CN117907511A - Automatic analysis method and device for multi-component overlapping peaks and electronic equipment - Google Patents
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Abstract
The embodiment of the specification discloses an automatic analysis method and device for multi-component overlapping peaks and electronic equipment. The method includes acquiring first matrix data; carrying out small wave peak detection on each ion spectrum data to determine a spectrum peak; grouping all spectral peaks; calculating the sharpness value of each target spectral peak in the first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak; determining deconvolution intervals of candidate model spectrum peaks, and constructing second matrix data; and determining an overlapped spectrum peak to be optimized in the deconvolution interval based on Gaussian similarity, and reconstructing a mass spectrum profile after optimizing the overlapped spectrum peak based on an LM algorithm. According to the embodiment of the specification, the automatic deconvolution separation process of various complex overlapping systems is realized, manual intervention is reduced, the number of the overlapping components determined in the analysis result is more accurate, the peak profile optimization effect is better, and the accuracy of qualitative and quantitative analysis according to the analysis result is improved.
Description
Technical Field
One or more embodiments of the present disclosure relate to data processing technology, and in particular, to an automated analysis method, an apparatus, and an electronic device for multi-component overlapping peaks.
Background
In the chemical analysis of a spectrum chromatography mass spectrometer, the component co-elution is a ubiquitous phenomenon, and at present, the problem of partial separation can be solved only by optimizing experimental parameters. For more accurate qualitative and quantitative analysis of more overlapping components, a mathematical analysis (i.e., deconvolution) is a simpler and more optimal option.
However, the current mathematical analysis methods have some limitations due to the complexity of the measurement system. The most common method is to select a model spectrum peak through peak parameters such as peak sharpness value and peak intensity, and directly analyze the model spectrum peak, but the mode is not used for optimizing the shape of the spectrum peak in the analysis process, so that the result is often influenced by the accuracy of the selected spectrum peak. Another widely accepted approach is to resolve from matrix decomposition using multivariate resolution techniques, a representative approach is the multivariate curve resolution-iterative least squares method (MCR-ALS) and parallel factor analysis 2 (paramac 2). Such methods require the initial estimation of the number of components and the chromatogram (or spectrum or mass spectrum) in a given matrix, but may result in inaccurate given component fractions and initial estimates due to some co-linear components, high similarity, or overlapping systems with smaller shoulders, thereby significantly affecting the final analytical results. In addition, some mathematical analysis methods need to manually cut spectral peak data, and under the conditions of large number of samples and large number of components, the requirements on personnel operation are high, the intensity is high, the control accuracy is low, and therefore the influence on the accuracy of analysis results is also great. Therefore, in the analysis of a complex overlapping system, the current mathematical analysis method cannot meet the requirement of automatic separation, and the accuracy of an analysis result is not ideal.
Disclosure of Invention
To solve the above problems, one or more embodiments of the present specification describe an automated analysis method, apparatus and electronic device for multi-component overlapping peaks.
According to a first aspect, there is provided an automated analysis method of multicomponent overlapping peaks, the method comprising:
acquiring first matrix data of a mixture to be analyzed, wherein rows of the first matrix data are used for representing a target dimension, columns of the first matrix data are used for representing a mass-to-charge ratio dimension, and the target dimension is a time dimension when analyzing chromatography and a wavelength dimension when analyzing spectrum;
Determining all ion spectrum data of the first matrix data, respectively carrying out small peak detection on each ion spectrum data, and determining a spectrum peak corresponding to each ion spectrum data, wherein the ion spectrum data is a column of data in the first matrix data;
After a first target interval for grouping is determined based on a matching filter, all spectrum peaks are grouped based on each first target interval, and the minimum value of a corresponding target vector of the matching filter is a dividing point of each first target interval;
Calculating the sharpness value of each target spectral peak in the first target interval aiming at each first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak, wherein the target spectral peak is the spectral peak with the intensity value in the first target interval being larger than the preset proportion of the intensity value of the strongest spectral peak;
Determining a deconvolution interval of the candidate model spectral peaks for each candidate model spectral peak, constructing second matrix data, determining the number of components according to the number of the candidate model spectral peaks in the deconvolution interval, wherein rows of the second matrix data are used for representing target point number dimensions, columns of the second matrix data are used for representing the mass-to-charge ratio dimensions, and the range of the target point number dimensions is determined based on the interval range of the deconvolution interval;
And determining overlapping spectrum peaks to be optimized in the deconvolution interval based on Gaussian similarity for each deconvolution interval, and reconstructing a mass spectrum profile corresponding to the second matrix data after optimizing the overlapping spectrum peaks based on an LM algorithm.
Preferably, the obtaining the first matrix data of the mixture to be resolved includes:
And reading the original data of the mixture to be analyzed based on a preset format, and constructing first matrix data based on the original data, wherein the preset format comprises at least one of a cdf format, a mzXML format and a mzML format.
