CN115409068B - Analysis processing method based on flow cytometry data - Google Patents
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Abstract
The application discloses an analysis processing method based on flow cytometry data, which comprises the following steps: detecting electrical characteristics of the flow cell sample using a plurality of different frequencies of electromagnetic excitation sources; respectively establishing the different frequencies and the electrical characteristic mapping relation table, combining the different frequencies and the electrical characteristic mapping relation table to form an electrical characteristic data matrix, performing two-dimensional Fourier transform on the data matrix to obtain a spectrum analysis result, processing the data matrix to form a two-dimensional graph input trained full convolution network when the low-frequency component of the longitudinal spectrum of the spectrum analysis result exceeds a threshold value, and otherwise weighting all samples to form a two-dimensional graph input full convolution network; and judging the risk level according to the output result of the full convolution network. When the application analyzes the electrical characteristics of a large number of sample flow cells, the risk level is obtained by rapidly positioning the data positions and the number of abnormal cells in mass data through frequency domain analysis and gradient analysis, and a more accurate risk prediction method is provided, so that the tumor prediction accuracy is improved.
Description
Technical Field
The application relates to the field of artificial intelligence, in particular to an analysis processing method based on flow cytometry data.
Background
Many diseases are difficult to detect by a single pathway or method. In particular, many serious diseases with high morbidity and mortality, including respiratory/pulmonary diseases, cancer and heart diseases, are difficult to diagnose in early stages with high sensitivity, specificity and high efficiency by a single detection device. Notably, current detection of many important pulmonary diseases (e.g., pneumonia, tuberculosis, severe Acute Respiratory Syndrome (SARS) and coronaviruses) remains ineffective, complex and/or expensive.
In addition, current disease diagnosis techniques typically detect and rely on single macroscopic data and information, such as body temperature, blood pressure, body scan images. For example, each diagnostic instrument currently in common use is mostly an imaging-based device such as an X-ray, CT scan or Nuclear Magnetic Resonance (NMR) imaging technique for detecting a significant disease such as cancer. When these diagnostic instruments are used in combination, diagnosis of diseases is useful to varying degrees. However, when these devices are used individually, accurate, reliable, efficient, and economical detection is not possible at the early stage of the onset of a major disease, and it is difficult to detect multiple types of cancer simultaneously. In addition, many of these existing diagnostic devices are large and invasive, such as X-ray, CT scanning, or Nuclear Magnetic Resonance (NMR) imaging techniques.
Even recently new technologies based on gene detection have emerged, which often rely on single diagnostic techniques, and do not allow comprehensive, reliable, accurate, reliable, and economical detection of large diseases. In recent years, many efforts have been made to apply nanotechnology to various biological fields, and a great deal of work has been focused on gene maps and their minor changes in the field of disease detection. To date, most of the above techniques have been limited to single sensing techniques, using relatively simple, large-sized systems with limited functionality, lack of sensitivity and specificity. Moreover, these prior art techniques require multiple tests to be performed together with multiple devices. These can increase costs and affect sensitivity and specificity.
Therefore, finding a method that can reduce the patient detection flow, improve the accuracy of tumor prediction in digestive systems such as gall bladder and provide effective data support for doctor diagnosis is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present application provides a method for analyzing and processing flow cytometry data, comprising the following steps:
extracting a plurality of said flow cell samples;
detecting an electrical characteristic of the flow cell sample using an electromagnetic excitation source at a plurality of different frequencies within a specific range;
respectively establishing a mapping relation table of the different frequencies and the electrical characteristics;
combining the electrical characteristic mapping tables of the multiple flow cytometry samples to form an electrical characteristic data matrix, and placing the electrical characteristic data under the same frequency excitation in the same dimension;
performing two-dimensional Fourier transform on the data matrix to obtain a corresponding spectrum analysis result;
when the low-frequency component of the longitudinal spectrum of the spectrum analysis result exceeds a threshold value, inputting the two-dimensional graph formed after processing the data matrix into a trained full convolution network, otherwise, directly carrying out weighted average on the longitudinal components of all samples to form the two-dimensional graph and inputting the two-dimensional graph into the trained full convolution network;
and judging the risk level according to the output result of the full convolution network.
