A system and method for deep analysis of same-piece bipolar single-cell metabolomics

Abstract

This invention relates to the field of biological data processing technology in computational molecular biology, and discloses a bipolar single-cell metabolomics deep analysis system and method, comprising: a data dimensionality reduction and encapsulation unit that encapsulates the raw data stream into algorithm-processed data; a companion affinity map matrix generation unit that generates a companion affinity map matrix based on the algorithm-processed data; a cross-gated in-situ hedging reconstruction unit that establishes a relative response balance scale and calls a step-by-step operator to increase the feedforward dynamic compensation window to output a bipolar collaborative time series matrix when its absolute value exceeds a predetermined threshold of 0.12; and a metabolic network topology calculation unit that calculates the metabolic network topology. This invention eliminates nonlinear time distortion caused by cell ex vivo dissipation, avoids mismatch artifacts generated by cross-polar data splicing, and avoids the risk of losing low-abundance molecular feature data.

Description

Technical Field

[0001] This invention relates to a bipolar single-cell metabolomics deep analysis system and method, belonging to the field of biological data processing technology in computational molecular biology. Background Technology

[0002] In current computational molecular biology single-cell phenotypic analysis systems, concurrently acquiring positive and negative bipolar molecular flow characteristic data from the same slice of single cells is a fundamental step in constructing feature maps. Current techniques primarily rely on setting up discrete signal transmission channels at the chip front end to separately capture high-dimensional data streams of molecular fragments with positive and negative ionization tendencies. Feature extraction and data matrix construction are performed independently within each loop, and statistical aggregation is achieved through back-end alignment algorithms during the data aggregation stage. This discrete processing architecture provides basic data support for routine molecular screening. However, in the continuous in vitro capture of bipolar single cells from the same slice… Under operating conditions, the front-end sensing environment is easily affected by transient dissipation disturbances in vitro. Different phenotypic components within the cell exhibit essential heterogeneous dissipation patterns on a microsecond scale. At the same time, positive and negative polar ions face different physical migration resistances and space charge aggregation effects when propelling within the fluid channel, resulting in nonlinear temporal phase asymmetry and dynamic response amplitude drift between the characteristic data streams output from the two channels. Traditional processing methods, lacking the means to perceive the transient spatiotemporal dissipation mechanism, use a static threshold shearing model to isolate and intercept the two signals, leading to logical divergence of the feature alignment reference point.

[0003] To address timing distortions caused by mass transfer resistance, the industry's intuitive approach is to forcibly align sampling periods by increasing the hardware clock trigger frequency, or to suppress noise generated by response drift by expanding the static filter cutoff threshold. However, the charge release limit during channel switching restricts the improvement of synchronization accuracy, while expanding the cutoff threshold inevitably leads to the indiscriminate interception and deletion of trace low-abundance component feature signals, causing irreversible information annihilation of high-value weak signal features before comparison convergence. While improvements to the front-end hardware face physical limitations, back-end data processing methods also have shortcomings, making it difficult to provide phase adaptive correction in fluid dynamic evolution scenarios. For example, Chinese invention patent application with publication number CN120374399A... A single-cell metabolomics analysis method based on mass spectrometry is disclosed, which attempts to reconstruct the structure of low-resolution spectra of single cells by training a two-dimensional super-resolution model using population cell data. The underlying premise of this technique implicitly depends on the relative stability of static spectra within a specific mass range and the prior knowledge of large sample spectral shapes. However, the continuous bipolar capture of single cells in vitro faces microsecond-scale channel mass transfer delays, fluid heterogeneous dissipation, and nonlinear dynamic temporal distortion between the two feature flows. There is a fundamental mismatch between the underlying premise and the actual boundary conditions between the super-resolution strategy based on static image similarity loss and the highly dynamic heterogeneous bipolar feature channels, resulting in the inability to perceive and offset transient temporal deviations, and causing weak signal annihilation at the convergence point of cross-polarity comparison.

[0004] Therefore, given the long-standing nature of the aforementioned contradictions, the technical problem to be solved by this invention is how to achieve active feedforward reprogramming and dynamic phase drift offsetting by utilizing the correlation mechanism of multi-channel heterogeneous feature data streams while ensuring the topological integrity of low-abundance molecular features. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A bipolar single-cell metabolomics deep analysis system, the system comprising: The data dimensionality reduction and encapsulation unit is used to remove business labels from the original positive and negative ion high-dimensional tensor data stream and encapsulate it into data for algorithm processing. The associated affinity map matrix generation unit, connected to the data dimensionality reduction and encapsulation unit, is used to generate the associated affinity map matrix based on data processing according to the algorithm. The cross-gated in-situ counter-current reconstruction unit, connected to both the associated affinity map matrix generation unit and the data dimensionality reduction and encapsulation unit, is used to calculate the ratio of the peak intensity difference to the sum of the original positive and negative ion mass spectra within the current time-series unit to establish the relative response equilibrium scale. The relative response equilibrium scale is determined according to the following mathematical expression: Where R is the relative response balance scale, This represents the peak intensity of the original positive ion mass spectrum within the current time unit. The peak intensity of the original negative ion mass spectrum within the current time unit is used. When the absolute value of the relative response balance scale exceeds the determined threshold value of 0.12, adaptive degradation optimization is initiated. The step-by-step operator is called to increase the control boundary width of the feedforward dynamic compensation window to compensate for the time phase shift. At the same time, the local density of the manifold of the associated affinity map spectrum matrix within the current sampling period is scanned. The update weight matrix of the data truncation correction operator is allocated according to the local density gradient. The shearing weight of the original heterogeneous data stream is adjusted by updating the weight matrix to complete the active feedforward topology reconstruction and output the bipolar cooperative time series matrix. The metabolic network topology calculation unit is connected to the cross-gated in-situ hedging reconstruction unit and is used to perform metabolic network topology calculations based on the bipolar cooperative time series matrix.

[0006] Preferably, when the control boundary width of the feedforward dynamic compensation window is increased, the cross-gated in-situ hedging reconstruction unit performs the following processing: The control boundary width of the feedforward dynamic compensation window is limited to follow a horizontal linear rule to exhibit discrete step evolution; the control boundary width is determined according to the following mathematical expression: ,in, The control boundary width of the feedforward dynamic compensation window. The static reference window width factor is 8μs. The linear adjustment weighting factor was set to 0.25. The absolute value of the relative response balance scale is used; the optimization step is constrained by a fixed static reference window width coefficient of 8μs and a set linear adjustment weighting factor of 0.25. A feedforward dynamic compensation window for compensating for mass transfer delay and timing phase shift is constructed in the internal storage space to limit the convergence threshold of the nonlinear feature decoupling operator.

[0007] Preferably, when the data dimensionality reduction and encapsulation unit performs dimensionality reduction and encapsulation on the original positive and negative ion high-dimensional tensor data stream, it performs the following processing: when the original positive and negative ion high-dimensional tensor data stream is acquired at the data receiving port, the sample number and collection batch contained in the original positive and negative ion high-dimensional tensor data stream are stripped to remove the business tags; the original high-dimensional tensor after removing the business tags is recombined into a continuous single-cell temporal feature sparse matrix stream through multivariate linear model dimensionality reduction, and is output to the companion affinity map matrix generation unit as the algorithm processing data.

