 1000um/s. During the movement of the alignment motor, the spectrum data of the spectrometer is read in real time and displayed on the interface.

Taking four channels as an example, the detection values of the four channels are judged. If they are all greater than the product of the preset spatial correction standard value and the spatial correction optical alignment threshold, the alignment motor slow scan begins. Among them, the spatial correction standard value can be selected as 500, and the spatial correction optical alignment threshold can be selected as 50%. The detection value calculation method is as follows: find the maximum spectral intensity value between the sampling wavelength lower limit (610) and the sampling wavelength upper limit (620), and subtract the baseline value. Baseline calculation method: calculate the average value of the left baseline lower limit and the left baseline upper limit (left baseline, 560-590) and the average value of the right baseline lower limit and the right baseline upper limit (right baseline, 630-660), and then calculate the average of the left baseline average value and the right baseline average value.

The scan motor single motion step number threshold is preset for each movement of the alignment motor. The scan motor single motion step number threshold can be selected as 8 subdivisions. The spectral signal values of the four channels are recorded in the optical automatic alignment file.

The spectral signal value of a channel is judged. If they are all less than the product of the spatial correction standard value and the spatial correction optical alignment threshold, the spectral signal value of a channel is recorded, the slow scanning of the alignment motor is ended, and the single-step signal value file is recorded. If the value is not found, when the number of spectra taken exceeds 500 (if the spatial correction is set to 0.4 seconds integration time, it is 200 seconds), the single-step signal value file is still recorded, and the prompt "Finding alignment position failed" is displayed.

Calculate the four channel spectral signal values recorded in the optical automatic alignment file. Calculate the range coefficient using the four channel spectral signals at each position, take the position with the range coefficient greater than the range coefficient threshold as the candidate position, and select the position with the largest average value among all the candidate positions as the alignment position. Among them, the range coefficient = min/max, and the range coefficient can be used to evaluate whether the alignment is uniform.

The alignment motor moves to the alignment position. The movement steps are: the difference between the current position of the alignment motor and the alignment position.

Take the spectral signal value, that is, the average detection value within the preset acquisition time. The preset acquisition time can be selected as 10 seconds, and compare it with the spatial correction standard value. If the spectral signal value is greater than the spatial correction standard value, enter the peak lift judgment.

Peak tilt judgment: The average value of the left baseline lower limit and the left baseline upper limit (i.e., the left baseline, 560-590) minus the average value of the right baseline lower limit and the right baseline upper limit (i.e., the right baseline 630-660), take the absolute value, and compare it with the preset normalized symmetry value. The normalized symmetry value can be selected as 2000. If it is greater than, it is tilted and the judgment fails. If it is less than, it is judged that the spatial correction passes and the normalized value is calculated. If the spatial correction fails, it stays at the position where the sum of the four channels is the largest.

In the embodiment of the present invention, internal standard matching is performed on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, specifically including the following steps not shown in the drawings:

S10, performing data pre-processing on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer to filter out the background noise of the spectral signal and perform smoothing on the spectral signal;

S11, performing peak recognition on the spectral signal after data pre-processing to screen out candidate peak sequences contained in the current spectral signal.

Specifically, step S11 specifically includes: identifying the maximum points in the spectral signal, calculating the peak characteristics of the peak signal corresponding to each maximum point, the peak characteristics including one or more characteristics of peak height, bottom peak width, half-height width, peak spacing and adjacent point drop height; screening the peak characteristics of the peak signal corresponding to each maximum point in the order of peak height, half-height width, peak spacing, adjacent point drop height and bottom peak width, and taking the peak signals that meet the corresponding threshold requirements of each peak characteristic as candidate peaks to form a candidate peak sequence.

In this embodiment, after obtaining the collected fluorescence spectrum signal, the maximum and minimum values of all fluorescence intensities in the fluorescence spectrum signal are identified. The maximum value here is the peak value we are looking for, and the following characteristics of each peak are calculated:

Peak height: The height of the peak, which refers to the size of the peak;

Bottom peak width: the distance between the left and right boundaries of the peak;

Half-height width: Move from the peak to the left and right sides until the fluorescence intensity drops to half the height of the peak, and then measure the horizontal distance between the two positions;

Peak distance: the horizontal distance between the highest points of two peaks;

Neighboring point drop height: the height difference between the peak and the adjacent points.

The threshold requirements corresponding to these features are set by testing the accuracy and the characteristics of the selected internal standard, as shown in Table 1, and the peak points are screened to leave the peak points that meet the requirements to form a candidate peak sequence.