Preferably, the detecting small peaks of each of the ion spectrum data, determining a spectrum peak corresponding to each of the ion spectrum data, includes:
And carrying out convolution operation on each piece of ion spectrum data based on a mexico cap wavelet function, determining a spectrum peak corresponding to each piece of ion spectrum data, and determining peak information of the spectrum peak based on a local maximum value and a local minimum value in a wavelet space, wherein the peak information comprises a peak position, a peak starting point, a peak ending point and a spectrum profile, the position of the local maximum value is the peak position, the position of the local minimum value is the peak starting point and the peak ending point, and a part between the peak starting point and the peak ending point is the spectrum profile.
Preferably, after grouping all the spectral peaks based on each of the first target intervals, the method further includes:
And calculating the retention time difference value between the spectrum peaks in the first target interval by every two for each first target interval, and merging the spectrum peaks with the retention time difference value not larger than a preset difference value.
Preferably, the determining the deconvolution interval of the candidate model spectrum peak includes:
constructing a second target interval based on the peak of the candidate model spectrum and the peak width of the candidate model spectrum with a preset multiple, determining an overlapped peak in the second target interval, and generating a deconvolution interval, wherein the left boundary of the deconvolution interval is the starting point of the leftmost peak, and the right boundary of the deconvolution interval is the ending point of the rightmost peak.
Preferably, the determining the overlapping spectrum peak to be optimized in the deconvolution interval based on gaussian similarity and optimizing the overlapping spectrum peak based on LM algorithm includes:
Carrying out Gaussian similarity calculation on the candidate model spectrum peaks in the deconvolution interval, and determining Gaussian model spectrum peaks and/or overlapping spectrum peaks to be optimized, wherein the Gaussian model spectrum peaks are spectrum peaks with Gaussian similarity not smaller than a preset value, and the overlapping spectrum peaks are spectrum peaks with Gaussian similarity smaller than the preset value;
And optimizing the overlapped spectrum peaks based on a modified Gaussian model to obtain modified Gaussian spectrum peaks, and optimizing Gaussian model parameters based on an LM algorithm to obtain each target ion spectrum peak in the second matrix data, wherein the target ion spectrum peaks are represented based on linear summation of the modified Gaussian spectrum peaks and Gaussian model spectrum peaks.
Preferably, the reconstructing the mass spectrum profile corresponding to the second matrix data includes:
and splicing the target ion spectrum peaks into optimal spectrum peaks, and reconstructing the mass spectrum profile of the optimal spectrum peaks based on a least square algorithm.
According to a second aspect, there is provided an automated resolving device for multicomponent overlapping peaks, the device comprising:
The device comprises an acquisition module, a first analysis module and a second analysis module, wherein the acquisition module is used for acquiring first matrix data of a mixture to be analyzed, rows of the first matrix data are used for representing target dimensions, columns of the first matrix data are used for representing mass-to-charge ratio dimensions, and the target dimensions are time dimensions when analyzing a chromatograph and wavelength dimensions when analyzing the spectrum;
The first determining module is used for determining all the ion spectrum data of the first matrix data, respectively carrying out small peak detection on each ion spectrum data, and determining a spectrum peak corresponding to each ion spectrum data, wherein the ion spectrum data is a column of data in the first matrix data;
the second determining module is used for grouping all spectral peaks based on each first target interval after determining the first target interval for grouping based on a matching filter, wherein the minimum value of a corresponding target vector of the matching filter is a dividing point of each first target interval;
the calculation module is used for calculating the sharpness value of each target spectral peak in the first target interval according to each first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak, wherein the target spectral peak is the spectral peak with the intensity value in the first target interval being larger than the preset proportion of the intensity value of the strongest spectral peak;
A third determining module, configured to determine, for each of the candidate model spectral peaks, a deconvolution interval of the candidate model spectral peaks, construct second matrix data, and determine a component number according to the number of the candidate model spectral peaks in the deconvolution interval, where rows of the second matrix data are used to represent a target point dimension, columns of the second matrix data are used to represent the mass-to-charge ratio dimension, and a range of the target point dimension is determined based on an interval range of the deconvolution interval;
And the reconstruction module is used for determining an overlapped spectrum peak to be optimized in the deconvolution interval based on Gaussian similarity for each deconvolution interval, and reconstructing a mass spectrum profile corresponding to the second matrix data after optimizing the overlapped spectrum peak based on an LM algorithm.
According to a third aspect, there is provided an electronic device comprising a processor and a memory;
The processor is connected with the memory;
the memory is used for storing executable program codes;
The processor runs a program corresponding to executable program code stored in the memory by reading the executable program code for performing the steps of the method as provided in the first aspect or any one of the possible implementations of the first aspect.
According to a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program having instructions stored therein which, when run on a computer or processor, cause the computer or processor to perform a method as provided by any one of the possible implementations of the first aspect or the first aspect.