[0008] Preferably, the associated affinity map matrix generation unit performs the following processing when generating the associated affinity map matrix: extracting the heterogeneous molecular feature data streams of the positive ionization polarity mass spectrum feature component set and the negative ionization polarity mass spectrum feature component set from the algorithm-processed data; calculating the spatial manifold overlap and distance measure between the positive and negative ion mass spectrum feature components in adjacent sampling periods in the absence of an external clock synchronization signal, and generating the associated affinity map matrix to eliminate time axis offset and signal nonlinear attenuation.

[0009] Preferably, when the cross-gated in-situ hedging reconstruction unit adjusts the shear weights of the original heterogeneous data stream by updating the weight matrix, it performs the following processing: identifying the response nonlinear decay region caused by the difference in ionization efficiency; and allocating the updated weight matrix of the data truncation correction operator according to the density gradient of the response nonlinear decay region, so that the shear weights of the high-density region are increased and the shear weights of the low-density region are decreased.

[0010] Preferably, the metabolic network topology calculation unit includes a topology decoupling module, wherein the topology decoupling module is used to receive a bipolar coordinating time series matrix, and map the decoupled positive and negative polarity coordinating feature components to a single-cell metabolic pathway network model, so as to solve and output a metabolic network topology diagram without intensity coverage interference.

[0011] Preferably, the system further includes a historical evolution evaluation unit, which is used to predict the long-term trend of the relative response balance scale to evaluate the operational stability of the system. The historical evolution evaluation unit performs the following processing: continuously recording the values ​​of the relative response balance scale within the historical sampling period to construct a long-term time series reflecting the evolution of the ion mass spectrometry channel state over time; calculating the first-order time derivative of the long-term time series to quantify the response characteristic shift rate of the bipolar data acquisition channel; and generating and outputting a calibration warning signal indicating that the data channel deviates from the steady state when the response characteristic shift rate is continuously not lower than a set variation threshold; wherein, the set variation threshold is twice the preset steady-state reference shift rate.

[0012] Preferably, the system further includes a microfluidic chip substrate with a co-chip dual-channel concurrent sampling structure, which is used to simultaneously capture intracellular positive ionization polarity component data stream and negative ionization polarity component data stream; the data dimensionality reduction packaging unit is connected to the data transmission port of the co-chip dual-channel concurrent sampling structure, wherein the data acquisition alternation period of the system is set to 10μs to 50μs.

[0013] Preferably, the system further includes a global adaptive calibration unit, which is connected to the data dimensionality reduction and encapsulation unit and the metabolic network topology calculation unit, respectively. After completing a single metabolic network topology calculation, the unit feeds back the topology convergence residual to the data dimensionality reduction and encapsulation unit to dynamically update the baseline feature weights in the dimensionality reduction of the multivariate linear model and achieve closed-loop error compensation. Specifically, the topology convergence residual is the quality balance residual of each pathway node in the single-cell metabolic pathway network model calculated by the graph convolution information flow algorithm. The global adaptive calibration unit feeds back the quality balance residual as the closed-loop error compensation amount to the data dimensionality reduction and encapsulation unit to dynamically update the baseline feature weights in the dimensionality reduction of the multivariate linear model.

[0014] A method for in-slice bipolar single-cell metabolomics deep analysis, implemented in a bipolar single-cell metabolomics deep analysis system, includes the following steps: Step S101: The data dimensionality reduction and encapsulation unit removes the business tags from the original positive and negative ion high-dimensional tensor data stream and encapsulates it into algorithm processing data. During dimensionality reduction and encapsulation, the original high-dimensional tensor after removing the business tags is input into the dimensionality reduction operation module to calculate the covariance matrix of the ion intensity of each feature channel in adjacent scanning cycles to extract the principal component feature vectors. Sparse redundant components with feature values ​​lower than the preset background noise baseline variance are removed. The original high-dimensional tensor is linearly projected and transformed using the retained core principal component load matrix to reorganize it into a continuous single-cell temporal feature sparse matrix stream. Step S102: The associated affinity map matrix generation unit maps the positive and negative polarity components and generates the associated affinity map matrix by processing the data according to the algorithm and through the multi-head spatial cross attention mechanism. Step S103: The cross-gated in-situ counter-current reconstruction unit calculates the ratio of the peak intensity difference to the sum of the original positive and negative ion mass spectra within the current time unit to establish the relative response balance scale. The relative response balance scale is determined according to the following mathematical expression: Where R is the relative response balance scale, This represents the peak intensity of the original positive ion mass spectrum within the current time unit. This represents the peak intensity of the original negative ion mass spectrum within the current time unit. Step S104, on the relative response balance scale absolute value When the threshold value of 0.12 is exceeded, the cross-gated in-situ offset reconstruction unit initiates adaptive degradation optimization, and calls the step-type stepping operator to increase the control boundary width of the feedforward dynamic compensation window to compensate for the timing phase offset. The control boundary width is determined according to the following mathematical expression: ,in, The control boundary width of the feedforward dynamic compensation window. The static reference window width factor is 8μs. The linear adjustment weighting factor was set to 0.25. The absolute value of the relative response balance scale; Step S105: The cross-gated in-situ hedging reconstruction unit scans the manifold local density of the associated affinity map matrix in the current sampling period, allocates the update weight matrix of the data truncation correction operator according to the local density gradient, adjusts the shearing weight of the original heterogeneous data stream through the update weight matrix to complete the active feedforward topology reconstruction, and outputs the bipolar cooperative time series matrix. Step S106: The metabolic network topology calculation unit performs metabolic network topology calculation based on the bipolar cooperative time series matrix.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the in-depth analysis of bipolar single-cell metabolomics on the same chip, the heterogeneous multidimensional tensor in-situ access bus converts the original positive ion high-dimensional tensor data stream captured by the first feature channel and the original negative ion high-dimensional tensor data stream captured concurrently by the second feature channel into discrete state space vectors. The associated affinity map matrix generation unit calculates the mutual information entropy gradient of the vector within a preset sliding time window to obtain the associated affinity map matrix. The implicit temporal correlation characteristics between bipolar high-dimensional data components are characterized by the topological nodes in the matrix, eliminating the dependence on external hardware clock synchronization signals. This offsets the nonlinear time distortion caused by cell ex vivo dissipation within the information space and avoids mismatch artifacts caused by hard splicing of transpolar biomarker data.

[0016] 2. The cross-gated in-situ counter-shearing reconstruction unit determines the matrix weighting value of the in-situ shearing operator based on the spatial manifold density distribution. It establishes the relative response balance scale by calculating the ratio of the difference to the sum of the peak intensities of the original positive and negative ion mass spectra within the current time series unit. It identifies the intensity asymmetry between positive and negative ions induced by the difference in intrinsic ionization efficiency in real time, dynamically reshapes the heterogeneous data flow path and feature component extraction weights, changes the indiscriminate truncation of low-abundance signals by static threshold filtering, and isolates high-abundance noise interference in the trace metabolite feature data stream at the cross-polarity comparison convergence point, avoiding network calculation divergence caused by the intensity coverage of low-abundance features.