Table 1 Threshold requirements

In a specific embodiment, the screening rules are as follows:

Peak identification ==> peak height ==> half-height width ==> peak spacing ==> adjacent point drop height ==> bottom peak width

In this embodiment, individual features are judged one by one, and the judgment order is related to the data characteristics. For example, half-width ==> peak spacing. In these two orders, the half-width is judged first to remove abnormal peaks. Some peaks with smaller half-width are abnormal peaks, but the peak height is higher. In the judgment of the peak spacing, the peak height is used to make judgments in order. If the abnormal peaks are not removed, some normal peaks will not be recognized in the peak spacing, so the order here cannot be changed.

S12. Perform internal standard matching on the candidate peaks in the candidate peak sequence and the standard peak sequence in the standard spectral signal corresponding to the internal standard selected in the electrophoresis process in sequence according to the sampling order, so as to find the candidate peak combination in the candidate peak sequence that meets the preset internal standard matching conditions, and the number of candidate peaks in the candidate peak combination is greater than or equal to 3; the internal standard matching conditions include that the absolute value of the difference between the first distance and the second distance is less than the preset distance error threshold, and max{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}/min{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}<preset relative height threshold, wherein the first distance is the distance between the peak points of adjacent candidate peaks in the candidate peak sequence, and the second distance is the distance between the peak points of adjacent standard peaks in the standard peak sequence that have the same sampling order as the candidate peak for calculating the first distance currently.

Specifically, step S12 specifically includes: obtaining the distance between any adjacent candidate peak peak points in the candidate peak sequence, and obtaining the distance between any adjacent standard peak peak points in the standard peak sequence; matching a corresponding number of candidate peaks that meet the preset internal standard matching conditions from the candidate peak sequence in the sampling order according to the number of candidate peaks included in the preset candidate peak combination to obtain a candidate peak combination.

In an embodiment of the present invention, two acceptable indicators, distance error and relative height, are introduced when performing internal standard matching, and internal standard matching is performed according to the internal standard matching mode that satisfies both distance similarity and height uniformity as needed. Among them, the distance error can be a distance length error or a ratio error ratioD, and the relative height is the relative peak height relativeH. In a specific embodiment, the ratio error ratioD is used as the distance error, and the relative peak height relativeH is used as the relative height for explanation. Optionally, the ratio error generally takes a value less than 2; the relative peak height relativeH takes a value greater than 1.

The proportional error ratioD is explained below. Referring to Figure 2, the internal standard (standard) is known, the standard peak sequence is represented by red in Figure 2, and the candidate peak sequence is represented by blue in Figure 2. The values of a1, a2, ... are calculated. In the standard electrophoresis data, i.e., the candidate peak sequence, if |ai-bi|<ratioD, then it is considered that the distance corresponding to bi meets the requirements. Here, ratioD is an acceptable proportional error. ai and bi represent the coefficients of the distance relationship between the standard internal standard and the identified internal standard, respectively. "||" indicates the symbol for calculating the absolute value.

The relative peak height relativeH is explained below. Each time a new peak (peak to be selected) is added or matched, it must satisfy max{peak height of the selected peak, peak height of the peak to be selected}/min{peak height of the selected peak, peak height of the peak to be selected}<relativeH.

Among them, the threshold of the ratio error ratioD is matchTH, which indicates the maximum acceptable ratio error; the threshold of the relative peak height relativeH is evennessTH, which indicates the maximum acceptable relative peak height error. Here, relativeH is converted from evennessTH through a certain threshold adjustment rule or function, and ratioD is converted from matchTH through a certain threshold adjustment rule or function.

S13, using each candidate peak combination as a matching basis, sequentially performing internal standard matching on other candidate peaks in the candidate peak sequence to obtain a matching result corresponding to each candidate peak combination.

S14. Count the number of candidate peaks included in the matching results corresponding to each candidate peak combination.

S15. When the maximum number of candidate peaks contained in the matching results corresponding to each candidate peak combination is equal to the number of standard peaks contained in the standard peak sequence, the internal standard matching is determined to be successful, and the matching result corresponding to the maximum value is used as the optimal internal standard matching result.

The step S15 of taking the matching result corresponding to the maximum value as the optimal internal standard matching result specifically includes: if there is only one matching result corresponding to the maximum value, taking the matching result corresponding to the maximum value as the optimal internal standard matching result; if there is more than one matching result corresponding to the maximum value, taking the matching result with the largest sampling point position of the first candidate peak among the matching results corresponding to the maximum value as the optimal internal standard matching result.

