A method, device, apparatus and readable storage medium for fluorescent quantitative detection

Abstract

The application provides a fluorescence quantitative detection method, device, equipment and readable storage medium. The method comprises the following steps: removing abnormal signals in a plurality of initial to-be-detected signal sets by a preset variance denoising model to obtain a plurality of to-be-detected signal sets, wherein the abnormal signals represent non-target signals and outlier signals collected when collecting signals; and detecting and analyzing the plurality of to-be-detected signal sets by a preset differential linear fitting model to obtain a detection result. The method can improve the accuracy of fluorescence quantitative detection.

Description

Technical Field

[0001] This application relates to the field of quantitative fluorescence detection, and more specifically, to a method, apparatus, device, and readable storage medium for quantitative fluorescence detection. Background Technology

[0002] Currently, real-time fluorescence quantitative detection algorithms are widely used in biological and medical research applications due to their advantages such as high sensitivity, high accuracy and high efficiency. The mainstream raw data processing algorithms are divided into two types: threshold method and maximum second derivative method. Fluorescence quantitative detection is achieved through the above methods.

[0003] However, due to noise interference in the initial stage of the current quantitative fluorescence reaction, the background signal fluctuates to a certain extent. The above algorithm does not solve the problem of background signal fluctuation and noise interference in the fluorescence signal in the early stage of the exponential amplification process very well, thus the results of quantitative fluorescence detection are not very accurate.

[0004] Therefore, improving the accuracy of quantitative fluorescence detection is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a method for quantitative fluorescence detection. The technical solution of this application can improve the accuracy of quantitative fluorescence detection.

[0006] In a first aspect, embodiments of this application provide a method for quantitative fluorescence detection, comprising: removing abnormal signals from multiple initial sets of signals to be detected by using a preset variance denoising model to obtain multiple sets of signals to be detected, wherein the abnormal signals represent non-target signals collected during signal collection; and performing detection analysis on the multiple sets of signals to be detected by using a preset differential linear fitting model to obtain detection results.

[0007] In the above embodiments, by eliminating abnormal signals, the differential linear fitting model can avoid the interference of abnormal signals when performing interpretation and analysis, and directly perform quantitative fluorescence detection on the detection index. This method can improve the accuracy of quantitative fluorescence detection.

[0008] In some embodiments, abnormal signals are removed from multiple initial sets of signals to be detected using a preset variance denoising model, including:

[0009] The variance of each set in the multiple initial sets of signals to be detected is calculated using a variance denoising model. The variance of each set in the multiple initial sets of signals to be detected is obtained by the following formula:

[0010]

[0011]

[0012] Where m represents the number of signals in the initial set of signals to be detected, ni represents the i-th signal point in the n-th initial set of signals to be detected, and y ni Y represents the signal value of the i-th signal within the n-th initial set of signals to be detected. n V represents the mean value of the signals in the nth initial set of signals to be detected. n This represents the mean variance of the nth initial set of signals to be detected;

[0013] Abnormal signals in multiple initial sets of signals to be detected are eliminated based on the variance of each set.

[0014] In the above embodiments, the variance denoising model can accurately calculate abnormal signals through the above algorithm, thereby eliminating the influence of abnormal signals on quantitative fluorescence detection.

[0015] In some embodiments, abnormal signals in the plurality of initial sets of signals to be detected are eliminated based on the variance of each set in the plurality of initial sets of signals to be detected, including:

[0016] The rejection threshold is determined based on the variance of each set and the mean of the signal values ​​in each set of multiple initial sets of signals to be detected. The rejection threshold is determined by the following formula:

[0017] T n =F(V n Y n );

[0018] Among them, T n V is the threshold for removing abnormal signals. n Y represents the mean variance of the nth initial set of signals to be detected. n This represents the mean value of the signals in the nth initial set of signals to be detected.

[0019] Signals in each set whose signal values ​​are greater than the rejection threshold are rejected.

[0020] In the above embodiments, this application determines a rejection threshold based on the influence of variance and initial value, which can reject signals that are greater than the rejection threshold, and can accurately reject abnormal signals.

[0021] In some embodiments, a preset differential linear fitting model is used to detect and analyze multiple sets of signals to be detected, and detection results are obtained, including:

[0022] The set of multiple signals to be detected is normalized to obtain multiple normalized signals;

[0023] The detection results are obtained by jointly analyzing multiple signals using a differential linear fitting model.

[0024] In the above embodiments, this application uses normalization processing to make each set of signals to be detected correspond to a normalized signal, which can make the signals simpler and easier to analyze.