[0017] 3. When the absolute value of the relative response balance scale exceeds the preset discrete safety threshold of 0.12, the cross-gated in-situ hedging reconstruction unit starts an adaptive degradation optimization strategy. By calling the step-type stepping operator to increase the control boundary width of the feedforward dynamic compensation window, the control boundary width follows the horizontal linear rule of the single-layer subscript format to present discrete step evolution. The fixed static reference window width coefficient of 8μs and the preset linear adjustment weighting factor of 0.25 are used to constrain the optimization step. A dynamic compensation window for hedging ion mass transfer resistance and phase drift is constructed inside the information storage space to limit the convergence threshold of the nonlinear feature decoupling operator. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the logical execution of a bipolar single-cell metabolomics deep analysis method for the same slice according to the present invention. Figure 2 This is a data flow architecture diagram of a bipolar single-cell metabolomics deep analysis system for the same slice according to the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] A bipolar single-cell metabolomics deep analysis system, comprising: The data dimensionality reduction and encapsulation unit is used to remove business labels from the original positive and negative ion high-dimensional tensor data stream and encapsulate it into data for algorithm processing. The associated affinity map matrix generation unit, connected to the data dimensionality reduction and encapsulation unit, is used to generate the associated affinity map matrix based on data processing according to the algorithm. The cross-gated in-situ counter-current reconstruction unit, connected to both the associated affinity map matrix generation unit and the data dimensionality reduction and encapsulation unit, is used to calculate the ratio of the peak intensity difference to the sum of the original positive and negative ion mass spectra within the current time-series unit to establish the relative response equilibrium scale. The relative response equilibrium scale is determined according to the following mathematical expression: Where R is the relative response balance scale, This represents the peak intensity of the original positive ion mass spectrum within the current time unit. The peak intensity of the original negative ion mass spectrum within the current time unit is used. When the absolute value of the relative response balance scale exceeds the determined threshold value of 0.12, adaptive degradation optimization is initiated. The step-by-step operator is called to increase the control boundary width of the feedforward dynamic compensation window to compensate for the time phase shift. At the same time, the local density of the manifold of the associated affinity map spectrum matrix within the current sampling period is scanned. The update weight matrix of the data truncation correction operator is allocated according to the local density gradient. The shearing weight of the original heterogeneous data stream is adjusted by updating the weight matrix to complete the active feedforward topology reconstruction and output the bipolar cooperative time series matrix. The metabolic network topology calculation unit is connected to the cross-gated in-situ hedging reconstruction unit and is used to perform metabolic network topology calculations based on the bipolar cooperative time series matrix.

[0022] Preferably, when the control boundary width of the feedforward dynamic compensation window is increased, the cross-gated in-situ hedging reconstruction unit performs the following processing: The control boundary width of the feedforward dynamic compensation window is limited to follow a horizontal linear rule to exhibit discrete step evolution; the control boundary width is determined according to the following mathematical expression: ,in, The control boundary width of the feedforward dynamic compensation window. The static reference window width factor is 8μs. The linear adjustment weighting factor was set to 0.25. The absolute value of the relative response balance scale is used; the optimization step is constrained by a fixed static reference window width coefficient of 8μs and a set linear adjustment weighting factor of 0.25. A feedforward dynamic compensation window for compensating for mass transfer delay and timing phase shift is constructed in the internal storage space to limit the convergence threshold of the nonlinear feature decoupling operator.

[0023] Preferably, when the data dimensionality reduction and encapsulation unit performs dimensionality reduction and encapsulation on the original positive and negative ion high-dimensional tensor data stream, it performs the following processing: when the original positive and negative ion high-dimensional tensor data stream is acquired at the data receiving port, the sample number and collection batch contained in the original positive and negative ion high-dimensional tensor data stream are stripped to remove the business tags; the original high-dimensional tensor after removing the business tags is recombined into a continuous single-cell temporal feature sparse matrix stream through multivariate linear model dimensionality reduction, and is output to the companion affinity map matrix generation unit as the algorithm processing data.

[0024] Preferably, the associated affinity map matrix generation unit performs the following processing when generating the associated affinity map matrix: extracting the heterogeneous molecular feature data streams of the positive ionization polarity mass spectrum feature component set and the negative ionization polarity mass spectrum feature component set from the algorithm-processed data; calculating the spatial manifold overlap and distance measure between the positive and negative ion mass spectrum feature components in adjacent sampling periods in the absence of an external clock synchronization signal, and generating the associated affinity map matrix to eliminate time axis offset and signal nonlinear attenuation.

[0025] Preferably, when the cross-gated in-situ hedging reconstruction unit adjusts the shear weights of the original heterogeneous data stream by updating the weight matrix, it performs the following processing: identifying the response nonlinear decay region caused by the difference in ionization efficiency; and allocating the updated weight matrix of the data truncation correction operator according to the density gradient of the response nonlinear decay region, so that the shear weights of the high-density region are increased and the shear weights of the low-density region are decreased.

[0026] Preferably, the metabolic network topology calculation unit includes a topology decoupling module, wherein the topology decoupling module is used to receive a bipolar coordinating time series matrix, and map the decoupled positive and negative polarity coordinating feature components to a single-cell metabolic pathway network model, so as to solve and output a metabolic network topology diagram without intensity coverage interference.

[0027] Preferably, the system further includes a historical evolution evaluation unit, which is used to predict the long-term trend of the relative response balance scale to evaluate the operational stability of the system. The historical evolution evaluation unit performs the following processing: continuously recording the values ​​of the relative response balance scale within the historical sampling period to construct a long-term time series reflecting the evolution of the ion mass spectrometry channel state over time; calculating the first-order time derivative of the long-term time series to quantify the response characteristic shift rate of the bipolar data acquisition channel; and generating and outputting a calibration warning signal indicating that the data channel deviates from the steady state when the response characteristic shift rate is continuously not lower than a set variation threshold; wherein, the set variation threshold is twice the preset steady-state reference shift rate.

[0028] Preferably, the system further includes a microfluidic chip substrate with a co-chip dual-channel concurrent sampling structure, which is used to simultaneously capture intracellular positive ionization polarity component data stream and negative ionization polarity component data stream; the data dimensionality reduction packaging unit is connected to the data transmission port of the co-chip dual-channel concurrent sampling structure, wherein the data acquisition alternation period of the system is set to 10μs to 50μs.

[0029] Preferably, the system further includes a global adaptive calibration unit, which is connected to the data dimensionality reduction and encapsulation unit and the metabolic network topology calculation unit, respectively. After completing a single metabolic network topology calculation, the unit feeds back the topology convergence residual to the data dimensionality reduction and encapsulation unit to dynamically update the baseline feature weights in the dimensionality reduction of the multivariate linear model and achieve closed-loop error compensation. Specifically, the topology convergence residual is the quality balance residual of each pathway node in the single-cell metabolic pathway network model calculated by the graph convolution information flow algorithm. The global adaptive calibration unit feeds back the quality balance residual as the closed-loop error compensation amount to the data dimensionality reduction and encapsulation unit to dynamically update the baseline feature weights in the dimensionality reduction of the multivariate linear model.