The embodiment of the present invention performs peak recognition on the spectral signal to screen out the candidate peak sequence that meets the requirements contained in the current spectral signal, and then based on the two internal standard matching conditions of distance similarity and height uniformity, internal standard matching is performed on the candidate peaks in the candidate peak sequence and the standard peak sequence in the standard spectral signal corresponding to the internal standard selected in the electrophoresis process in sequence in accordance with the sampling order to find the candidate peak combination that meets the preset internal standard matching conditions in the candidate peak sequence, and the candidate peak combination is used as the initial part of the matching result, and based on this, internal standard matching is performed on other candidate peaks in the candidate peak sequence in sequence to obtain a complete matching result corresponding to each candidate peak combination. When the number of candidate peaks contained in the obtained matching result is equal to the number of standard peaks contained in the standard peak sequence, it is determined that the internal standard matching is successful and the current matching result is used as the optimal internal standard matching result. The present invention can quickly and accurately achieve the optimal matching of the internal standard peak, thereby ensuring the accuracy of the spectral detection fragment length.

In an embodiment of the present invention, after the matching result corresponding to the maximum value is used as the optimal internal standard matching result, the method includes an internal standard matching degree scoring operation. The specific implementation is as follows: curve fitting is performed on the standard peak sequence and the peak sequence of the optimal internal standard matching result, and the standard deviation, average residual and maximum residual after the curve fitting are used as feature data of the current optimal internal standard matching result; the feature data is input into a preset internal standard matching scoring model for identification to obtain a matching degree score of the current optimal internal standard matching result.

Specifically, in the present invention, when the internal standard match is successful, the Local Southern method or the high-order polynomial fitting method can be used to implement curve fitting of the standard peak sequence and the peak sequence of the internal standard matching result, and the standard deviation, average residual and maximum residual after curve fitting are selected as the three characteristic data for each matching situation, and then the characteristic data are input into a preset internal standard matching scoring model to perform internal standard matching degree scoring.

Furthermore, the method also includes a training step of an internal standard matching scoring model, specifically including: using the standard deviation, average residual and maximum residual after curve fitting corresponding to the sample data under different preset internal standard matching conditions as sample feature data of the corresponding sample, setting the sample data of the correct internal standard matching result as the positive class, and setting the sample data of the incorrect internal standard matching result as the negative class to obtain a training data set; using Hinge loss as the loss function for model training, using Sigmoid function to normalize the classification results, and performing learning and training on the training data set based on a preset machine learning model to obtain a trained internal standard matching scoring model.

Specifically, the present invention calculates the above three feature data of the matching conditions of a large number of samples corresponding to different internal standards, sets the correct match as a positive class, and the incorrect match as a negative class to form a training data set, selects Hinge loss as the loss function for model training, and uses a machine learning model, such as a support vector machine, to train the training data set to obtain model parameters, and finally uses a sigmoid function to further process the results to obtain a trained internal standard matching scoring model.

In an optional embodiment of the present invention, the method further includes: if the maximum number of candidate peaks included in the matching results corresponding to each candidate peak combination is not equal to the number of standard peaks included in the standard peak sequence, updating the distance error threshold according to a preset first threshold adjustment rule, and returning to step S12 to re-perform internal standard matching until the updated distance error threshold is greater than the maximum value of the distance error threshold;

When the updated distance error threshold is greater than the maximum value of the distance error threshold, the relative height threshold is updated according to the preset second threshold adjustment rule, and the distance error threshold is updated to the corresponding initial value, and the process returns to step S12 to re-perform the internal standard matching until the updated relative height threshold is greater than the maximum value of the relative height threshold;

When the updated relative height threshold is greater than the maximum value of the relative height threshold, it is determined that the internal standard matching fails.

The embodiment of the present invention realizes dynamic matching of internal standards by dynamically adjusting the height threshold and the distance error threshold within a preset value range. Specifically, if the internal standard cannot be successfully matched under the initial height threshold and the distance error threshold, in order to avoid matching failure caused by inappropriate threshold selection, the present invention can dynamically adjust the two types of thresholds respectively, firstly dynamically increase the distance error threshold under the premise of keeping the relative height threshold unchanged, such as adjusting the preset unit step length each time, and re-performing internal standard matching under the current height threshold and the adjusted distance error threshold, until the internal standard is successfully matched or the updated distance error threshold is greater than the maximum value of the distance error threshold, when the updated distance error threshold is greater than the maximum value of the distance error threshold, then dynamically increase the height threshold, and under the adjusted height threshold state, re-start internal standard matching with the initial distance error threshold, until the internal standard is successfully matched or the updated relative height threshold is greater than the maximum value of the relative height threshold, and use the dynamic programming of the inner and outer double threshold cycles to match the identified internal standard peak with the standard internal standard length, which can further ensure the accuracy of internal standard matching.