[0025] In some embodiments, a pre-defined differential linear fitting model is used to perform positive detection on a set of multiple signals to be detected, and the detection results are obtained, including:

[0026] The slope of the curve corresponding to each of the normalized signals is calculated using a difference linear fitting model. The slope of the curve corresponding to each of the normalized signals is calculated using the following formula:

[0027]

[0028] Among them, S i Represents the i-th normalized signal Y′ i The slope obtained by fitting the data to k consecutive data points, where k represents the slope obtained by normalizing the signal with k data points, Y′ i This represents the normalized value of the i-th signal. Indicates from Y′ i To Y′ i+k Signal average value, This represents the average value from signal i to signal i+k;

[0029] The slopes of multiple curves corresponding to multiple normalized signals are analyzed according to preset rules to obtain detection results.

[0030] In the above embodiments, the slope corresponding to each signal to be detected can be accurately calculated using the above algorithm. The slope can accurately describe the curve change of the fitted curve and analyze the detection result of the signal to be detected.

[0031] In some embodiments, the slopes of multiple curves corresponding to multiple normalized signals are analyzed according to preset rules to obtain detection results, including:

[0032] The slopes of multiple curves corresponding to multiple normalized signals are subtracted to obtain multiple difference values. These difference values ​​are obtained using the following formula:

[0033] ΔS i+1 =S i+1 -Si ;

[0034] Among them, S i S is the slope of the curve corresponding to the i-th signal. i+1 Let ΔS be the slope of the curve corresponding to the (i+1)th signal. i+1 The slopes of the curves corresponding to the (i+1)th signal and the ith signal are respectively.

[0035] When there are at least e consecutive difference values ​​greater than a preset threshold among multiple difference values, the detection result is positive, where e is a positive integer greater than or equal to 2;

[0036] The detection result is negative when there are no at least e consecutive difference values ​​greater than the preset threshold among multiple difference values.

[0037] In the above embodiments, if there are e consecutive difference values ​​greater than a preset threshold, the result can be determined as positive; if there are no e consecutive difference values ​​greater than the preset threshold, the result can be determined as negative, thus achieving an accurate judgment effect.

[0038] In some embodiments, before removing abnormal signals from multiple initial sets of signals to be detected using a preset variance denoising model, the method further includes:

[0039] The initial detection signal of the target object is continuously acquired, and the initial detection signal is clustered into m signals to obtain multiple sets of initial detection signals, where m is a positive integer greater than or equal to 2.

[0040] In the above embodiments, continuous signal acquisition can avoid signal omission, and clustered signals can be processed together to reduce interference from other signal sources.

[0041] In some embodiments, a preset differential linear fitting model is used to detect and analyze multiple sets of signals to be detected, and detection results are obtained, including:

[0042] The slopes of multiple normalized signals are differentially approximated using a differential linear fitting model to obtain the differential results.

[0043] The positive and negative results are analyzed based on the difference results to obtain the detection results.

[0044] In the above embodiments, this application can accurately determine the positive or negative nature of the target by using the difference of the difference linear fitting model.

[0045] Secondly, embodiments of this application provide a device for quantitative fluorescence detection, comprising:

[0046] The elimination module is used to eliminate abnormal signals from multiple initial sets of signals to be detected using a preset variance noise reduction model, thereby obtaining multiple sets of signals to be detected. The abnormal signals refer to non-target signals collected during signal collection.

[0047] The detection module is used to perform positive detection on multiple sets of signals to be detected using a preset differential linear fitting model, and obtain the detection results.

[0048] Optionally, the rejection module is specifically used for:

[0049] The variance of each set in the multiple initial sets of signals to be detected is calculated using a variance denoising model. The variance of each set in the multiple initial sets of signals to be detected is obtained by the following formula:

[0050]

[0051]

[0052] Where m represents the number of signals in the initial set of signals to be detected, ni represents the i-th signal point in the n-th initial set of signals to be detected, and y ni Y represents the signal value of the i-th signal within the n-th initial set of signals to be detected. n V represents the mean value of the signals in the nth initial set of signals to be detected. n This represents the mean variance of the nth initial set of signals to be detected;

[0053] Abnormal signals in multiple initial sets of signals to be detected are eliminated based on the variance of each set.

[0054] Optionally, the rejection module is specifically used for:

[0055] The rejection threshold is determined based on the variance of each set and the mean of the signal values ​​in each set of multiple initial sets of signals to be detected. The rejection threshold is determined by the following formula:

[0056] T n =F(V n Y n );

[0057] Among them, T n V is the threshold for removing abnormal signals. n Y represents the mean variance of the nth initial set of signals to be detected. n This represents the mean value of the signals in the nth initial set of signals to be detected.