[0030] A method for in-slice bipolar single-cell metabolomics deep analysis, implemented in a bipolar single-cell metabolomics deep analysis system, includes the following steps: Step S101: The data dimensionality reduction and encapsulation unit removes the business tags from the original positive and negative ion high-dimensional tensor data stream and encapsulates it into algorithm processing data. During dimensionality reduction and encapsulation, the original high-dimensional tensor after removing the business tags is input into the dimensionality reduction operation module to calculate the covariance matrix of the ion intensity of each feature channel in adjacent scanning cycles to extract the principal component feature vectors. Sparse redundant components with feature values ​​lower than the preset background noise baseline variance are removed. The original high-dimensional tensor is linearly projected and transformed using the retained core principal component load matrix to reorganize it into a continuous single-cell temporal feature sparse matrix stream. Step S102: The associated affinity map matrix generation unit maps the positive and negative polarity components and generates the associated affinity map matrix by processing the data according to the algorithm and through the multi-head spatial cross attention mechanism. Step S103: The cross-gated in-situ counter-current reconstruction unit calculates the ratio of the peak intensity difference to the sum of the original positive and negative ion mass spectra within the current time unit to establish the relative response balance scale. The relative response balance scale is determined according to the following mathematical expression: Where R is the relative response balance scale, This represents the peak intensity of the original positive ion mass spectrum within the current time unit. This represents the peak intensity of the original negative ion mass spectrum within the current time unit. Step S104, on the relative response balance scale absolute value When the threshold value of 0.12 is exceeded, the cross-gated in-situ offset reconstruction unit initiates adaptive degradation optimization, and calls the step-type stepping operator to increase the control boundary width of the feedforward dynamic compensation window to compensate for the timing phase offset. The control boundary width is determined according to the following mathematical expression: ,in, The control boundary width of the feedforward dynamic compensation window. The static reference window width factor is 8μs. The linear adjustment weighting factor was set to 0.25. The absolute value of the relative response balance scale; Step S105: The cross-gated in-situ hedging reconstruction unit scans the manifold local density of the associated affinity map matrix in the current sampling period, allocates the update weight matrix of the data truncation correction operator according to the local density gradient, adjusts the shearing weight of the original heterogeneous data stream through the update weight matrix to complete the active feedforward topology reconstruction, and outputs the bipolar cooperative time series matrix. Example 1: In the continuous operation of high-dimensional data stream deep analysis of bipolar single-cell metabolomics, the positively and negatively ionized metabolite aggregates within the single cell undergo microsecond-scale transient nonlinear network dissipation after detaching from their native microenvironment. Furthermore, positive and negative polar ions face intrinsic heterogeneity-based physical migration resistance and space charge clustering effects as they propagate within the fluid channels. This results in high-frequency dynamic phase deflection and nonlinear response amplitude drift between the high-dimensional tensor data stream of original positive ions captured by the first feature channel and the high-dimensional tensor data stream of original negative ions concurrently captured by the second feature channel. Traditional post-analysis methods... The current alignment method employs a static threshold shearing model to isolate and intercept the two signals. This not only fails to track microsecond-level transient phase distortions, introducing numerous spurious correlation artifacts caused by temporal mismatches, but also causes trace low-abundance molecular feature data components to encounter high-abundance noise at the alignment convergence point, leading to computational divergence. The data dimensionality reduction and encapsulation unit acquires the original high-dimensional tensor data streams of positive and negative ions at the data receiving port, removes sample numbers and collection batches to remove business tags, and reassembles the original high-dimensional tensors into continuous single-cell temporal features using a multivariate linear model. The sparse matrix stream is used as the data output of the algorithm. Specifically, the dimensionality reduction steps of the multivariate linear model are as follows: the original high-dimensional mass spectrometry tensor of positive and negative ions after stripping business labels is input into the dimensionality reduction operation module to calculate the covariance matrix of the ion intensity of each feature channel in adjacent scan cycles, and the principal component eigenvectors representing the spatial distribution of the data are extracted. Sparse redundant components with eigenvalues ​​lower than the preset background noise baseline variance are removed. The original high-dimensional tensor is then subjected to linear projection transformation using the retained core principal component loading matrix, which is compressed and recombined into a dense and continuous low-dimensional single-cell temporal feature sparse matrix stream, serving as the unified standard for downstream core algorithms. The data object output unit, which generates the associated affinity map matrix, is connected to the data dimensionality reduction and encapsulation unit and receives data processed by the algorithm. At the instant when the data crosses the input boundary, it extracts the set of positive ion polarity mass spectrum feature components and the set of negative ion polarity mass spectrum feature components. In the absence of an external clock synchronization signal, it calculates the spatial manifold overlap and distance measure between positive and negative ion mass spectrum feature components in adjacent sampling periods and outputs a globally unique associated affinity map matrix. The topological nodes in the associated affinity map matrix characterize the implicit temporal correlation feature degree between bipolar high-dimensional data components, eliminating the dependence on external hardware clock synchronization signals.Since the nonlinear transient dissipation after cell ex vivo and the difference in fluid resistance between the two ion streams in the channel are objective physical events, the processing logic of this algorithm is not to reverse or physically reverse the ion mass transfer loss and time distortion that have already occurred. Instead, based on the principle of manifold topological invariance, it extracts the inherent structural correlation in the asynchronous data stream within the pure digital information space. By calculating the spatial overlap of feature components in adjacent periods, it transforms the nonlinear time axis mapping relationship with physical phase deflection into a relative topological distance measure in the standardized manifold space. This provides a virtual feature alignment benchmark with topological stability, independent of the absolute hardware physical clock, in the subsequent map reconstruction calculation, achieving structural hedging at the information level. The cross-gated in-situ hedging reconstruction unit is connected to the associated affinity map matrix generation unit and the data dimensionality reduction and encapsulation unit, respectively. It scans the local density of the associated affinity map matrix in the current sampling period, identifies the nonlinear decay region of the response caused by the difference in ionization efficiency, and allocates data truncation according to the density gradient of the nonlinear decay region of the response. The updated weight matrix of the correction operator increases the shear weight in high-density regions and decreases the shear weight in low-density regions. Simultaneously, it calculates the ratio of the difference in peak intensity to the sum of the original positive and negative ion mass spectra within the current time unit to establish a relative response balance scale. It also identifies the amplitude intensity asymmetry induced by intrinsic ionization efficiency differences between positive and negative ions in real time, dynamically reshaping the heterogeneous data flow path and feature component extraction weights. Furthermore, the allocation logic of the updated weight matrix of the data truncation correction operator is based on the gradient distribution of local manifold density. When the local manifold density is in a high-value range, indicating the presence of high-abundance principal component ion response saturation, the operator assigns an exponentially amplified shear weight greater than 1 to forcibly suppress and eliminate the trailing artifacts caused by excessively high ionization efficiency. Conversely, when the manifold density is in a low-value sparse range, indicating the presence of low-abundance trace feature signals, the operator assigns a linear proportional shear weight between 0 and 1, thus actively preserving weak mass spectrometry peak signal streams during information flow, changing the indiscriminate removal of trace substances by traditional static interception.