In the embodiment of the present invention, data pre-processing is performed on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, specifically including the following steps:

S01. Using a preset local adaptive polynomial fitting algorithm to filter out background noise of the spectral signal.

Specifically, the data is first segmented, and then polynomial fitting is performed in the following manner:

initial: k=1, O ₀ (i)=O (i);

Step 1: Fit the signal with a polynomial of appropriate order (1, 2, 3, ...) to obtain P _k (i);

Step 2: Calculate the residual between the original signal and the fitted signal R _k (i) = O _k-1 (i) - _Pi (i);

Step 3: Calculate the standard deviation of the residual DEV _k ;

Step 4: If it is the first iteration, delete the signals that satisfy the condition O ₀ (i)＞P _k (i)+DEV _k (remove the corresponding subscript points); if it is not the first iteration, assign values according to the following rules:

if O _k+1 (i)＜P _k (i)+DEV _k , O _k (i)＝O _k-1 (i)

Otherwise, O _k (i) = P _k (i) + DEV _k

Step 5: Calculate whether |(DEV _k -DDV _k-1 )/DEV _k |＜gradient, where gradient＝0.01 is an adjustable parameter. If it is satisfied, proceed to the next step. If not, return to step 1 and repeat this process.

Step 6: Calculate the baseline P _k (i) using the polynomial coefficients, and output the signal S (i) = O ₀ (i) - P _k (i) after removing the baseline.

Wherein: K is the number of iterations; i is the spectral data point, i = 0, 1, 2, ..., N-1; O is the original signal; P is the polynomial fitting signal (baseline); R is the residual between the original signal and the fitting signal; DEV is the standard deviation; O _k (i) is the i-th data in the original spectral data at the k-th iteration; P _k (i) is the i-th data in the polynomial fitting data at the k-th iteration; R _k (i) is the residual between the original spectral data and the fitting data at the k-th iteration; DEV _k is the standard deviation at the k-th iteration; gradient stops the iteration threshold; S(i) is the i-th data after removing the baseline (the data used in the next step of processing).

S02. Use Savitzky Golay polynomial smoothing algorithm to smooth the spectral signal after filtering out the background noise.

Specifically, at each data point _Si , a window of length 2n+1 is used to perform polynomial fitting on the data in the window. The common polynomial order is p (p=1, 2, 3, ...). Finally, the value of the center point of the fitting polynomial is used to replace the original data point _Si , thereby completing the smoothing process.

Specifically, the formula for the Savitzky-Golay filter is:

Among them, Hi is the smoothed data point, _Si is the original data point, and _cj is the coefficient of the filter. The calculation of the filter coefficient involves the process of polynomial fitting, which can be solved by methods such as the least squares method.

Due to the problems of laser background, base Raman signal, spectrometer dark noise, etc., the signal baselines collected in different bands are inconsistent, which makes it impossible to directly determine the type and content of DNA by signal intensity. Therefore, the present invention processes the raw data by removing the baseline, smoothing, etc., which can ensure the accurate identification of candidate peaks.

In a specific embodiment of the present invention, a method for implementing internal standard matching of a spectrum detected by a genetic analyzer is shown in FIG3 , where each identified peak is represented as (pi, H _pi ), indicating that the signal intensity of the pi th data point in the spectrum data is H _pi , where p0<p1<p2<···<pi<···, and all identified peaks are called candidate peak sequences {(p0, H _p0 ), (p1, H _p1 ),···, (pi, H _pi ),···}.

The peaks corresponding to the standard internal standard length are called the standard peak sequence {s0, s1, s2, ···, si, ···}, s0<s1<s2<···<si<···.