According to the method and the device provided by the embodiment of the specification, the number of the overlapped components can be determined through small peak detection, the overlapped spectrum peak to be optimized in the candidate model spectrum peaks in the deconvolution interval is determined through the sharpness value and Gaussian similarity, and the mass spectrum profile is reconstructed after the peak shape is optimized according to the LM algorithm, so that the automatic deconvolution separation process of various complex overlapped systems is realized, the manual intervention is reduced, the number of the overlapped components determined in the analysis result is more accurate, the peak shape profile optimization effect is better, and the accuracy of qualitative and quantitative analysis according to the analysis result is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of an automated analysis method of overlapping peaks in one embodiment of the present disclosure.
FIG. 2 is a schematic diagram of an automated analysis device in one embodiment of the present disclosure for multicomponent overlapping peaks.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.
In the following description, the terms "first," "second," and "first," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The following description provides various embodiments of the application that may be substituted or combined between different embodiments, and thus the application is also to be considered as embracing all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the application should also be seen as embracing one or more of all other possible combinations of one or more of A, B, C, D, although such an embodiment may not be explicitly recited in the following.
The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the application. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of an automated analysis method for multi-component overlapping peaks according to an embodiment of the present application. In an embodiment of the present application, the method includes:
S101, acquiring first matrix data of a mixture to be analyzed.
The rows of the first matrix data are used for representing a target dimension, the columns of the first matrix data are used for representing a mass-to-charge ratio dimension, and the target dimension is a time dimension when analyzing a spectrum and a wavelength dimension when analyzing the spectrum.
The execution subject of the present application may be a cloud server.
In the embodiment of the present disclosure, the cloud server may acquire the raw data of the mixture to be analyzed in the form of matrix data, so that in the subsequent step, the spectrum analysis can be performed through the first matrix data. The rows of the first matrix data are used to represent the target dimension and the columns are used to represent the mass to charge ratio dimension, which typically takes the range of 0-800 Da. According to different actual conditions, the target dimension of the first matrix data can be adjusted, if the chromatographic profile is required to be acquired for analysis, the target dimension is set as a time dimension, and if the spectral profile is required to be analyzed, the target dimension is set as a wavelength dimension. If both the chromatographic profile and the spectral profile are required, two different first matrix data can be generated simultaneously, and the analytical calculation can be performed respectively.
In one embodiment, the obtaining the first matrix data of the mixture to be resolved includes:
And reading the original data of the mixture to be analyzed based on a preset format, and constructing first matrix data based on the original data, wherein the preset format comprises at least one of a cdf format, a mzXML format and a mzML format.
In this embodiment of the present disclosure, the cloud server may not be able to directly read the matrix data meeting the requirement of subsequent parsing, in this case, the cloud server may read the original data in the format of cdf, mzXML, mzML, or the like, and splice the original data according to the dimensions of the rows and columns to form the first matrix data.
S102, determining all the ion spectrum data of the first matrix data, respectively carrying out small peak detection on each ion spectrum data, and determining a spectrum peak corresponding to each ion spectrum data.
Wherein the ion spectrum data is a column of data in the first matrix data.
In the embodiment of the present disclosure, the cloud server selects different mass-to-charge ratio values to determine each ion spectrum respectively. Then, the cloud server carries out convolution operation on the ion spectrum data in a small peak detection mode so as to realize peak detection and generate a spectrum peak of each ion spectrum data. After the spectral peaks are determined, the peak information of each spectral peak can be determined, and further the data such as the peak position, the spectral profile and the like of each spectral peak can be determined, so that the subsequent analysis and calculation of the spectral peaks can be facilitated. The wavelet function used for small peak detection can be a mexico cap wavelet function, a Morlet wavelet function, a haar wavelet function, a multi Bei Xixiao wave function, and the like.
In an embodiment, the detecting small peaks of each of the ion spectrum data, and determining a spectrum peak corresponding to each of the ion spectrum data, includes:
And carrying out convolution operation on each piece of ion spectrum data based on a mexico cap wavelet function, determining a spectrum peak corresponding to each piece of ion spectrum data, and determining peak information of the spectrum peak based on a local maximum value and a local minimum value in a wavelet space, wherein the peak information comprises a peak position, a peak starting point, a peak ending point and a spectrum profile, the position of the local maximum value is the peak position, the position of the local minimum value is the peak starting point and the peak ending point, and a part between the peak starting point and the peak ending point is the spectrum profile.
In the embodiment of the present specification, the ionic spectrum data is convolved with a mexico cap wavelet function as an example, so as to obtain a spectral peak corresponding to the ionic spectrum data. After the spectrum peak is obtained, the cloud server also calculates a local maximum value and a local minimum value in the wavelet space, wherein the position of the local maximum value is the peak position, two positions of the local minimum value are respectively the peak starting point and the peak ending point from left to right in a coordinate system, and the spectrum peak part between the peak starting point and the peak ending point is the spectrum profile. These data together constitute peak information for calculation of the subsequent parsing process.