Wherein the plurality of different frequency ranges are [10000Hz,300000Hz ].
Wherein the electrical characteristics include phase and amplitude.
Wherein the combined electrical characteristic mapping table of the plurality of flow cytometry samples forms an electrical characteristic data matrix, and the electrical characteristic data under the same frequency excitation are placed in the same dimension, comprising:
the frequencies, phases and amplitudes in the sample test are in one-to-one correspondence, and a mapping relation table is established;
establishing an amplitude characteristic data matrix, placing amplitude characteristic values generated under the excitation of the same frequency in different samples in the same longitudinal dimension, and placing different sample data in different longitudinal dimensions;
and establishing a phase characteristic data matrix, placing phase characteristic values generated under the same frequency excitation in different samples in the same longitudinal dimension, and placing different sample data in different longitudinal dimensions.
Performing two-dimensional Fourier transform on the data matrix, wherein the two-dimensional Fourier transform comprises the step of obtaining the frequency spectrum characteristics of the electrical characteristic data:
where f (x, y) is a spatial data matrix of m×n of the two-dimensional graph, x=0, 1,2, …, M-1 and y=0, 1,2, … N-1, f (u, v) represents fourier transform of f (x, y), which is a frequency domain data matrix of m×n, u=0, 1,2, …, M-1 and v=0, 1,2, … N-1, M is the number of samples, and N is the number of frequency test points.
The spectrum analysis result is filtered by a low-pass filter under the condition that the low-frequency component of the longitudinal spectrum exceeds a threshold value, and the electrical characteristic data of the abnormal sample is required to be oriented when the low-frequency component on the low-pass band exceeds the threshold value, wherein the threshold value is determined according to historical test data.
Wherein, the two-dimensional graph formed after the data matrix is processed comprises: performing longitudinal difference on the electrical characteristic data matrix to obtain a differential electrical characteristic data matrix, solving a longitudinal gradient of the differential matrix to obtain a gradient map, selecting column numbers of all positions where the gradient map has concave outline, searching corresponding columns of the electrical characteristic data matrix, and performing weighted average on the selected columns to obtain a two-dimensional map;
respectively obtaining gradient graphs of an amplitude characteristic data matrix and a phase characteristic data matrix, respectively carrying out weighted average on the selected longitudinal columns in the two data matrices to obtain two one-dimensional longitudinal vectors, inverting the two longitudinal vectors into two one-dimensional transverse vectors, and then combining the two one-dimensional transverse vectors to obtain a two-dimensional graph of N2.
Wherein directly performing weighted average on the longitudinal components of all samples to form a two-dimensional graph comprises: carrying out weighted average on each column of the amplitude characteristic data matrix and the phase characteristic data matrix to obtain a two-dimensional graph; two one-dimensional longitudinal vectors are obtained, inverted into two one-dimensional transverse vectors, and then combined to obtain a two-dimensional diagram of N2.
Wherein, judging the risk level according to the output result of the full convolution network in the off-line stage comprises: training by using marked electrical characteristic data in advance to obtain a full convolution network, wherein the full convolution network can output corresponding risk coefficients;
in the online stage, inputting the two-dimensional graph into the full convolution network and outputting a corresponding risk coefficient;
and combining the risk coefficient and the proportion of the selected column to all the samples in the calculation of the two-dimensional graph to determine the corresponding risk level.
Meanwhile, the application also provides electronic equipment, which comprises a memory and a processor, wherein the processor is used for executing the risk level model stored in the memory so as to realize the analysis processing method based on the flow cytometry data.
Compared with the prior art, the application has the following beneficial effects: when electrical characteristic analysis is carried out on a large number of sample flow cells, the data positions of abnormal cells in mass data are rapidly positioned through frequency domain analysis and gradient analysis, the number of all abnormal samples is obtained, meanwhile, the degree of abnormality and the proportion of the abnormal samples are comprehensively analyzed, and the risk grade is obtained.