1. Calculate the distance difference of all adjacent data points in the candidate peak sequence, denoted as C = {C ₀ , C ₁ , ···} = {p1-p0, p2-p1, ···};

2. Calculate the difference of all adjacent lengths in the standard peak sequence, denoted as S = {S ₀ , S ₁ , ···} = {s1-s0, s2–s1, ···};

3. Initialize relative height ralativeH;

4. Initialize the acceptable ratio error ratioD;

5. In the candidate peak sequence, find a preset number of candidate peaks that meet the internal matching conditions by matching the data points from small to large, and find all possible combinations; wherein the preset number may be 3;

6. Next, all combinations are matched completely. At this time, it may be that all detected peaks have been judged, but the internal standard is not completely matched;

7. Calculate the number of matched peaks in each combination and take the maximum value;

8. Determine whether this maximum value is equal to the number of internal standards;

9. If it is equal to the number of internal standards, the internal standard match is successful, and the combination with the largest data point corresponding to the maximum value and the first peak is output; if it is not equal, proceed to step 10

10. Update ratioD and determine whether the updated ratioD is less than matchTH. If so, return to step 5; otherwise, proceed to step 11.

11. The relative height ralativeH increases by 1, and it is determined whether the increased ralativeH exceeds the relative peak height threshold. If not, return to step 4 and repeat the matching process. Otherwise, the internal standard matching fails, the analysis process ends, and the matching score is equal to 0.

The present invention adopts a local adaptive fluorescence background noise removal algorithm, segments the data, removes the baseline and performs smoothing by polynomial fitting, and then screens the peak points by setting the threshold value corresponding to the feature of the processed data, leaving the candidate peak points that meet the requirements. Through the two matching conditions of distance similarity and height uniformity, dynamic programming is used to match the identified internal standard peak with the standard internal standard length. In the case of successful internal standard matching, the standard peak sequence and the matched sequence are curve fitted, and the standard deviation, average residual and maximum residual after curve fitting are selected as the three characteristic data of each matching situation, and then the characteristic data is input into a preset internal standard matching scoring model to score the internal standard matching degree, which can not only accurately realize the internal standard matching, but also intelligently score the internal standard matching degree, and improve the intelligence and accuracy of the electrophoresis spectrum data analysis process.

For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

In addition, an embodiment of the present invention further provides a system for realizing normalization of detection signals of a gene analyzer. The system includes a functional module for realizing the method for realizing normalization of detection signals of a gene analyzer as described above. As shown in FIG4 , the system for realizing normalization of detection signals of a gene analyzer according to an embodiment of the present invention includes an internal standard matching unit 40, a calculation unit 50 and a normalization unit 60, wherein:

The internal standard matching unit 40 is used to perform internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, and calculate the single channel internal standard average value of each capillary internal standard channel according to the successfully matched internal standard matching sequence;

A calculation unit 50 is used to divide the preset internal standard value by the single channel internal standard average value of the internal standard channel of each capillary, and use the obtained calculation result as the normalization coefficient of the corresponding capillary;

The normalization unit 60 is used to perform normalization correction on the spectral signals of other color channels in the respective capillaries according to the normalization coefficient of each capillary, so as to realize the normalization of the detection signal.

In the embodiment of the present invention, the internal standard matching unit is further used to perform electrophoresis on a plurality of capillaries containing only an internal standard of a specified concentration before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis of the gene analyzer, so as to collect the spectral signal of the internal standard channel of each capillary and perform internal standard matching, calculate the average peak value of each internal standard peak in the internal standard matching sequence corresponding to each capillary that meets the internal standard matching requirements, and obtain the single-channel internal standard average value of the internal standard channel of each capillary;

The calculation unit is also used to calculate the coefficient of variation of the single-channel internal standard average value of each capillary internal standard channel. If the coefficient of variation is less than a preset variation threshold, the mean of the single-channel internal standard average values of each capillary internal standard channel or a preset multiple of the mean is used as the internal standard standard value.

In an embodiment of the present invention, the system further includes a judgment unit and a reset unit. The judgment unit is used to judge whether the normalization coefficient of each capillary is compliant after the calculation unit uses the obtained calculation result as the normalization coefficient of the corresponding capillary, and when the normalization coefficient of the capillary is within the preset normalization coefficient value range, the normalization coefficient of the current capillary is judged to be compliant, otherwise, the normalization coefficient of the current capillary is judged to be non-compliant;

The reset unit is further used to correct the internal standard value if the ratio of the number of capillaries with non-compliant normalization coefficients in the current electrophoresis detection data to the total number of capillaries is greater than a preset first ratio threshold, or if the ratio of the number of capillaries with non-compliant normalization coefficients in the electrophoresis detection data for at least three consecutive times to the total number of capillaries is greater than a preset second ratio threshold;

The second ratio threshold is smaller than the first ratio threshold.