The formula for convolving ion spectrum data by the mexico cap wavelet function is as follows:
wherein t is the intensity at different time points in the ion spectrum data, Is wavelet space data (i.e. one row in the first matrix data)/>To control parameters of the wavelet width of the mexico cap,/>E is a natural index.
S103, after the first target intervals for grouping are determined based on the matching filter, grouping all spectral peaks based on each first target interval.
And the minimum value of the corresponding target vector of the matching filter is the dividing point of each first target interval.
In the embodiment of the present specification, after determining all the spectral peaks, the cloud server groups all the spectral peaks using the matching filter, that is, partitions all the peak top points (peak top point distribution areas). Each region corresponds to a set of peaks, each set of peaks corresponding to a component. The formula of the target vector corresponding to the matching filter is:
where N is the number of rows of the first matrix data, coef is a coefficient, As the width control parameter of the first target interval, the peak top points used for determining how wide the time range is will be identified as belonging to the same group, and p takes a default value of 10. The minimum value of the vector is a partition point of the first target section, that is, each first target section is partitioned according to each minimum value point.
In one embodiment, after grouping all the spectral peaks based on each of the first target intervals, the method further includes:
And calculating the retention time difference value between the spectrum peaks in the first target interval by every two for each first target interval, and merging the spectrum peaks with the retention time difference value not larger than a preset difference value.
In the embodiment of the present disclosure, in each group (i.e., each first target interval), the cloud server calculates the retention time difference between any two spectral peaks. Spectral peaks for which the retention time difference is not greater than a preset difference (e.g., 2 scan point distances) will be considered the same peak and combined.
And S104, calculating the sharpness value of each target spectral peak in the first target interval according to each first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak.
The target spectral peak is the spectral peak with the intensity value in the first target interval being larger than the preset proportion of the intensity value of the strongest spectral peak.
In this embodiment of the present disclosure, the cloud server calculates an intensity value of each spectrum peak in each first target interval, determines a strongest spectrum peak with a maximum intensity value, and uses a spectrum peak with an intensity value greater than a preset proportion (e.g., 90%) of the intensity value of the strongest spectrum peak as the target spectrum peak. Then, the sharpness value of the target spectrum peak is calculated, the target spectrum peak with the largest sharpness value is selected as the candidate model spectrum peak, and deconvolution is carried out by taking the candidate model spectrum peak as a reference in the follow-up step.
S105, determining a deconvolution interval of the candidate model spectrum peaks for each candidate model spectrum peak, constructing second matrix data, and determining the component number according to the number of the candidate model spectrum peaks in the deconvolution interval.
The rows of the second matrix data are used for representing a target point dimension, the columns of the second matrix data are used for representing the mass-to-charge ratio dimension, and the range of the target point dimension is determined based on the interval range of the deconvolution interval.
In this embodiment of the present disclosure, the cloud server may construct a deconvolution interval for each candidate model spectrum peak, and construct the second matrix data according to the interval range of the deconvolution interval. Because there may be overlapping between different candidate model spectrum peaks, a plurality of candidate model spectrum peaks may be covered in the deconvolution section, in this case, the candidate model spectrum peaks are all put into the same second matrix data for subsequent analysis processing, and the number of candidate model spectrum peaks in the deconvolution section is the number of components. The method comprises the steps of determining the spectrum peaks through small peak detection, grouping, determining the candidate model spectrum peaks in each group of peaks, and finally determining the number of components according to the number of the candidate model spectrum peaks in a deconvolution interval, so that the number of components can be determined more accurately, and the components are not easy to interfere. The deconvolution interval may be determined by setting a preset multiplying power according to the width of the candidate model spectrum peak, or by first determining an overlapping peak in a certain range, and then setting the positions of the leftmost peak and the rightmost peak in the range, or may be another embodiment. In addition, according to the difference of the target dimensions, the target point dimensions also correspondingly differ, namely the time point dimension corresponds to the wavelength point dimension.
The second matrix data corresponds to a section of first matrix data intercepted according to the range of the deconvolution interval, the ion spectrum data in the second matrix data is the target ion spectrum data, and the corresponding spectrum peak is the target ion spectrum peak.
In one embodiment, the determining the deconvolution interval of the candidate pattern spectral peaks includes:
constructing a second target interval based on the peak of the candidate model spectrum and the peak width of the candidate model spectrum with a preset multiple, determining an overlapped peak in the second target interval, and generating a deconvolution interval, wherein the left boundary of the deconvolution interval is the starting point of the leftmost peak, and the right boundary of the deconvolution interval is the ending point of the rightmost peak.
In this embodiment of the present disclosure, first, a second target interval is constructed according to the peak of the candidate model spectrum and the peak width of the candidate model spectrum of a preset multiple, and assuming that the preset multiple is 1.5 times and the peak width of the candidate model spectrum is w, the second target interval may be [ -1.5×w,1.5×w ]. And then, the cloud server calculates the difference between the starting point and the ending point of the candidate model spectrum peak and the ending point and the starting point of the adjacent peak, and if the absolute value of the difference is smaller than 2 points, the cloud server indicates that the two peaks are overlapped. The cloud server determines overlapping peaks in the left side [ -1.5 x w,0] and the right side [0,1.5 x w ] of the interval respectively, takes the starting point of the leftmost peak as the left boundary t1 of the deconvolution interval, takes the end point of the rightmost peak as the right boundary t2 of the deconvolution interval, and the row of the second matrix data is expressed as the target point number in the range from t1 to t 2.