[0058] Signals in each set whose signal values ​​are greater than the rejection threshold are rejected.

[0059] Optionally, the detection module is specifically used for:

[0060] The set of multiple signals to be detected is normalized to obtain multiple normalized signals;

[0061] The detection results are obtained by jointly analyzing multiple signals using a differential linear fitting model.

[0062] Optionally, the detection module is specifically used for:

[0063] The slope of the curve corresponding to each of the normalized signals is calculated using a difference linear fitting model. The slope of the curve corresponding to each of the normalized signals is calculated using the following formula:

[0064]

[0065] Among them, S i Represents the i-th normalized signal Y′ i The slope obtained by fitting the data to k consecutive data points, where k represents the slope obtained by normalizing the signal with k data points, Y′ i This represents the normalized value of the i-th signal. Indicates from Y′ i To Y′ i+k Signal average value, This represents the average value from signal i to signal i+k;

[0066] The slopes of multiple curves corresponding to multiple normalized signals are analyzed according to preset rules to obtain detection results.

[0067] Optionally, the detection module is specifically used for:

[0068] The slopes of multiple curves corresponding to multiple normalized signals are subtracted to obtain multiple difference values. These difference values ​​are obtained using the following formula:

[0069] ΔS i+1 =S i+1 -S i ;

[0070] Among them, S i S is the slope of the curve corresponding to the i-th signal. i+1 Let ΔS be the slope of the curve corresponding to the (i+1)th signal. i+1 The slopes of the curves corresponding to the (i+1)th signal and the ith signal are respectively.

[0071] When there are at least e consecutive difference values ​​greater than a preset threshold among multiple difference values, the detection result is positive, where e is a positive integer greater than or equal to 2;

[0072] The detection result is negative when there are no at least e consecutive difference values ​​greater than the preset threshold among multiple difference values.

[0073] Optionally, the device also includes:

[0074] The clustering module is used to continuously collect the initial detection signals of the target object before the elimination module removes abnormal signals from multiple initial detection signal sets through a preset variance denoising model. The initial detection signals are then clustered into m signals to obtain multiple initial detection signal sets, where m is a positive integer greater than or equal to 2.

[0075] Optionally, the detection module is specifically used for:

[0076] The slopes of multiple normalized signals are differentially approximated using a differential linear fitting model to obtain the differential results.

[0077] The positive and negative results are analyzed based on the difference results to obtain the detection results.

[0078] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the method provided in the first aspect above are performed.

[0079] Fourthly, embodiments of this application provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0080] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0081] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1A flowchart of a fluorescence quantitative detection method provided in this application embodiment;

[0083] Figure 2 A flowchart illustrating an implementation method for quantitative fluorescence detection provided in this application embodiment;

[0084] Figure 3 A schematic block diagram of a fluorescence quantitative detection device provided in an embodiment of this application;

[0085] Figure 4 This is a schematic diagram of a fluorescence quantitative detection device provided in an embodiment of this application. Detailed Implementation

[0086] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0087] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0088] First, some of the terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

[0089] RT-PCR: Quantitative Real-time PCR is a method that uses fluorescent chemicals to measure the total amount of product after each polymerase chain reaction (PCR) cycle in a DNA amplification reaction. It is a method for quantitative analysis of specific DNA sequences in a sample using internal or external controls.

[0090] CT: Also known as cycle threshold, where C stands for Cycle and t stands for threshold. It refers to the number of cycles required for the fluorescence signal to transition from background to exponential growth during PCR cycling.

[0091] CQ (Crossing point) is the number of PCR cycles at which the sample reaction curve intersects the threshold line. This value indicates the number of cycles required to detect the true jump signal from the background.

[0092] This application is applied to the scenario of quantitative fluorescence detection. Specifically, the detection signal of the target is collected, clustered and then noise is reduced, and finally the detection is completed by quantitative fluorescence detection. This application can be used for the detection of various molecular substances, such as nucleic acid detection.

[0093] However, currently, real-time fluorescence quantitative detection algorithms are widely used in biological and medical research applications due to their advantages such as high sensitivity, high accuracy, and high efficiency. The mainstream raw data processing algorithms are divided into two types: thresholding and maximum second derivative methods, which are used to achieve fluorescence quantitative detection. However, due to noise interference in the initial stage of the fluorescence quantitative reaction, the background signal fluctuates to some extent. The aforementioned algorithms do not effectively address background signal fluctuations or noise interference in the fluorescence signal during the early stages of exponential amplification, resulting in inaccurate fluorescence quantitative detection results.