Furthermore, the normalization unit 60 is specifically used to multiply the spectral signals of other color channels in the current capillary by the normalization coefficient of the corresponding capillary to achieve normalization correction of the spectral signal if the normalization coefficient of the capillary is compliant; if the normalization coefficient of the capillary is not compliant, multiply the spectral signals of other color channels in the current capillary by the normalization coefficient of the corresponding capillary close to the extreme value within the normalization coefficient value range to achieve normalization correction of the spectral signal.

In an embodiment of the present invention, the system further includes an optical alignment unit, which is used to monitor the Raman signal during the electrophoresis process and obtain the signal value, peak balance data and/or uniformity data of the Raman signal before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the genetic analyzer; when any parameter among the signal value, peak balance data and/or uniformity data of the Raman signal does not meet the corresponding preset optical parameter standard, perform an optical alignment operation until the signal value, peak balance data and/or uniformity data all meet the corresponding optical parameter standard.

In the embodiment of the present invention, the internal standard matching unit 40 includes a data pre-processing module 400, a peak identification module 401, an initial matching module 402, an internal standard matching module 403, a statistical module 404 and a determination module 405, which are not shown in the drawings, wherein:

The data pre-processing module 400 is used to perform data pre-processing on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, so as to filter out the background noise of the spectral signal and smooth the spectral signal;

The peak identification module 401 performs peak identification on the spectral signal after data pre-processing to screen out candidate peak sequences contained in the current spectral signal;

The initial matching module 402 performs internal standard matching on the candidate peaks in the candidate peak sequence and the standard peak sequence in the standard spectral signal corresponding to the internal standard selected in the electrophoresis process in sequence according to the sampling order, so as to find a candidate peak combination in the candidate peak sequence that meets the preset internal standard matching conditions, and the number of candidate peaks in the candidate peak combination is greater than or equal to 3; the internal standard matching conditions include that the absolute value of the difference between the first distance and the second distance is less than the preset distance error threshold, and max{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}/min{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}<preset relative height threshold, wherein the first distance is the distance between the peak points of adjacent candidate peaks in the candidate peak sequence, and the second distance is the distance between the peak points of adjacent standard peaks in the standard peak sequence that have the same sampling order as the candidate peak for calculating the first distance currently;

The internal standard matching module 403 performs internal standard matching on other candidate peaks in the candidate peak sequence in sequence with each candidate peak combination as a matching basis to obtain a matching result corresponding to each candidate peak combination;

A statistics module 404 is used to count the number of candidate peaks included in the matching results corresponding to each candidate peak combination;

The determination module 405 determines that the internal standard match is successful when the maximum number of candidate peaks included in the matching results corresponding to each candidate peak combination is equal to the number of standard peaks included in the standard peak sequence, and uses the matching result corresponding to the maximum value as the optimal internal standard matching result.

The internal standard matching unit 40 provided in the embodiment of the present invention further includes an intelligent scoring module not shown in the accompanying drawings, wherein the intelligent scoring module is used to perform curve fitting on the standard peak sequence and the peak sequence of the optimal internal standard matching result after taking the matching result corresponding to the maximum value as the optimal internal standard matching result, and taking the standard deviation, average residual and maximum residual after the curve fitting as the characteristic data of the current optimal internal standard matching result; and inputting the characteristic data into a preset internal standard matching scoring model for identification to obtain a matching degree score of the current optimal internal standard matching result.

Specifically, when the internal standard match is successful, the present invention can use a high-order polynomial fitting method to realize curve fitting of the standard peak sequence and the peak sequence of the internal standard matching result, and select the standard deviation, average residual and maximum residual after curve fitting as the three characteristic data of each matching situation, and then input the characteristic data into a preset internal standard matching scoring model to score the degree of internal standard matching.

Furthermore, the internal standard matching unit 40 provided in the embodiment of the present invention also includes a model training module not shown in the accompanying drawings, which is used to perform training operations of the internal standard matching scoring model, specifically including: using the standard deviation, average residual and maximum residual after curve fitting corresponding to the sample data under different preset internal standard matching conditions as sample feature data of the corresponding sample, setting the sample data of the correct internal standard matching result as the positive class, and setting the sample data of the incorrect internal standard matching result as the negative class, to obtain a training data set; using Hinge loss as the loss function for model training, using Sigmoid function to normalize the classification results, and learning and training the training data set based on a preset machine learning model to obtain a trained internal standard matching scoring model.