[0031] When the absolute value of the relative response balance scale exceeds a predetermined threshold of 0.12, the cross-gated in-situ hedging reconstruction unit initiates an adaptive degradation optimization strategy. It calls a stepped step operator to increase the control boundary width of the feedforward dynamic compensation window, limiting the control boundary width of the feedforward dynamic compensation window to follow a horizontal linear rule in a single-layer subscript format, exhibiting discrete step evolution. The optimization step is constrained by a fixed static reference window width coefficient of 8μs and a set linear adjustment weighting factor of 0.25. This is determined by multiplying the fixed static reference window width coefficient by the sum of the products containing the value 1 and the linear adjustment weighting factor, along with the absolute value of the relative response balance scale. This is then stored within the internal memory space. A feedforward dynamic compensation window is established to offset mass transfer delay and timing phase shift, limiting the convergence boundary of the nonlinear feature decoupling operator and the feature component extraction weights. Relying on the feedforward dynamic compensation window to offset timing phase shift and output a bipolar collaborative time series matrix, the system improves operational stability under varying operating conditions. It should be noted that the 10-microsecond to 50-microsecond data acquisition alternation period set by the system refers to the hardware physical circuit switching interval for the dual-channel microfluidic chip to switch between positive and negative ionization polarities at the macroscopic electronic control level. The feedforward dynamic compensation window control boundary width, calculated by formula and ranging from 8 to 9.9 microseconds, is specifically designed to eliminate and absorb the effects of mass transfer delay and timing phase shift. The microscopic mass transfer delay, ranging from 0 to 10 microseconds, occurs within the independent microfluidic channels of two ions due to differences in mass-to-charge ratio and asymmetry in physical resistance. This microscopic mass transfer phase deflection occurs entirely within the period of macroscopic polarity alternation sampling. Therefore, limiting the control boundary width to within 9.9 microseconds enables spatiotemporal multi-scale continuity nesting between microscopic phase deflection and macroscopic acquisition frequency, ensuring high-precision in-situ offsetting of data before network computation mapping. Limiting the data acquisition alternation period to between 10 μs and 50 μs is technically essential; if the data acquisition alternation period is below the lower limit of 10 μs, the macroscopic hardware loop switching... If the switching frequency is too fast, the switching cycle will not be able to fully enclose and absorb the mass transfer delay within the microscopic scale in the spatiotemporal scale, thereby breaking the spatiotemporal multi-scale continuity nesting between the microscopic phase deflection and the macroscopic acquisition frequency, causing mismatch in data flow. If the data acquisition alternation cycle is higher than the upper limit of 50μs, the time after a single cell leaves its native microenvironment will be too long, and the transient dissipation disturbance of intracellular components will be nonlinearly and drastically enhanced, causing an essential distortion of the intracellular heterogeneous dissipation law. This will lead to the indiscriminate and irreversible annihilation of low-abundance trace weak signal features at the cross-polarity comparison convergence point. Therefore, multi-scale continuity nesting or the topological integrity of low-abundance features cannot be guaranteed outside this value range.

[0032] The metabolic network topology computation unit is connected to the cross-gated in-situ hedging reconstruction unit. Its internal topology decoupling module receives the bipolar cooperative time series matrix, maps the decoupled positive and negative polarity cooperative feature components onto the single-cell metabolic pathway network model, and calculates and outputs a metabolic network topology diagram that excludes intensity coverage interference. This allows the computational reconstruction of the transpolar metabolic network to overcome the limitations of traditional unipolar data matrix independent extraction algorithms when facing nonlinear time distortions. In actual operation, the single-cell metabolic pathway network model is a digital network model constructed from a pre-set directed graph database of biochemical reactions. Each node in the network topology represents the mass-to-charge ratio and intensity feature of a specific metabolite, and the edges connecting the nodes represent known enzyme-catalyzed reaction conversion steps. The topology decoupling module directly inputs the positive and negative polarity cooperative feature components from the received bipolar cooperative time series matrix as activation values ​​into the corresponding nodes, and performs computational processing through graph convolution information flow. The method calculates the quality balance residuals of each pathway node, thereby automatically isolating and filtering out overlapping noise interference caused by high-abundance principal components at the graph structure level. Finally, it outputs a metabolic network topology diagram that accurately reflects the intracellular material flow direction. The overall system relies on the bidirectional dependency and synergy of the associated affinity map matrix generation unit and the cross-gated in-situ counterbalancing reconstruction unit to reshape the originally fragmented positive and negative polarity data streams into a bidirectionally dependent collaborative network. This improves the accuracy of the calculation and reconstruction of transpolar metabolite feature maps from 72% to over 96%. Furthermore, it uses unmatched feature isolated points in the latent feature topology map matrix to dynamically and inversely adjust the peak discrimination threshold of the feature extraction operator, capturing low-abundance trace negative ion metabolite signals that are discarded as noise in the static threshold filtering method. This reduces the information entropy loss of the total data stream, improves the sensitivity of trace metabolite feature recognition, maintains the continuity and transparency of the data chain, and improves the system's operational stability under operating conditions.

[0033] Example 2: The effectiveness test of this invention was conducted on a fluid channel platform equipped with a high-throughput single-cell differential separation molecule capture chip. The fluid channel platform is externally connected to a mass spectrometry analysis and detection device with microsecond-level high-speed polarity switching response characteristics to provide a continuous raw data source with a sampling rate of not less than 20kHz. In order to reproduce the random differential mode interference and signal transmission loss under complex industrial and electrophysical fields at the data acquisition source, Gaussian white noise with a signal-to-noise ratio of 20dB and scrambling frequency of 50Hz power frequency harmonic electromagnetic waveform are superimposed between the original positive ion high-dimensional tensor data stream and the original negative ion high-dimensional tensor data stream transmission bus at the data receiving port. This makes the original positive ion high-dimensional tensor data stream and the original negative ion high-dimensional tensor data stream contain nonlinear time distortion caused by mass transfer delay and amplitude attenuation characteristics that increase with cell dissipation after ex vivo. When establishing the control parameter conditions of the cross-gated in-situ counter-current reconstruction unit, the determination threshold value of the quantitative limit is related to the dynamic storage load of the data processing chip and the capture accuracy of low-abundance unknown metabolite feature fragments.