Drawings
FIG. 1 is a flow chart of a flow cytometry-based analysis method according to an embodiment of the present application;
fig. 2 is a flowchart of risk level determination according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present application, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It is noted that when an element is referred to as being "fixed" or "disposed on" another element, it can be directly on the other element or be indirectly disposed on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are merely for convenience in describing and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not indicate or imply that the devices or components referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" or "a number" means two or more, unless specifically defined otherwise.
It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for the purpose of understanding and reading the disclosure, and are not intended to limit the scope of the application, which is defined by the claims, but rather by the claims, unless otherwise indicated, and that any structural modifications, proportional changes, or dimensional adjustments, which would otherwise be apparent to those skilled in the art, would be made without departing from the spirit and scope of the application.
As shown in fig. 1, a preferred embodiment of the present application provides a method for analyzing and processing flow cytometry data, which comprises the following steps:
extracting a plurality of said flow cell samples;
detecting an electrical characteristic of the flow cell sample using an electromagnetic excitation source at a plurality of different frequencies within a specific range;
respectively establishing a mapping relation table of the different frequencies and the electrical characteristics;
combining the electrical characteristic mapping tables of the multiple flow cytometry samples to form an electrical characteristic data matrix, and placing the electrical characteristic data under the same frequency excitation in the same dimension;
performing two-dimensional Fourier transform on the data matrix to obtain a corresponding spectrum analysis result;
when the low-frequency component of the longitudinal spectrum of the spectrum analysis result exceeds a threshold value, inputting the two-dimensional graph formed after processing the data matrix into a trained full convolution network, otherwise, directly carrying out weighted average on the longitudinal components of all samples to form the two-dimensional graph and inputting the two-dimensional graph into the trained full convolution network;
and judging the risk level according to the output result of the full convolution network.
In one embodiment, the plurality of different frequency ranges is [10000Hz,300000Hz ].
In one embodiment, the electrical characteristics include phase and amplitude.
In one embodiment, the combined electrical property map of the plurality of flow cytometric samples forms an electrical property data matrix, the electrical property data under the same frequency excitation being placed in the same dimension, comprising:
the frequencies, phases and amplitudes in the sample test are in one-to-one correspondence, and a mapping relation table is established;
establishing an amplitude characteristic data matrix, placing amplitude characteristic values generated under the excitation of the same frequency in different samples in the same longitudinal dimension, and placing different sample data in different longitudinal dimensions;
and establishing a phase characteristic data matrix, placing phase characteristic values generated under the same frequency excitation in different samples in the same longitudinal dimension, and placing different sample data in different longitudinal dimensions.
In one embodiment, performing a two-dimensional fourier transform on the data matrix includes obtaining spectral characteristics of the electrical characteristic data by a two-dimensional fourier transform:
where f (x, y) is a spatial data matrix of m×n of the two-dimensional graph, x=0, 1,2, …, M-1 and y=0, 1,2, … N-1, f (u, v) represents fourier transform of f (x, y), which is a frequency domain data matrix of m×n, u=0, 1,2, …, M-1 and v=0, 1,2, … N-1, M is the number of samples, and N is the number of frequency test points.
In one embodiment, the spectral analysis results exceed a threshold value at low frequency components of the longitudinal spectrum, indicating that there is a large change in electrical characteristics between samples, the spectral analysis results are filtered using a low pass filter, and the electrical characteristic data of the anomalous sample is oriented when the low frequency components at the low pass band exceed the threshold value, the threshold value being determined from historical test data.
The method comprises the steps of carrying out analysis on a frequency spectrum by using fast Fourier transform, wherein the calculation time required by the analysis on the frequency spectrum is short, and simultaneously, whether a situation of mixing normal samples with abnormal samples exists in a large number of samples can be rapidly judged, because when the longitudinal data of the samples are mixed with a large number of samples, the situation that a data matrix is separated from a normal data band obviously occurs in an experiment is found, the situation can be screened out through a low pass band, and thus, when unified analysis is carried out on normal cell data and abnormal cell data, a larger error is caused on the result.