Specifically, the present invention calculates the above three feature data of the matching conditions of a large number of samples corresponding to different internal standards, sets the correct match as a positive class, and the incorrect match as a negative class to form a training data set, selects Hinge loss as the loss function for model training, and uses a machine learning model, such as a support vector machine, to train the training data set to obtain model parameters, and finally uses a sigmoid function to further process the results to obtain a trained internal standard matching scoring model.

The internal standard matching unit 40 provided in the embodiment of the present invention further includes a threshold dynamic adjustment module not shown in the drawings, which is used to update the distance error threshold according to a preset first threshold adjustment rule when the maximum number of candidate peaks contained in the matching results corresponding to each candidate peak combination is not equal to the number of standard peaks contained in the standard peak sequence, and return to the initial matching module 402 to perform corresponding operations until the distance error threshold updated by the threshold dynamic adjustment module is greater than the maximum value of the distance error threshold;

The threshold dynamic adjustment module is further used to update the relative height threshold according to a preset second threshold adjustment rule when the updated distance error threshold is greater than the maximum value of the distance error threshold, and update the distance error threshold to the corresponding initial value, and return to the initial matching module 402 to perform corresponding operations until the relative height threshold updated by the threshold dynamic adjustment module is greater than the maximum value of the relative height threshold;

The determination module 405 is further configured to determine that the internal standard matching fails when the updated relative height threshold is greater than the maximum value of the relative height threshold.

As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment, and it has the corresponding technical effects.

In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for normalizing detection signals of a genetic analyzer are implemented as described above.

In this embodiment, if the method for realizing the normalization of the detection signal of the gene analyzer is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for normalizing the detection signal of the gene analyzer as described above when executing the computer program. For example, steps S1 to S3 shown in FIG1 . Alternatively, when the processor executes the computer program, the functions of each module/unit in the above-mentioned system embodiment for normalizing the detection signal of the gene analyzer are implemented, such as the internal standard matching unit 40, the calculation unit 50, and the normalization unit 60 shown in FIG4 .

The method, system and device for realizing normalization of detection signals of a gene analyzer provided by the embodiments of the present invention calculate the normalization coefficient of the corresponding capillary through the preset internal standard standard value and the single-channel internal standard average value of the internal standard channel of each capillary in the electrophoresis process of the gene analyzer, and perform normalization correction on the spectral signals of other color channels in each capillary according to the normalization coefficient of each capillary, so as to adjust the peak height of the sample to be tested according to the peak height of the internal standard, improve the signal difference between different capillaries, and the difference of different detection times of the same capillary, and effectively improve the quality of gene analysis results.