[0034] The main considerations for determining the threshold value boundary are the space charge repulsion resistance of high-abundance principal component ions and the nonlinear dissipation rate of intracellular components in single cells accelerating with increasing in vitro time. When the temperature and pressure of the fluid in the single-cell in vitro channel oscillate at the boundary window, if the threshold value is determined to be below 0.12, background radio frequency noise or thermal stray ionization noise is easily identified as a phase deflection signal, leading to redundant computation scheduling of the step-by-step operator and increasing the cache throughput pressure of the processor core. If the threshold value is determined to be above 0.12, it will cause microsecond-level phase distortion omissions due to the asymmetry of positive and negative polarity migration speeds, resulting in the merging and erasure of mass spectrometry feature components corresponding to trace metabolite components during alignment convergence. In order to balance the data processing load and the phase tracking resolution of low-abundance signals, based on the physical amplitude distribution rules of the relative response balance scale, the transient triggering critical point under the condition of a 15% deviation in heterogeneous ionization efficiency is used as the boundary criterion, and the threshold value is determined to be 0.12, which is used for feature hedging. In practical engineering calibration, when the intrinsic ionization efficiency deviation of the positive and negative polarity components is 15%, the theoretical relative ratio deviation of the corresponding mass spectrum intensity is directly calculated using the ratio formula of the difference and the sum. The absolute value of its pure algebraic scalar is in the range of 0.069 to 0.081. However, under the complex physical conditions of the fluid channel of the microfluidic chip, the mutual repulsion resistance caused by the space charge clustering effect and the micro-fluid friction resistance of the inner wall of the channel will cause the detached component to generate an empirical physical attenuation gain amplification of about 1.5 times during the mass transfer process. This invention performs baseline calibration and quantitative deduction of three times the standard deviation boundary on thousands of background noise flows, and multiplies the gain amplification coefficient caused by the above-mentioned fluid physical mass transfer loss on the theoretical algebraic deviation, thereby locking the transient trigger critical threshold value measured at the hardware bus end to 0.12, which serves as the only closed logic criterion for the hedging circuit under the condition of taking into account both low abundance tracking and noise false triggering.

[0035] The testing process included parallel setups of a control group, a partially missing control group, an out-of-range control group, and the sample group of this invention. The control group employed an independent extraction and alignment method using a unipolar data matrix. Under conditions of 20dB Gaussian white noise and 50Hz power frequency harmonic interference, due to the lack of online phase correction, random phase mismatch artifacts occurred between the original positive ion high-dimensional tensor data stream and the original negative ion high-dimensional tensor data stream when transmitted to the bus backend. The measured accuracy of transpolar metabolite feature map reconstruction was 72.3%, and the information entropy loss of the total system data stream was 27.6%. The partially missing control group retained the accompanying data in the data processing architecture. The unit generating the affinity map matrix and removing the cross-gated in-situ counterbalancing reconstruction unit achieved an actual transpolar metabolite characteristic map reconstruction accuracy of 81.4%. However, due to the lack of active intervention from the dynamic feedforward compensation window, the nonlinear mass transfer delay could not be offset, resulting in noise truncation of low-abundance trace negative ion metabolites. In the out-of-range control group, when the determination threshold was set to 0.05, background electromagnetic interference frequently triggered the correction loop, causing disordered scaling of the feedforward dynamic compensation window, increasing the actual data processing delay by 42.1%, and reducing the transpolar metabolite characteristic map reconstruction accuracy to 76.8%.

[0036] When the threshold value is set to 0.25, the system stops triggering feedforward hedging when the ionization efficiency of the bipolar component shows an asymmetric fluctuation of 18.4%, causing the characteristic components of low-abundance components to be hidden in the baseline noise, and the measured sensitivity of trace metabolite feature recognition decreases to 61.2%. When the sample group of this invention is connected to the same original time-series data stream containing perturbations, the associated affinity map matrix generation unit calculates the spatial manifold overlap of the single-cell mass spectrometry components and outputs a unique associated affinity map matrix in the absence of an external clock synchronization signal. When the absolute value of the relative response balance scale exceeds the threshold value of 0.12, the cross-gated in-situ hedging reconstruction unit calls the step-step operator to adjust the control boundary width of the feedforward dynamic compensation window in real time according to the local density gradient of the manifold, in conjunction with a fixed static reference window width coefficient of 8μs and a set linear adjustment weighting factor. The feedforward in-situ hedging was completed at 0.25, and the final measured total data stream noise floor was less than 3.2%. The accuracy of transpolar metabolite feature map calculation and reconstruction was 96.4%, while the sensitivity of trace metabolite feature recognition was 94.8%. The data chain was in a continuous state. In the quantitative multidimensional control data stream matrix measured under different causal perturbation intensities and parameter boundary gradient conditions, the causal cascade topological relationship between the associated affinity map matrix generation unit and the cross-gated in-situ hedging reconstruction unit generated continuous monotonic regular directional compensation for physical mass transfer delay, heterogeneous channel phase misalignment, and polar ionization efficiency asymmetry during the same-chip bipolar acquisition process. The intermediate process feature variation trend in the data stream characterizes the constraint effect of the parameter range boundary set by the determined threshold value on maintaining the structural integrity of the bipolar high-dimensional tensor signal, enabling the biological data processing system to maintain operational stability under non-ideal industrial conditions.

[0037] Example 3: In an automated analysis system of a bipolar single-cell metabolomics deep analysis system running continuously on a multi-channel high-throughput microfluidic array, physical precipitation and deposition on the surface of the fluid channel platform and local physical loss of the mass spectrometry ionization source electrode caused by high-frequency polarity switching lead to a nonlinear slow shift in the baseline of the original electrical signal. At the same time, when the scale of the parallel single-cell high-dimensional tensor data stream expands to a clustered deployment environment containing hundreds of independent single-cell parallel tracks, the system faces the transient impact of high-concurrency information flow on the processing and storage space, as well as the characteristic temporal local asynchronous tortuosity caused by temperature drift. The static feedforward control rules are difficult to automatically track the aging loss of components generated during operation, thus introducing the risk of computational throughput overload and implicit loss of trace molecule low-abundance spectral nodes at the concurrent alignment convergence point of multiple material channels.

[0038] The data dimensionality reduction and encapsulation unit acquires the original high-dimensional tensor data streams of positive and negative ions, which contain random noise, at the data receiving port. At the instant the data crosses the input physical boundary, a built-in data desensitization layer automatically identifies and removes sample clinical numbers, donor personal identification, and collection time stamps to eliminate privacy traces. This confines the processed object to a purely digital high-dimensional feature flow matrix of mass-to-charge ratio and logarithm of ion intensity, preventing subsequent algorithms from tracing back to a specific individual. The data dimensionality reduction and encapsulation unit utilizes a dual-stream homomorphic compression operator to eliminate redundant space. The sparse component transforms the original high-dimensional tensor into a discrete state space vector, and outputs the generated single-cell temporal feature sparse matrix stream to a dual-channel, dual-branch graph sparse network layer. The graph sparse network layer, in conjunction with a multi-head spatial cross-attention mechanism, constitutes the internal topology of the associated affinity graph matrix generation unit. The positive and negative ionization polarity spectrum feature component sets are input as two parallel streams into the corresponding first and second encoder branches, respectively. The multi-head spatial cross-attention mechanism converts the positive ionization polarity spectrum feature component set into a query matrix, while simultaneously converting the negative ionization polarity spectrum feature component set into a query matrix. The set of polarity feature components is converted into a bond matrix and a value matrix. By performing multi-dimensional similarity product operations on the query matrix and the bond matrix within the overlapping sliding time window, the spatial manifold overlap and distance metric between positive and negative ion mass spectrometry feature components are calculated, outputting a globally unique associated affinity map matrix. The implicit temporal correlation features between bipolar high-dimensional data components are then characterized through topological nodes in the associated affinity map matrix, eliminating the dependence on external hardware clock synchronization signals at the pure information topology level. In this process, the internal transformation mechanism of the multi-head spatial cross-attention mechanism utilizes the first encoder branch... The first linear projection parameter matrix in the encoder transforms the positive ionization polarity spectrum feature components into a multidimensional query matrix containing local time features. At the same time, the second and third linear projection parameter matrices in the second encoder branch map the negative ionization polarity spectrum feature components into a key matrix and a value matrix, respectively. By performing matrix multiplication of the query matrix and the transposed key matrix within the sliding time window and allocating weights using a normalized exponential function, the two independent and asynchronous polarity signals are directly projected and aligned in the feature space. The correlation values ​​reflecting their spatial geometric overlap and distance measure are calculated, thus deconstructing their internal black box.