[0094] To address this, this application employs a pre-defined variance denoising model to remove outlier signals from multiple initial sets of signals to be detected, resulting in multiple sets of signals to be detected. Outlier signals refer to non-target sample signals collected during signal acquisition, including outliers. A pre-defined differential linear fitting model is then used to calculate the detection results for these multiple sets of signals. By removing outlier signals, the differential linear fitting model can avoid their influence during detection analysis, allowing direct quantitative fluorescence detection of the target signal. This method improves the accuracy of quantitative fluorescence detection.

[0095] In this embodiment of the application, the executing entity can be the fluorescence quantitative detection device in the fluorescence quantitative detection system. In practical applications, the fluorescence quantitative detection device can be electronic devices such as terminal devices and servers, and there are no restrictions here.

[0096] The following is combined Figure 1 The fluorescence quantitative detection method of the embodiments of this application will be described in detail.

[0097] Please refer to Figure 1 , Figure 1 A flowchart of a fluorescence quantitative detection method provided in this application embodiment is shown below. Figure 1 The methods for quantitative fluorescence detection shown include:

[0098] Step 110: Remove abnormal signals from multiple initial sets of signals to be detected using a preset variance denoising model to obtain multiple sets of signals to be detected.

[0099] Abnormal signals include non-target signals and outlier signals. Outlier signals can be understood as signals that are far from most signals during clustering, while non-target signals can be understood as other substances or background objects. Before removing abnormal signals from multiple initial sets of signals to be detected using a pre-set variance denoising model, the process includes: training the variance denoising model, which is trained using historical quantitative fluorescence detection signals and corresponding detection results. Multiple initial sets of signals to be detected are signals collected from one or more target objects. The same initial set of signals to be detected is a set of signals of the same type collected from one target object, while multiple initial sets of signals to be detected are sets of different signals collected from multiple target objects (which can be different amplification products of the same sample). During signal processing, multiple signals can be processed simultaneously, and variance denoising and differential linear fitting can be performed on them to generate detection results corresponding to multiple signals.

[0100] In some embodiments of this application, before removing abnormal signals from multiple initial sets of signals to be detected using a preset variance denoising model, the method further includes: continuously acquiring initial signals to be detected from the target object to obtain a signal set; and dividing the signals in the signal set into clusters of m signals to obtain multiple sets of initial signals to be detected, where m is a positive integer greater than or equal to 2.

[0101] In the above embodiments, continuous signal acquisition can avoid signal omission, and clustered signals can be processed together to reduce interference from other signal sources.

[0102] The continuous acquisition of the target analyte's signal can involve collecting more data at shorter intervals, such as 1 second intervals, resulting in a total sampling volume of approximately 1800 signal values. The target analyte can be one or more, including viruses or bacteria for nucleic acid detection. An initial target signal set can be obtained based on a fixed length m, for example, clustering 50 data points into a set of 50, or clustering 100 data points into a set, or any other number; this application is not limited to these.

[0103] In some embodiments of this application, abnormal signals are removed from multiple initial sets of signals to be detected using a preset variance denoising model. This includes: calculating the variance of each set in the multiple initial sets of signals to be detected using the variance denoising model, wherein the variance of each set in the multiple initial sets of signals to be detected is obtained by the following formula:

[0104]

[0105]

[0106] Where m represents the number of signals in the initial set of signals to be detected, ni represents the i-th signal point in the n-th initial set of signals to be detected, and y ni Y represents the signal value of the i-th signal within the n-th initial set of signals to be detected. n V represents the mean value of the signals in the nth initial set of signals to be detected. n This represents the mean variance of the nth initial set of signals to be detected; abnormal signals in the multiple initial sets of signals to be detected are eliminated based on the variance of each set.

[0107] In the above process, the variance denoising model can accurately calculate abnormal signals through the above algorithm, thereby eliminating the influence of abnormal signals on quantitative fluorescence detection.

[0108] The above algorithm can be pre-embedded in a variance denoising model, which can then directly remove abnormal signals through the calculations of the algorithm.