In one embodiment, the two-dimensional map formed after processing the data matrix includes: performing longitudinal difference on the electrical characteristic data matrix to obtain a differential electrical characteristic data matrix, solving a longitudinal gradient of the differential matrix to obtain a gradient map, selecting column numbers of all positions where the gradient map has concave outline, searching corresponding columns of the electrical characteristic data matrix, and performing weighted average on the selected columns to obtain a two-dimensional map;
because the resistance-capacitance characteristic of the abnormal electrical characteristic data is increased, the amplitude phase of the abnormal electrical characteristic data is shifted downwards, and according to the characteristic, the abnormal electrical characteristic data band is positioned at the concave outline in the gradient diagram corresponding to the matrix, so that all abnormal data column sequences can be rapidly positioned through all the concave outlines in the gradient diagram, then the original data position of the data matrix is searched, and meanwhile, the abnormal data number and the proportion of the abnormal data number to all samples can be obtained.
Respectively obtaining gradient graphs of an amplitude characteristic data matrix and a phase characteristic data matrix, respectively carrying out weighted average on the selected longitudinal columns in the two data matrices to obtain two one-dimensional longitudinal vectors, inverting the two longitudinal vectors into two one-dimensional transverse vectors, and then combining the two one-dimensional transverse vectors to obtain a two-dimensional graph of N2.
In one embodiment, directly weighted averaging the longitudinal components of all samples to form a two-dimensional map includes: carrying out weighted average on each column of the amplitude characteristic data matrix and the phase characteristic data matrix to obtain a two-dimensional graph; two one-dimensional longitudinal vectors are obtained, inverted into two one-dimensional transverse vectors, and then combined to obtain a two-dimensional diagram of N2.
In one embodiment, determining the risk level based on the output of the full convolutional network comprises: in an offline stage, training is carried out by using marked electrical characteristic data in advance to obtain a full convolution network, and the full convolution network can output a corresponding risk coefficient;
in the online stage, inputting the two-dimensional graph into the full convolution network and outputting a corresponding risk coefficient;
and combining the risk coefficient and the proportion of the selected column to all the samples in the calculation of the two-dimensional graph to determine the corresponding risk level.
If the difference of the sample data is very small, it indicates that the characteristics of cells of all samples are consistent, and the risk coefficient can be obtained by using weighted average of all samples, but when the difference is very large, the result is larger error by using the weighted of all samples, so that the abnormal data needs to be screened out, but if the abnormal data only occupies very low proportion of all samples, the risk of the sample is not very high, the proportion of the risk sample and the risk coefficient need to be combined to obtain the overall risk level, for example, the proportion coefficient can be determined for different proportion ranges in advance, and the final risk level can be obtained by multiplying the risk coefficient by the proportion coefficient.
Compared with the prior art, the application has the following beneficial effects: when electrical characteristic analysis is carried out on a large number of sample flow cells, the data positions of abnormal cells in mass data are rapidly positioned through frequency domain analysis and gradient analysis, the number of all abnormal samples is obtained, meanwhile, the degree of abnormality and the proportion of the abnormal samples are comprehensively analyzed, and the risk grade is obtained.
The application also provides electronic equipment, which comprises a memory and a processor, wherein the processor is used for executing the risk level model stored in the memory so as to realize the analysis processing method based on the flow cytometry data.