[0039] The cross-gated in-situ hedging reconstruction unit is connected to the associated affinity map matrix generation unit. Its internal gating weighting module periodically scans the manifold local density distribution of the associated affinity map matrix within the current sampling period. When it detects nonlinear signal amplitude attenuation caused by electrode loss, it adaptively calculates and distributes the matrix update weights of the data truncation correction operator based on the gradient distribution law of the manifold local density, increasing the shearing weights in high-density regions and decreasing the shearing weights in low-density regions. To eliminate processing backlog under multi-track cluster deployment, the system introduces a cluster load balancing allocation strategy based on polling and current state-specified routing in the upper-layer task scheduling, dynamically distributing data fragments generated in the single-cell temporal characteristic sparse matrix stream to the least loaded idle processor cores. Simultaneously, the cross-gated in-situ hedging reconstruction unit... The system acquires the peak intensities of the original positive and negative ion mass spectra within the current time-series unit. It calculates the difference between the peak intensity scalars of the positive and negative ion mass spectra in the original high-dimensional tensor data stream of positive ions and negative ion mass spectra in the original high-dimensional tensor data stream of negative ions, and calculates the sum of the peak intensity scalars of the positive and negative ion mass spectra. Finally, it calculates the ratio of the difference to the sum to establish a relative response balance scale, which objectively characterizes the amplitude intensity asymmetry of the bipolar signal. When the absolute value of the relative response balance scale exceeds a predetermined threshold of 0.12, the cross-gated in-situ offset reconstruction unit initiates an adaptive degradation optimization strategy, calling a step-by-step operator to calculate the control boundary width of the feedforward dynamic compensation window. When calculating the control boundary width of the feedforward dynamic compensation window, the step-by-step operator adjusts the set linear weighting factor to 0.25 is multiplied by the absolute value of the relative response balance scale, and the product is added to the value 1 to determine the adjustment coefficient scalar. The adjustment coefficient scalar is multiplied and superimposed with a fixed static reference window width coefficient of 8 μs to calculate the specific value of the control boundary width of the feedforward dynamic compensation window. A corresponding spatiotemporal sliding hedging interval is opened in the internal storage space to limit the convergence boundary of the nonlinear feature decoupling operator and the feature component extraction weight, thereby eliminating the time-series phase shift caused by channel mass transfer delay and outputting a highly reliable bipolar cooperative time series matrix. To ensure the adaptive adjustment capability of the algorithm under long-term operation, the system introduces a sliding window elimination mechanism with a time decay factor in the update of the sliding time window. By setting a sliding time window of one hour, old historical features are automatically eliminated, and when the variance of the noise baseline measured by ten consecutive sampling points exceeds twice the preset reference, the entire system parameters are automatically recalibrated. As an alternative implementation, when the system's processor computing resources are limited and the computation delay of the above multi-head spatial cross-attention mechanism exceeds the preset safe time control window of 5, At milliseconds, the system automatically switches to a low-power first-order differential correlation approximation algorithm. This algorithm calculates the differential rate of change of feature vectors between adjacent time nodes to replace manifold overlap calculation, thus providing system crash protection under computationally limited conditions. To completely offset the backlog of data caused by the maximum 5ms computational delay due to the multi-head cross-attention mechanism under high-throughput sampling, the system allocates a cyclic first-in-first-out hardware cache loop in parallel at the front end of the distributed computing hardware bus. Its storage capacity is quantitatively divided into a dedicated data stack capable of holding 1000 scan cycle data frames. When the hardware timing core detects that the processing delay of the attention mechanism exceeds the preset safety control boundary of 5ms, the system triggers a high-priority hardware interrupt instruction. Simultaneously, while seamlessly switching to the low-power first-order differential correlation approximation algorithm, the backlog of data frames temporarily stored in the cache loop is pumped into the differential reconstruction loop in a burst transmission mode for rapid processing. The system employs rapid alignment calculations to establish a smooth buffer between microsecond-level sampling and millisecond-level latency, eliminating the risk of data loss and system overload during blind transition phases. The topology decoupling module within the metabolic network topology calculation unit receives the bipolar collaborative time series matrix and directly injects the decoupled positive and negative polarity collaborative feature components into a pre-defined single-cell metabolic pathway network model. The pathway graph calculation nodes in the model then calculate and output a metabolic network topology diagram that excludes intensity coverage interference. In the aforementioned causal loop of information flow processing, composed of multi-track load balancing, graph sparse attention topology mapping, time decay feedforward compensation window, and differential correlation approximation alternative algorithms, the causal loop fully offsets and absorbs phase distortion caused by component aging and storage backlog due to high concurrency. The accuracy of transpolar metabolite feature map calculation and reconstruction is improved from 72.3% in the original state and maintained at 96%.The sensitivity for identifying low-abundance trace negative ion metabolite signals is significantly improved, exceeding 4%, while the information entropy loss of the total data stream is limited. This enables the bipolar single-cell metabolomics deep analysis system to demonstrate high reproducibility and operational stability in long-term continuous industrial environments.

[0040] Example 4: When the system faces a chip switching deployment environment, the surface roughness of the flow channel causes nonlinear disturbances in the initial background noise baseline. In order to establish a stable benchmark required by the cross-gating unit, the system performs baseline calibration before the sample is introduced, controls the chip to inject blank buffer, and starts the mass spectrometer to extract no less than 1000 cycles of background noise stream at a frequency of 20kHz. The data dimensionality reduction and encapsulation unit converts the pure background signal stream into a noise tensor, and the associated affinity map spectrum matrix generation unit calculates the background manifold density envelope under the interference-free state and calculates the physical background variance under the current environment. The cross-gating in-situ hedging reconstruction unit reads the variance and completes pure text quantization deduction based on the three-standard-deviation boundary, thereby automatically correcting and locking the determined threshold value to the set value of 0.12, and completing the parameter adaptive fine-tuning.

[0041] When the system faces degradation conditions due to electrode surface oxidation and increased mass transfer resistance caused by long-term operation, the internal mechanism initiates a periodic online reset. During the interval between nodes where 50 samples are analyzed, the sample extraction flow is automatically paused and cleaning fluid is injected. The sensor is controlled to measure the residual polarization current decay slope in situ. An adaptive strategy reads the slope change rate. When it is detected that the slope deviates monotonically from the preset baseline by more than a threshold, the algorithm automatically adjusts the aforementioned linear adjustment weighting factor with a step gradient from 0.25 to the target extreme value, thereby compensating for the gain loss caused by hardware dissipation. This ensures that the graph sparse attention topology mapping maintains data continuity, and ultimately enables the bipolar cooperative time series matrix output of the system to maintain a stable state with an error fluctuation range of less than 5% after running continuously for more than 48 hours.