[0109] In some embodiments of this application, abnormal signals in multiple initial sets of signals to be detected are eliminated based on the variance of each set in the multiple initial sets of signals to be detected. This includes: determining a elimination threshold based on the variance of each set in the multiple initial sets of signals to be detected and the mean of the signal values ​​in each set. The elimination threshold is determined by the following formula:

[0110] T n =F(V n Y n );

[0111] Among them, T n V is the threshold for removing abnormal signals. n Y represents the mean variance of the nth initial set of signals to be detected. n This represents the mean value of the signals in the nth initial set of signals to be detected; signals with a value greater than the elimination threshold in each set are eliminated.

[0112] In the above embodiments, this application determines a rejection threshold based on the influence of variance and initial value, which can reject signals that are greater than the rejection threshold, and can accurately reject abnormal signals.

[0113] The determination of the elimination threshold, along with the variance of each set and the mean of the signal values ​​in each set, can be expressed as a function, for example, T. n =F(V n ,Y n ), where T n The threshold for rejection.

[0114] In some embodiments of this application, a detection result is obtained by detecting and analyzing multiple sets of signals to be detected through a preset differential linear fitting model, including: normalizing multiple sets of signals to be detected to obtain multiple normalized signals, wherein each set of signals to be detected corresponds to a normalized signal; and jointly analyzing multiple signals through a differential linear fitting model to obtain a detection result.

[0115] In the above embodiments, normalization processing in this application can make the signal simpler and easier to analyze.

[0116] Step 120: Detect and analyze multiple sets of signals to be detected using a preset differential linear fitting model to obtain detection results.

[0117] In some embodiments of this application, the slope of the curve corresponding to each of the normalized signals is calculated using a differential linear fitting model. The slope of the curve corresponding to each of the normalized signals is calculated using the following formula:

[0118]

[0119] Among them, S i Represents the i-th normalized signal Y′ i The slope obtained by fitting the data to k consecutive data points, where k represents the slope obtained by normalizing the signal with k data points, Y′ i This represents the normalized value of the i-th signal. Indicates from Y′ i To Y′ i+k Signal average value, This represents the average value from signal i to signal i+k; based on preset rules, the slopes of multiple curves corresponding to multiple normalized signals are analyzed to obtain the detection results.

[0120] In the above embodiments, the slope corresponding to each signal to be detected can be accurately calculated using the above algorithm, and the detection result of the signal to be detected can be accurately analyzed using the slope.

[0121] Each signal can be calculated to have a slope. The above algorithm can be embedded into a differential linear fitting model, which can then calculate the curve slope using the algorithm, and accurately determine the detection result based on the slope. Furthermore, the differential linear fitting model can determine the current CT and CQ values, avoiding errors caused by subjective determination.

[0122] In some embodiments of this application, the slopes of multiple curves corresponding to multiple normalized signals are analyzed according to preset rules to obtain detection results, including:

[0123] The slopes of multiple curves corresponding to multiple normalized signals are subtracted to obtain multiple difference values. These difference values ​​are obtained using the following formula:

[0124] ΔS i+1 =S i+1 -S i ;

[0125] Among them, S i S is the slope of the curve corresponding to the i-th signal. i+1 Let ΔS be the slope of the curve corresponding to the (i+1)th signal. i+1 Let be the slope of the curve corresponding to the (i+1)th signal and the slope of the curve corresponding to the ith signal; when there are at least e consecutive difference values ​​greater than the preset threshold among multiple difference values, the detection result is positive, where e is a positive integer greater than or equal to 2; when there are no at least e consecutive difference values ​​greater than the preset threshold among multiple difference values, the detection result is negative.

[0126] In the above embodiments, if there are e consecutive difference values ​​greater than a preset threshold, the result can be determined as positive; if there are no e consecutive difference values ​​greater than the preset threshold, the result can be determined as negative, thus achieving an accurate judgment effect.

[0127] A positive result must meet at least two conditions: at least e difference values ​​are greater than a preset threshold, and at least e consecutive difference values ​​are greater than the preset threshold. The preset threshold can be set by the user.

[0128] In some embodiments of this application, positive detection is performed on multiple normalized signals using a preset differential linear fitting model to obtain detection results, including: differentiating the slopes corresponding to multiple sets of signals to be detected using the differential linear fitting model to obtain differential results; and performing positive / negative analysis based on the differential results to obtain detection results.

[0129] In the above embodiments, this application can accurately determine the positive or negative detection results of the target by using the difference of the differential linear fitting model.

[0130] In the above Figure 1In the process shown, this application uses a preset variance denoising model to remove abnormal signals from multiple initial sets of signals to be detected, resulting in multiple sets of signals to be detected. The abnormal signals include outliers and non-target signals. A preset differential linear fitting model is then used to analyze and detect these multiple sets of signals, yielding the detection results. By removing abnormal signals, the differential linear fitting model can avoid their influence when performing positive detection, allowing direct quantitative fluorescence detection of the target signal. This method improves the accuracy of quantitative fluorescence detection.