S106, determining overlapping spectrum peaks to be optimized in the deconvolution intervals based on Gaussian similarity, and reconstructing mass spectrum contours corresponding to the second matrix data after optimizing the overlapping spectrum peaks based on an LM algorithm.
In the embodiment of the present specification, analysis of a complex overlapping system generally requires analysis to distinguish between different components, the number of components in each component, spectral profile, chromatographic profile, mass spectrum profile, etc., so as to perform quantitative analysis according to the spectral profile and qualitative analysis according to the mass spectrum profile. Thus, after the foregoing steps have resolved the spectral profile and/or chromatographic profile and component numbers, it is also necessary to determine the mass spectrum profile. Since each first target interval may contain a plurality of spectral peaks of the ion channel, wherein a part of the spectral peaks have a better peak shape (gaussian similarity value is high, and typically these spectral peaks are selective ion fragments, and are ions unique to the component) and a part of the overlapping spectral peaks (ions contained in both adjacent components), it is first necessary to perform spectral profile optimization on the spectral peaks with bad peak shape (generally referred to as overlapping peaks). Specifically, the spectrum peak in the deconvolution interval is calculated through Gaussian similarity, and the overlapped spectrum peak with the Gaussian similarity value lower than a preset value is determined. The cloud server will then optimize the profile of the overlapping spectral peaks using a Levenberg-Marquardt iterative optimization algorithm. And finally, each ion spectrum peak in the second matrix data can be represented by the linear sum of all the optimized candidate model spectrum peaks, and the mass spectrum profile can be reconstructed according to the ion spectrum peaks, so that the obtained mass spectrum profile has higher accuracy because the overlapping spectrum peaks are subjected to profile optimization, and each ion spectrum peak is represented by the linear sum of each optimized candidate model spectrum peak.
In one embodiment, the determining the overlapping spectral peak to be optimized in the deconvolution interval based on gaussian similarity and optimizing the overlapping spectral peak based on LM algorithm includes:
Carrying out Gaussian similarity calculation on the candidate model spectrum peaks in the deconvolution interval, and determining Gaussian model spectrum peaks and/or overlapping spectrum peaks to be optimized, wherein the Gaussian model spectrum peaks are spectrum peaks with Gaussian similarity not smaller than a preset value, and the overlapping spectrum peaks are spectrum peaks with Gaussian similarity smaller than the preset value;
And optimizing the overlapped spectrum peaks based on a modified Gaussian model to obtain modified Gaussian spectrum peaks, and optimizing Gaussian model parameters based on an LM algorithm to obtain each target ion spectrum peak in the second matrix data, wherein the target ion spectrum peaks are represented based on linear summation of the modified Gaussian spectrum peaks and Gaussian model spectrum peaks.
In the embodiment of the present specification, firstly, gaussian similarity calculation is performed on candidate model spectral peaks in a deconvolution interval, and whether the spectral peak parameters need to be shape optimized is determined, where a calculation formula is as follows:
Wherein H is peak height, A is peak area, si is Gaussian similarity, and W is half-height peak width. A spectral peak having a gaussian similarity of 0.95 or more will be determined as a gaussian model spectral peak, and a spectral peak smaller than 0.95 will be determined as an overlapping spectral peak. The overlapped spectrum peak is the spectrum peak needing to be subjected to shape optimization.
Then, the shape of the overlapped spectrum peak is corrected by using a correction Gaussian model, and the formula is as follows:
wherein, ，/>Is the peak position, w is the peak width related quantity,/>For correction factors, erf is the error function, t is the acquisition time series,/>To correct the gaussian spectral peak. Gaussian model parameters at this time/>Is unknown and requires a solution of the optimal parameters.
Target ion spectrum peaks overlapping with any one of the second matrix dataThe profile can be expressed as a linear sum of n modified gaussian spectral peaks and m gaussian model spectral peaks, as shown in the following equation:
wherein, Gaussian model parameters for the ith modified Gaussian spectrum peak,/>For the jth gaussian model spectral peak,Is a candidate model chromatographic peak or a spectral profile of a candidate model spectral peak, wherein/>，/>To fit the residual.
The LM algorithm optimized function is:
through iterative optimization, the optimal parameters can be obtained And finally, obtaining an exact target ion spectrum peak.
In an embodiment, the reconstructing the mass spectrum profile corresponding to the second matrix data includes:
and splicing the target ion spectrum peaks into optimal spectrum peaks, and reconstructing the mass spectrum profile of the optimal spectrum peaks based on a least square algorithm.