In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, any one of the claimed embodiments may be used in any combination.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for realizing normalization of detection signals of a gene analyzer, characterized in that the method comprises: Performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, and calculating the single-channel internal standard average value of each capillary internal standard channel according to the successfully matched internal standard matching sequence; The preset internal standard value is divided by the single channel internal standard average value of the internal standard channel of each capillary, and the calculated result is used as the normalization coefficient of the corresponding capillary; According to the normalization coefficient of each capillary, the spectral signals of other color channels in each capillary are normalized and corrected respectively to achieve normalization of the detection signal. 2. The method according to claim 1, characterized in that before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, the method further comprises: Performing electrophoresis on a multi-channel capillary filled only with an internal standard of a specified concentration to collect the spectral signal of the internal standard channel of each capillary and perform internal standard matching, calculating the average peak value of each internal standard peak in the internal standard matching sequence corresponding to each capillary that meets the internal standard matching requirements, and obtaining the single-channel internal standard average value of each capillary internal standard channel; The coefficient of variation of the single-channel internal standard average value of each capillary internal standard channel is calculated. If the coefficient of variation is less than a preset variation threshold, the mean of the single-channel internal standard average values of each capillary internal standard channel or a preset multiple of the mean is used as the internal standard standard value. 3. The method according to claim 1, characterized in that after using the obtained calculation result as the normalization coefficient of the corresponding capillary, the method further comprises: Determine whether the normalization coefficient of each capillary is compliant. When the normalization coefficient of the capillary is within the preset normalization coefficient value range, the normalization coefficient of the current capillary is determined to be compliant. Otherwise, the normalization coefficient of the current capillary is determined to be non-compliant. If the ratio of the number of capillaries with non-compliant normalization coefficients in the current electrophoresis detection data to the total number of capillaries is greater than a preset first ratio threshold, or if the ratio of the number of capillaries with non-compliant normalization coefficients in the electrophoresis detection data for at least three consecutive times to the total number of capillaries is greater than a preset second ratio threshold, the internal standard value is corrected; The second ratio threshold is smaller than the first ratio threshold. 4. The method according to claim 3, characterized in that the normalization correction of the spectral signals of other color channels in each capillary according to the normalization coefficient of each capillary comprises: If the normalization coefficient of the capillary is in compliance, the spectral signals of other color channels in the current capillary are multiplied by the normalization coefficient of the corresponding capillary to achieve spectral signal normalization correction; If the normalization coefficient of the capillary is not compliant, the spectral signals of other color channels in the current capillary are multiplied by the normalization coefficient of the corresponding capillary close to the extreme value within the normalization coefficient value range to achieve spectral signal normalization correction. 5. The method according to claim 1, characterized in that before performing internal standard matching on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer, the method further comprises: Monitoring the Raman signal during the electrophoresis process, and obtaining the signal value, peak balance data and/or uniformity data of the Raman signal; When any parameter of the signal value, peak balance data and/or uniformity data of the Raman signal does not meet the corresponding preset optical parameter standard, an optical alignment operation is performed until the signal value, peak balance data and/or uniformity data all meet the corresponding optical parameter standard. 6. The method according to any one of claims 1 to 5, characterized in that the internal standard matching of the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer comprises: S10, performing data pre-processing on the spectral signal of the internal standard channel of each capillary collected during the electrophoresis process of the gene analyzer to filter out the background noise of the spectral signal and perform smoothing on the spectral signal; S11, performing peak recognition on the spectral signal after data pre-processing to screen out candidate peak sequences contained in the current spectral signal; S12. Perform internal standard matching on the candidate peaks in the candidate peak sequence and the standard peak sequence in the standard spectral signal corresponding to the internal standard selected in the electrophoresis process in sequence according to the sampling order, so as to find the candidate peak combination in the candidate peak sequence that meets the preset internal standard matching conditions, and the number of candidate peaks in the candidate peak combination is greater than or equal to 3; the internal standard matching conditions include that the absolute value of the difference between the first distance and the second distance is less than the preset distance error threshold, and max{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}/min{peak height of the selected candidate peak in the candidate peak combination, peak height of the current candidate peak to be matched}<preset relative height threshold, wherein the first distance is the distance between the peak points of adjacent candidate peaks in the candidate peak sequence, and the second distance is the distance between the peak points of adjacent standard peaks in the standard peak sequence that have the same sampling order as the candidate peak for calculating the first distance currently; S13, using each candidate peak combination as a matching basis, sequentially performing internal standard matching on other candidate peaks in the candidate peak sequence to obtain a matching result corresponding to each candidate peak combination; S14, counting the number of candidate peaks included in the matching results corresponding to each candidate peak combination; S15. When the maximum number of candidate peaks contained in the matching results corresponding to each candidate peak combination is equal to the number of standard peaks contained in the standard peak sequence, the internal standard matching is determined to be successful, and the matching result corresponding to the maximum value is used as the optimal internal standard matching result. 7. The method according to claim 6, characterized in that after taking the matching result corresponding to the maximum value as the optimal internal standard matching result, the method comprises: Perform curve fitting on the standard peak sequence and the peak sequence of the optimal internal standard matching result, and use the standard deviation, average residual and maximum residual after the curve fitting as characteristic data of the current optimal internal standard matching result; The characteristic data is input into a preset internal standard matching scoring model for identification to obtain a matching degree score of the current optimal internal standard matching result. 8. The method according to claim 6, characterized in that the method further comprises: If the maximum number of candidate peaks included in the matching results corresponding to each candidate peak combination is not equal to the number of standard peaks included in the standard peak sequence, the distance error threshold is updated according to the preset first threshold adjustment rule, and the process returns to step S12 until the updated distance error threshold is greater than the maximum value of the distance error threshold; When the updated distance error threshold is greater than the maximum value of the distance error threshold, the relative height threshold is updated according to the preset second threshold adjustment rule, and the distance error threshold is updated to the corresponding initial value, and the process returns to step S12 until the updated relative height threshold is greater than the maximum value of the relative height threshold; When the updated relative height threshold is greater than the maximum value of the relative height threshold, it is determined that the internal standard matching fails. 9. A system for realizing normalization of detection signals of a gene analyzer, characterized in that the system comprises a functional module for realizing the method according to any one of claims 1 to 8. 10. A computer device, characterized in that it comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.