[0131] The following is combined Figure 2 The implementation method of quantitative fluorescence detection in the embodiments of this application will be described in detail.

[0132] Please refer to Figure 2 , Figure 2 A flowchart illustrating an implementation method for quantitative fluorescence detection provided in this application is shown below. Figure 2 The implementation method of quantitative fluorescence detection shown includes:

[0133] Step 210: Continuously acquire the measured fluorescence data corresponding to the target signal and perform clustering processing.

[0134] Specifically: When performing fluorescence detection on an object, data is collected at 1-second intervals, and each set of m data points is grouped into a cluster. m can be 50, 100, or other values.

[0135] Step 220: Variance denoising model processing, outlier removal, and normalization.

[0136] Specifically: Outlier data is removed using a variance denoising model and then normalized. Each set of signals to be detected corresponds to a normalized signal.

[0137] Step 230: Differential processing and threshold determination.

[0138] Specifically: The data is differentially processed using a differential linear fitting model to obtain multiple slopes. The differences between these multiple slopes are then calculated to obtain multiple difference results, which are then compared with a preset threshold.

[0139] Step 240: If n consecutive points are greater than the threshold, it is positive; otherwise, it is negative.

[0140] Specifically: when there are n consecutive data points greater than a preset threshold, it can be judged as positive; otherwise, it is considered negative.

[0141] also, Figure 2 The methods and steps shown can be found in [reference]. Figure 1 The method shown will not be elaborated further here.

[0142] The previous text passed Figures 1-2 The method for quantitative fluorescence detection is described below, in conjunction with... Figures 3-4 Describe the apparatus for quantitative fluorescence detection.

[0143] Please refer to Figure 3 This is a schematic block diagram of a fluorescence quantitative detection device 300 provided in an embodiment of this application. The device 300 can be a module, program segment, or code on an electronic device. This device 300 is related to the above... Figure 1 The method implementation corresponds to this and can be executed. Figure 1 The various steps involved in the method embodiments and the specific functions of the device 300 can be found in the following description. To avoid repetition, detailed descriptions are appropriately omitted here.

[0144] Optionally, the device 300 includes:

[0145] The elimination module 310 is used to eliminate abnormal signals from multiple initial sets of signals to be detected by using a preset variance denoising model, thereby obtaining multiple sets of signals to be detected. The abnormal signals refer to non-target signals and outlier signals collected during signal collection.

[0146] The detection module 320 is used to detect and analyze multiple sets of signals to be detected through a preset differential linear fitting model to obtain detection results.

[0147] Optionally, the rejection module is specifically used for:

[0148] The variance of each set in the multiple initial sets of signals to be detected is calculated using a variance denoising model. The variance of each set in the multiple initial sets of signals to be detected is obtained by the following formula:

[0149]

[0150]

[0151] Where m represents the number of signals in the initial set of signals to be detected, ni represents the i-th signal point in the n-th initial set of signals to be detected, and y ni Y represents the signal value of the i-th signal within the n-th initial set of signals to be detected. n V represents the mean value of the signals in the nth initial set of signals to be detected. n This represents the mean variance of the nth initial set of signals to be detected; abnormal signals in the multiple initial sets of signals to be detected are eliminated based on the variance of each set.

[0152] Optionally, the rejection module is specifically used for:

[0153] The rejection threshold is determined based on the variance of each set and the mean of the signal values ​​in each set of multiple initial sets of signals to be detected. The rejection threshold is determined by the following formula:

[0154] T n =F(V n Y n );

[0155] Among them, T n V is the threshold for removing abnormal signals. n Y represents the mean variance of the nth initial set of signals to be detected. n This represents the mean value of the signals in the nth initial set of signals to be detected; signals with a value greater than the elimination threshold in each set are eliminated.

[0156] Optionally, the detection module is specifically used for:

[0157] Multiple sets of signals to be detected are normalized to obtain multiple normalized signals; the multiple signals are then jointly analyzed using a differential linear fitting model to obtain the detection results.

[0158] Optionally, the detection module is specifically used for:

[0159] The slope of the curve corresponding to each of the normalized signals is calculated using a difference linear fitting model. The slope of the curve corresponding to each of the normalized signals is calculated using the following formula:

[0160]

[0161] Among them, S i Represents the i-th normalized signal Y′ i The slope obtained by fitting the data to k consecutive data points, where k represents the slope obtained by normalizing the signal with k data points, Y′ i This represents the normalized value of the i-th signal. Indicates from Y′ i To Y′ i+k Signal average value, This represents the average value from signal i to signal i+k; based on preset rules, the slopes of multiple curves corresponding to multiple normalized signals are analyzed to obtain the detection results.