In the embodiment of the present specification, for the second matrix data(Where M is the upper limit of the mass-to-charge ratio range), the optimum spectral peak may be expressed as a linear sum of the individual target ion spectral peaks as expressed in the following equation:
wherein, For the ith target ion spectrum peak,/>For the ith reconstructed mass spectrum profile,/>To fit the residual.
Then, solving a reconstructed mass spectrum profile by adopting a non-negative least square algorithm, wherein a solving formula is as follows:
An automated analysis device for multi-component overlapping peaks according to an embodiment of the present application will be described in detail with reference to fig. 2. It should be noted that, the automated analysis device of the multicomponent overlapping peaks shown in fig. 2 is used to perform the method of the embodiment of fig. 1 of the present application, and for convenience of explanation, only the portions relevant to the embodiment of the present application are shown, and specific technical details are not disclosed, please refer to the embodiment of fig. 1 of the present application.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an automated analysis device for multi-component overlapping peaks according to an embodiment of the present application. As shown in fig. 2, the apparatus includes:
an obtaining module 201, configured to obtain first matrix data of a mixture to be resolved, where rows of the first matrix data are used to represent a target dimension, columns of the first matrix data are used to represent a mass-to-charge ratio dimension, and the target dimension is a time dimension when resolving a spectrum and a wavelength dimension when resolving a spectrum;
A first determining module 202, configured to determine all ion spectrum data of the first matrix data, respectively perform small peak detection on each of the ion spectrum data, and determine a spectrum peak corresponding to each of the ion spectrum data, where the ion spectrum data is a column of data in the first matrix data;
A second determining module 203, configured to determine a first target interval for grouping based on a matching filter, and group all spectral peaks based on each first target interval, where a minimum value of a corresponding target vector of the matching filter is a partition point of each first target interval;
A calculation module 204, configured to calculate, for each first target interval, a sharpness value of each target spectral peak in the first target interval, and use a target spectral peak with the largest sharpness value as a candidate model spectral peak, where the target spectral peak is the spectral peak with an intensity value in the first target interval that is greater than a preset proportion of an intensity value of a strongest spectral peak;
A third determining module 205, configured to determine, for each of the candidate model spectral peaks, a deconvolution interval of the candidate model spectral peaks, construct second matrix data, and determine a component number according to the number of the candidate model spectral peaks in the deconvolution interval, where a row of the second matrix data is used to represent a target point dimension, a column of the second matrix data is used to represent the mass-to-charge ratio dimension, and a range of the target point dimension is determined based on an interval range of the deconvolution interval;
And a reconstruction module 206, configured to determine, for each deconvolution interval, an overlapping spectrum peak to be optimized in the deconvolution interval based on gaussian similarity, and reconstruct a mass spectrum profile corresponding to the second matrix data after optimizing the overlapping spectrum peak based on LM algorithm.
In one embodiment, the obtaining module 201 is specifically configured to:
And reading the original data of the mixture to be analyzed based on a preset format, and constructing first matrix data based on the original data, wherein the preset format comprises at least one of a cdf format, a mzXML format and a mzML format.
In one embodiment, the first determining module 202 is specifically configured to:
And carrying out convolution operation on each piece of ion spectrum data based on a mexico cap wavelet function, determining a spectrum peak corresponding to each piece of ion spectrum data, and determining peak information of the spectrum peak based on a local maximum value and a local minimum value in a wavelet space, wherein the peak information comprises a peak position, a peak starting point, a peak ending point and a spectrum profile, the position of the local maximum value is the peak position, the position of the local minimum value is the peak starting point and the peak ending point, and a part between the peak starting point and the peak ending point is the spectrum profile.
In one embodiment, the second determining module 203 is specifically configured to:
And calculating the retention time difference value between the spectrum peaks in the first target interval by every two for each first target interval, and merging the spectrum peaks with the retention time difference value not larger than a preset difference value.
In one embodiment, the third determining module 205 is specifically configured to:
constructing a second target interval based on the peak of the candidate model spectrum and the peak width of the candidate model spectrum with a preset multiple, determining an overlapped peak in the second target interval, and generating a deconvolution interval, wherein the left boundary of the deconvolution interval is the starting point of the leftmost peak, and the right boundary of the deconvolution interval is the ending point of the rightmost peak.
In one embodiment, the reconstruction module 206 is specifically configured to:
Carrying out Gaussian similarity calculation on the candidate model spectrum peaks in the deconvolution interval, and determining Gaussian model spectrum peaks and/or overlapping spectrum peaks to be optimized, wherein the Gaussian model spectrum peaks are spectrum peaks with Gaussian similarity not smaller than a preset value, and the overlapping spectrum peaks are spectrum peaks with Gaussian similarity smaller than the preset value;
And optimizing the overlapped spectrum peaks based on a modified Gaussian model to obtain modified Gaussian spectrum peaks, and optimizing Gaussian model parameters based on an LM algorithm to obtain each target ion spectrum peak in the second matrix data, wherein the target ion spectrum peaks are represented based on linear summation of the modified Gaussian spectrum peaks and Gaussian model spectrum peaks.