Any reference to memory, storage, database, or other medium used in the present application may include non-volatile and/or volatile memory. Suitable nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The analysis processing method based on the flow cytometry data is characterized by comprising the following steps:
extracting a plurality of said flow cell samples;
detecting an electrical characteristic of the flow cell sample using an electromagnetic excitation source at a plurality of different frequencies within a specific range;
respectively establishing a mapping relation table of the different frequencies and the electrical characteristics;
combining the electrical characteristic mapping tables of the multiple flow cytometry samples to form an electrical characteristic data matrix, and placing the electrical characteristic data under the same frequency excitation in the same dimension;
performing two-dimensional Fourier transform on the data matrix to obtain a corresponding spectrum analysis result;
when the low-frequency component of the longitudinal spectrum of the spectrum analysis result exceeds a threshold value, inputting the two-dimensional graph formed after processing the data matrix into a trained full convolution network, otherwise, directly carrying out weighted average on the longitudinal components of all samples to form the two-dimensional graph and inputting the two-dimensional graph into the trained full convolution network;
judging the risk level according to the output result of the full convolution network;
the two-dimensional graph formed after the data matrix is processed comprises: performing longitudinal difference on the electrical characteristic data matrix to obtain a differential electrical characteristic data matrix, solving a longitudinal gradient of the differential matrix to obtain a gradient map, selecting column numbers of all positions where the gradient map has concave outline, searching corresponding columns of the electrical characteristic data matrix, and performing weighted average on the selected columns to obtain a two-dimensional map;
the method comprises the steps of respectively obtaining gradient graphs of an amplitude characteristic data matrix and a phase characteristic data matrix, respectively carrying out weighted average on selected columns in the two data matrices to obtain two one-dimensional longitudinal vectors, inverting the two longitudinal vectors into two one-dimensional transverse vectors, and then combining the two one-dimensional transverse vectors to obtain a two-dimensional graph of N2.
2. The method of claim 1, wherein the plurality of different frequency ranges are [10000hz,300000hz ].
3. The method of claim 1, wherein the combining the electrical characteristic maps of the plurality of flow cytometry samples to form an electrical characteristic data matrix, the electrical characteristic data under the same frequency excitation being placed in the same dimension, comprises:
the frequencies, phases and amplitudes in the sample test are in one-to-one correspondence, and a mapping relation table is established;
establishing an amplitude characteristic data matrix, placing amplitude characteristic values generated under the excitation of the same frequency in different samples in the same longitudinal dimension, and placing different sample data in different longitudinal dimensions;
and establishing a phase characteristic data matrix, placing phase characteristic values generated under the same frequency excitation in different samples in the same longitudinal dimension, and placing different sample data in different longitudinal dimensions.
4. A method of analyzing and processing flow cytometry data as claimed in claim 3 wherein performing a two-dimensional fourier transform on the data matrix comprises obtaining spectral characteristics of the electrical characteristic data by a two-dimensional fourier transform:
where f (x, y) is a spatial data matrix of m×n of the two-dimensional graph, x=0, 1,2, …, M-1 and y=0, 1,2, … N-1, f (u, v) represents fourier transform of f (x, y), which is a frequency domain data matrix of m×n, u=0, 1,2, …, M-1 and v=0, 1,2, … N-1, M is the number of samples, and N is the number of frequency test points.
5. The method according to claim 1, wherein the spectral analysis result is filtered by a low-pass filter when the low-frequency component of the longitudinal spectrum exceeds a threshold value, which is determined according to the historical test data, and the spectral analysis result is filtered by a low-pass filter when the low-frequency component of the low-pass band exceeds the threshold value.
6. The method of claim 1, wherein directly weighted averaging longitudinal components of all samples to form a two-dimensional map comprises: carrying out weighted average on each column of the amplitude characteristic data matrix and the phase characteristic data matrix to obtain a two-dimensional graph; two one-dimensional longitudinal vectors are obtained, inverted into two one-dimensional transverse vectors, and then combined to obtain a two-dimensional diagram of N2.
7. The method for analyzing and processing flow cytometry data according to claim 1, wherein determining the risk level according to the output result of the full convolution network in the offline stage comprises: training by using marked electrical characteristic data in advance to obtain a full convolution network, wherein the full convolution network can output corresponding risk coefficients;
in the online stage, inputting the two-dimensional graph into the full convolution network and outputting a corresponding risk coefficient;
and combining the risk coefficient and the proportion of the selected column to all the samples in the calculation of the two-dimensional graph to determine the corresponding risk level.
8. An electronic device comprising a memory and a processor for executing a computer executable program stored in the memory to implement a method of flow cytometry data-based analysis processing as described in any one of claims 1-7.
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| 微流控阻抗流式细胞仪在单细胞检测中的应用;冯迪 等;《化学进展》;第33卷(第4期);第555-567页 * |
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