[0162] Optionally, the detection module is specifically used for:

[0163] The slopes of multiple curves corresponding to multiple normalized signals are subtracted to obtain multiple difference values. These difference values ​​are obtained using the following formula:

[0164] ΔS i+1 =S i+1 -S i ;

[0165] Among them, S i S is the slope of the curve corresponding to the i-th signal. i+1 Let ΔS be the slope of the curve corresponding to the (i+1)th signal. i+1 Let be the slope of the curve corresponding to the (i+1)th signal and the slope of the curve corresponding to the ith signal; when there are at least e consecutive difference values ​​greater than the preset threshold among multiple difference values, the detection result is positive, where e is a positive integer greater than or equal to 2; when there are no at least e consecutive difference values ​​greater than the preset threshold among multiple difference values, the detection result is negative.

[0166] Optionally, the device also includes:

[0167] The clustering module is used to continuously collect the initial signals to be detected of the target object before the elimination module removes abnormal signals from multiple initial sets of signals to be detected through a preset variance denoising model, and obtain a signal set; the signals in the signal set are clustered into m signals as units to obtain multiple initial sets of signals to be detected, where m is a positive integer greater than or equal to 2.

[0168] Optionally, the detection module is specifically used for:

[0169] The slopes of multiple normalized signals are differentially fitted using a differential linear fitting model to obtain the differential results; positive and negative results are then analyzed based on the differential results to obtain the detection results.

[0170] Please refer to Figure 4 This is a schematic block diagram of a fluorescence quantitative detection device provided in an embodiment of this application. The device may include a memory 410 and a processor 420. Optionally, the device may further include a communication interface 430 and a communication bus 440. This device is similar to the one described above. Figure 1 The method implementation corresponds to this and can be executed. Figure 1 The specific functions of the device involved in the method embodiments can be found in the following description.

[0171] Specifically, memory 410 is used to store computer-readable instructions.

[0172] Processor 420 is used to process readable instructions stored in memory and is capable of executing... Figure 1 Each step in the method.

[0173] The communication interface 430 is used for signaling or data communication with other node devices. For example, it is used for communication with a server or terminal, or for communication with other device nodes, but the embodiments of this application are not limited thereto.

[0174] Communication bus 440 is used to enable direct communication between the above components.

[0175] In this embodiment, the communication interface 430 of the device is used for signaling or data communication with other node devices. The memory 410 can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Optionally, the memory 410 can also be at least one storage device located remotely from the aforementioned processor. The memory 410 stores computer-readable instructions, which, when executed by the processor 420, enable the electronic device to perform the aforementioned... Figure 1 The method process is shown. Processor 420 can be used on device 300 and is used to perform the functions in this application. Exemplarily, the processor 420 described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, and the embodiments of this application are not limited thereto.

[0176] This application embodiment also provides a readable storage medium, wherein when the computer program is executed by a processor, it performs the following... Figure 1 The method process executed by the electronic device in the illustrated method embodiment.

[0177] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0178] In summary, this application provides a method, apparatus, electronic device, and readable storage medium for quantitative fluorescence detection. The method includes: removing outlier signals from multiple initial sets of signals to be detected using a preset variance denoising model to obtain multiple sets of signals to be detected, wherein outlier signals represent non-target signals and outliers collected during signal collection; and performing detection and analysis on the multiple sets of signals to be detected using a preset differential linear fitting model to obtain detection results. This method can improve the accuracy of quantitative fluorescence detection.

[0179] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0180] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0181] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0183] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0184] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for quantitative fluorescence detection, characterized in that, include: By using a preset variance denoising model, abnormal signals are removed from multiple initial sets of signals to be detected, resulting in multiple sets of signals to be detected. The abnormal signals refer to non-target signals and outlier signals collected during signal collection. The step of removing abnormal signals from multiple initial sets of signals to be detected using a preset variance denoising model includes: The variance of each set in the plurality of initial sets of signals to be detected is calculated using the variance denoising model. The variance of each set in the plurality of initial sets of signals to be detected is obtained using the following formula: ； ； Where m represents the number of signals in the initial set of signals to be detected, and ni represents the i-th signal point in the n-th initial set of signals to be detected. This represents the signal value of the i-th signal within the n-th initial set of signals to be detected. This represents the mean value of the signals in the nth initial set of signals to be detected. This represents the mean variance of the nth initial set of signals to be detected; Abnormal signals in the plurality of initial sets of signals to be detected are eliminated based on the variance of each set. The detection results are obtained by analyzing the multiple sets of signals to be detected using a preset differential linear fitting model.