In one embodiment, the reconstruction module 206 is specifically further configured to:
and splicing the target ion spectrum peaks into optimal spectrum peaks, and reconstructing the mass spectrum profile of the optimal spectrum peaks based on a least square algorithm.
It will be clear to those skilled in the art that the technical solutions of the embodiments of the present application may be implemented by means of software and/or hardware. "unit" and "module" in this specification refer to software and/or hardware capable of performing a particular function, either alone or in combination with other components, such as Field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA), integrated circuits (INTEGRATED CIRCUIT, ICs), and the like.
The processing units and/or modules of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 3, the electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein the communication bus 302 is used to enable connected communication between these components.
The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall electronic device 300, perform various functions of the electronic device 300, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may be implemented in at least one hardware form of digital signal processing (DIGITAL SIGNAL processing, DSP), field-programmable gate array (field-programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image central processing unit (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.
The memory 305 may include a random access memory (Random Access Memory, RAM) or a read-only memory (read-only memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. As shown in fig. 3, an operating system, a network communication module, a user interface module, and program instructions may be included in the memory 305, which is a type of computer storage medium.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user, and acquiring data input by the user; while processor 301 may be used to invoke an automated parsing application for multicomponent overlapping peaks stored in memory 305 and specifically:
acquiring first matrix data of a mixture to be analyzed, wherein rows of the first matrix data are used for representing a target dimension, columns of the first matrix data are used for representing a mass-to-charge ratio dimension, and the target dimension is a time dimension when analyzing chromatography and a wavelength dimension when analyzing spectrum;
Determining all ion spectrum data of the first matrix data, respectively carrying out small peak detection on each ion spectrum data, and determining a spectrum peak corresponding to each ion spectrum data, wherein the ion spectrum data is a column of data in the first matrix data;
After a first target interval for grouping is determined based on a matching filter, all spectrum peaks are grouped based on each first target interval, and the minimum value of a corresponding target vector of the matching filter is a dividing point of each first target interval;
Calculating the sharpness value of each target spectral peak in the first target interval aiming at each first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak, wherein the target spectral peak is the spectral peak with the intensity value in the first target interval being larger than the preset proportion of the intensity value of the strongest spectral peak;
Determining a deconvolution interval of the candidate model spectral peaks for each candidate model spectral peak, constructing second matrix data, determining the number of components according to the number of the candidate model spectral peaks in the deconvolution interval, wherein rows of the second matrix data are used for representing target point number dimensions, columns of the second matrix data are used for representing the mass-to-charge ratio dimensions, and the range of the target point number dimensions is determined based on the interval range of the deconvolution interval;
And determining overlapping spectrum peaks to be optimized in the deconvolution interval based on Gaussian similarity for each deconvolution interval, and reconstructing a mass spectrum profile corresponding to the second matrix data after optimizing the overlapping spectrum peaks based on an LM algorithm.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method. The computer-readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, dvds, CD-roms, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ics), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned memory includes: a U-disk, a read-only memory (ROM), a random access memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be performed by hardware associated with a program that is stored in a computer readable memory, which may include: flash disk, read-only memory (ROM), random-access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (10)
1. A method for automated analysis of multicomponent overlapping peaks, the method comprising:
acquiring first matrix data of a mixture to be analyzed, wherein rows of the first matrix data are used for representing a target dimension, columns of the first matrix data are used for representing a mass-to-charge ratio dimension, and the target dimension is a time dimension when analyzing chromatography and a wavelength dimension when analyzing spectrum;
Determining all ion spectrum data of the first matrix data, respectively carrying out small peak detection on each ion spectrum data, and determining a spectrum peak corresponding to each ion spectrum data, wherein the ion spectrum data is a column of data in the first matrix data;
After a first target interval for grouping is determined based on a matching filter, all spectrum peaks are grouped based on each first target interval, and the minimum value of a corresponding target vector of the matching filter is a dividing point of each first target interval;
Calculating the sharpness value of each target spectral peak in the first target interval aiming at each first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak, wherein the target spectral peak is the spectral peak with the intensity value in the first target interval being larger than the preset proportion of the intensity value of the strongest spectral peak;
Determining a deconvolution interval of the candidate model spectral peaks for each candidate model spectral peak, constructing second matrix data, determining the number of components according to the number of the candidate model spectral peaks in the deconvolution interval, wherein rows of the second matrix data are used for representing target point number dimensions, columns of the second matrix data are used for representing the mass-to-charge ratio dimensions, and the range of the target point number dimensions is determined based on the interval range of the deconvolution interval;
And determining overlapping spectrum peaks to be optimized in the deconvolution interval based on Gaussian similarity for each deconvolution interval, and reconstructing a mass spectrum profile corresponding to the second matrix data after optimizing the overlapping spectrum peaks based on an LM algorithm.