2. The method according to claim 1, characterized in that, The step of removing abnormal signals from the plurality of initial sets of signals to be detected based on the variance of each set includes: The rejection threshold is determined based on the variance of each set and the mean of the signal values ​​in each set of the plurality of initial sets of signals to be detected. The rejection threshold is determined by the following formula: ； in, The threshold for removing abnormal signals. This represents the mean variance of the nth initial set of signals to be detected. This represents the mean value of the signals in the nth initial set of signals to be detected. The signals to be detected that have a signal value greater than the rejection threshold in each set are rejected.

3. The method according to claim 1 or 2, characterized in that, The detection and analysis of the multiple sets of signals to be detected using a preset differential linear fitting model to obtain detection results includes: The set of multiple signals to be detected is normalized to obtain multiple normalized signals; The detection result is obtained by jointly analyzing the multiple signals using the differential linear fitting model.

4. The method according to claim 3, characterized in that, The detection and analysis of the multiple sets of signals to be detected using a preset differential linear fitting model to obtain detection results includes: The slope of the curve corresponding to each of the normalized multiple signals is calculated using the differential linear fitting model. The slope of the curve corresponding to each of the normalized multiple signals is calculated using the following formula: ； in, Represents the i-th normalized signal The slope obtained by fitting the data to the subsequent k consecutive data points, where k represents the k-th normalized signal. This represents the normalized value of the i-th signal. Indicates from arrive Signal average value, This represents the average value from signal i to signal i+k; The detection results are obtained by analyzing the slopes of multiple curves corresponding to the normalized signals according to preset rules.

5. The method according to claim 4, characterized in that, The step of analyzing the slopes of multiple curves corresponding to the normalized multiple signals according to preset rules to obtain the detection results includes: The slopes of the curves corresponding to the normalized signals are differentially analyzed to obtain multiple difference values. These difference values ​​are obtained using the following formula: ； in, S is the slope of the curve corresponding to the i-th signal. i+1 Let be the slope of the curve corresponding to the (i+1)th signal. The slopes of the curves corresponding to the (i+1)th signal and the ith signal are respectively. When at least e consecutive difference values ​​among the plurality of difference values ​​are greater than a preset threshold, the detection result is positive, where e is a positive integer greater than or equal to 2; The detection result is negative when there are no consecutive at least e difference values ​​greater than a preset threshold among the plurality of difference values.

6. The method according to claim 1 or 2, characterized in that, Before removing abnormal signals from multiple initial sets of signals to be detected using a preset variance denoising model, the method further includes: The initial detection signal of the target object is continuously acquired, and the initial detection signal is clustered into m signals to obtain the multiple initial detection signal sets, where m is a positive integer greater than or equal to 2.

7. The method according to claim 3, characterized in that, The detection and analysis of the multiple sets of signals to be detected using a preset differential linear fitting model to obtain detection results includes: The slopes corresponding to the normalized multiple signals are differentially analyzed using the differential linear fitting model to obtain the differential result. The positive and negative results are analyzed based on the difference results to obtain the detection results.

8. A device for quantitative fluorescence detection, characterized in that, include: The elimination module is used to eliminate abnormal signals from multiple initial sets of signals to be detected using a preset variance denoising model, thereby obtaining multiple sets of signals to be detected. The abnormal signals refer to non-target signals and outlier signals collected during signal collection. The rejection module is specifically used for: The variance of each set in the plurality of initial sets of signals to be detected is calculated using the variance denoising model. The variance of each set in the plurality of initial sets of signals to be detected is obtained using the following formula: ； ； Where m represents the number of signals in the initial set of signals to be detected, and ni represents the i-th signal point in the n-th initial set of signals to be detected. This represents the signal value of the i-th signal within the n-th initial set of signals to be detected. This represents the mean value of the signals in the nth initial set of signals to be detected. This represents the mean variance of the nth initial set of signals to be detected; Abnormal signals in the plurality of initial sets of signals to be detected are eliminated based on the variance of each set. The detection module is used to perform detection and analysis on the multiple sets of signals to be detected using a preset differential linear fitting model, and obtain the detection results.

9. An electronic device, characterized in that, include: A memory and a processor, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1-7.

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