2. The method of claim 1, wherein the obtaining first matrix data of the mixture to be parsed comprises:
And reading the original data of the mixture to be analyzed based on a preset format, and constructing first matrix data based on the original data, wherein the preset format comprises at least one of a cdf format, a mzXML format and a mzML format.
3. The method of claim 1, wherein the performing small peak detection on each of the ion spectrum data to determine a spectral peak corresponding to each of the ion spectrum data comprises:
And carrying out convolution operation on each piece of ion spectrum data based on a mexico cap wavelet function, determining a spectrum peak corresponding to each piece of ion spectrum data, and determining peak information of the spectrum peak based on a local maximum value and a local minimum value in a wavelet space, wherein the peak information comprises a peak position, a peak starting point, a peak ending point and a spectrum profile, the position of the local maximum value is the peak position, the position of the local minimum value is the peak starting point and the peak ending point, and a part between the peak starting point and the peak ending point is the spectrum profile.
4. The method of claim 1, wherein after grouping all spectral peaks based on each of the first target intervals, further comprising:
And calculating the retention time difference value between the spectrum peaks in the first target interval by every two for each first target interval, and merging the spectrum peaks with the retention time difference value not larger than a preset difference value.
5. The method of claim 1, wherein said determining a deconvolution interval for the candidate pattern spectral peaks comprises:
constructing a second target interval based on the peak of the candidate model spectrum and the peak width of the candidate model spectrum with a preset multiple, determining an overlapped peak in the second target interval, and generating a deconvolution interval, wherein the left boundary of the deconvolution interval is the starting point of the leftmost peak, and the right boundary of the deconvolution interval is the ending point of the rightmost peak.
6. The method of claim 1, wherein the determining overlapping spectral peaks in the deconvolution interval to be optimized based on gaussian similarity and optimizing the overlapping spectral peaks based on LM algorithm comprises:
Carrying out Gaussian similarity calculation on the candidate model spectrum peaks in the deconvolution interval, and determining Gaussian model spectrum peaks and/or overlapping spectrum peaks to be optimized, wherein the Gaussian model spectrum peaks are spectrum peaks with Gaussian similarity not smaller than a preset value, and the overlapping spectrum peaks are spectrum peaks with Gaussian similarity smaller than the preset value;
And optimizing the overlapped spectrum peaks based on a modified Gaussian model to obtain modified Gaussian spectrum peaks, and optimizing Gaussian model parameters based on an LM algorithm to obtain each target ion spectrum peak in the second matrix data, wherein the target ion spectrum peaks are represented based on linear summation of the modified Gaussian spectrum peaks and Gaussian model spectrum peaks.
7. The method of claim 6, wherein reconstructing the mass spectrum profile corresponding to the second matrix data comprises:
and splicing the target ion spectrum peaks into optimal spectrum peaks, and reconstructing the mass spectrum profile of the optimal spectrum peaks based on a least square algorithm.
8. An automated analysis device for multicomponent overlapping peaks, the device comprising:
The device comprises an acquisition module, a first analysis module and a second analysis module, wherein the acquisition module is used for acquiring first matrix data of a mixture to be analyzed, rows of the first matrix data are used for representing target dimensions, columns of the first matrix data are used for representing mass-to-charge ratio dimensions, and the target dimensions are time dimensions when analyzing a chromatograph and wavelength dimensions when analyzing the spectrum;
The first determining module is used for determining all the ion spectrum data of the first matrix data, respectively carrying out small peak detection on each ion spectrum data, and determining a spectrum peak corresponding to each ion spectrum data, wherein the ion spectrum data is a column of data in the first matrix data;
the second determining module is used for grouping all spectral peaks based on each first target interval after determining the first target interval for grouping based on a matching filter, wherein the minimum value of a corresponding target vector of the matching filter is a dividing point of each first target interval;
the calculation module is used for calculating the sharpness value of each target spectral peak in the first target interval according to each first target interval, and taking the target spectral peak with the largest sharpness value as a candidate model spectral peak, wherein the target spectral peak is the spectral peak with the intensity value in the first target interval being larger than the preset proportion of the intensity value of the strongest spectral peak;
A third determining module, configured to determine, for each of the candidate model spectral peaks, a deconvolution interval of the candidate model spectral peaks, construct second matrix data, and determine a component number according to the number of the candidate model spectral peaks in the deconvolution interval, where rows of the second matrix data are used to represent a target point dimension, columns of the second matrix data are used to represent the mass-to-charge ratio dimension, and a range of the target point dimension is determined based on an interval range of the deconvolution interval;
And the reconstruction module is used for determining an overlapped spectrum peak to be optimized in the deconvolution interval based on Gaussian similarity for each deconvolution interval, and reconstructing a mass spectrum profile corresponding to the second matrix data after optimizing the overlapped spectrum peak based on an LM algorithm.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program having instructions stored therein, which when run on a computer or processor, cause the computer or processor to perform the steps of the method according to any of claims 1-7.
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