[0042] Example 5: In the pre-deployment calibration scenario of the same-chip bipolar single-cell metabolomics deep analysis system, the distributed computing hardware infrastructure accesses the standard multi-track single-cell mass spectrometry detection stream to solidify the data chain flow parameters. The central processing unit receives multiple digital signal streams from the high-speed heterogeneous data bus and opens a dedicated spectrum storage area to construct an adaptive parameter matrix. The first feature channel concurrently latches the original positive ion high-dimensional tensor data stream, and the second feature channel concurrently latches the original negative ion high-dimensional tensor data stream. After the data dimensionality reduction and encapsulation unit obtains the above dual original high-dimensional tensor data streams, it strips off the sample number and collection batch, removes personal privacy traces, and reassembles them into a continuous single-cell temporal feature sparse matrix stream at the data receiving port based on a multivariate linear model.

[0043] The associated affinity map matrix generation unit is connected to the single-cell temporal feature sparse matrix stream. The associated affinity map matrix generation unit is configured with a dual-channel dual-branch graph sparse network layer, which operates in conjunction with a multi-head spatial cross-attention mechanism. The positive ion polarity mass spectrum feature component set and the negative ion polarity mass spectrum feature component set are respectively imported into the first encoder branch and the second encoder branch of the graph sparse network layer. The multi-head spatial cross-attention mechanism converts the positive ion polarity mass spectrum feature component set into a query matrix containing multiple floating-point numbers, and at the same time converts the negative ion polarity mass spectrum feature component set into a bond matrix and a value matrix. The central processing unit calculates the spatial manifold overlap and distance measure between the positive and negative ion mass spectrum feature components based on the multi-dimensional similarity product operation of the query matrix and the bond matrix within the overlapping sliding time window, and generates a globally unique associated affinity map matrix. The topological nodes in the associated affinity map matrix represent the implicit temporal correlation feature degree between the bipolar high-dimensional data components.

[0044] The cross-gated in-situ hedging reconstruction unit is connected to the associated affinity map matrix generation unit. The cross-gated in-situ hedging reconstruction unit periodically scans the local density distribution of the associated affinity map matrix within the current sampling period, identifies the nonlinear decay region caused by differences in ionization efficiency, and assigns the update weight matrix of the data truncation correction operator based on the density gradient of the nonlinear decay region. The cross-gated in-situ hedging reconstruction unit obtains the peak intensities of the original positive and negative ion mass spectra within the current time-series unit, calculates the difference between the peak intensity scalar of the positive ion mass spectrum in the original high-dimensional tensor data stream and the peak intensity scalar of the negative ion mass spectrum in the original high-dimensional tensor data stream, and calculates the difference between the peak intensity scalars of the positive and negative ion mass spectra. The sum is calculated, and the ratio of the difference to the sum is used to establish the relative response balance scale. To verify the convergence stability of the control boundary width under different operating conditions, the calibration process includes three physical verification sample groups with gradient changes. The first verification sample group corresponds to the lower limit operating condition where the absolute value of the relative response balance scale is 0.00. At this time, the positive and negative ion channels exhibit mass transfer balance, and the absolute value of the relative response balance scale does not exceed the preset discrete safety threshold value of 0.12. The cross-gated in-situ counter-shearing reconstruction unit maintains the control window width of the in-situ shear operator at a fixed static reference window width coefficient of 8μs. The convergence boundary of the nonlinear feature decoupling operator remains normal. The downstream metabolic network topology calculation unit calculates the metabolic network topology diagram without intensity coverage interference. The second verification sample group... When the absolute value of the relative response balance scale is 0.12, the temperature and pressure of the fluid in the single-cell ex vivo channel oscillate at the boundary window. Space charge repulsion causes microsecond-level phase deflection. Since the absolute value of the relative response balance scale reaches the preset discrete safety threshold of 0.12, the cross-gated in-situ counter-countermeasure reconstruction unit calls the step-by-step operator to increase the control boundary width of the feedforward dynamic compensation window. The preset linear adjustment weighting factor of 0.25 is multiplied by the absolute value of the relative response balance scale of 0.12. The product is added to the value 1 to determine the adjustment coefficient scalar as 1.03. The adjustment coefficient scalar is multiplied by the fixed static reference window width coefficient of 8μs to calculate the control boundary width of the feedforward dynamic compensation window. The specific value is 8.24 μs. A corresponding spatiotemporal sliding hedging interval is opened in the internal storage space to offset the nonlinear time distortion caused by the mass transfer delay. To verify the rationality of determining the critical technology with a threshold value of 0.12, the upper and lower critical control verification sample groups with absolute scaling values ​​of 0.10 and 0.15 were also introduced in parallel for comparative testing in this pre-calibration scenario. Under the lower critical control condition with an absolute scaling value of 0.10, since this value did not reach the trigger boundary of 0.12, the system still maintained a static reference window of 8 μs, which could not effectively absorb the microscopic mass transfer phase deflection caused by the space charge clustering effect, causing the accuracy of subsequent transpolar metabolite feature map reconstruction to drop directly to below 85%. However, when the absolute scaling value was 0.Under the upper critical control condition of 15, although the system successfully triggered window optimization adjustment, computational overshoot occurred due to excessive amplification of the control boundary width. This caused some trace low-abundance weak signal molecular features to be incorrectly sheared and submerged during data truncation, resulting in a deterioration in the sensitivity of trace metabolite feature recognition, decreasing to below 80%. Test results show that only at the precise scaling critical point of 0.12 does the system reach a globally optimal convergence state that balances complete phase shift offsetting with high-fidelity preservation of low-abundance trace weak signals. This objectively confirms the effectiveness of 0.12 in determining the optimal scaling critical point. To determine the irreplaceable critical technical significance of the threshold value, the third verification sample group corresponds to the upper limit condition with a relative response balance scale absolute value of 0.95. At this point, transient nonlinear network dissipation occurs in the metabolite material set inside the single cell, and the ionization efficiency of the bipolar component exhibits asymmetric fluctuations. Since the relative response balance scale absolute value exceeds the preset discrete safety threshold value of 0.12, the cross-gated in-situ hedging reconstruction unit calls the step-by-step operator to multiply the preset linear adjustment weighting factor of 0.25 with the relative response balance scale absolute value of 0.95. The obtained product result is then compared with the numerical value. Adding 1, the adjustment coefficient scalar is determined to be 1.2375. Multiplying the adjustment coefficient scalar by the fixed static reference window width coefficient of 8μs, the control boundary width of the feedforward dynamic compensation window is calculated to evolve to 9.9μs. This expands the feedforward dynamic compensation window to intercept the temporal phase shift caused by mass transfer delay, outputting a bipolar cooperative time series matrix. This prevents the characteristic components corresponding to trace low-abundance metabolite components from being hidden in baseline noise. When multi-track cluster deployment faces transient processing backlog and the computational delay of the multi-head spatial cross-attention mechanism exceeds the preset safety time control... With a 5ms window, the system employs a low-power first-order differential correlation approximation algorithm. This algorithm replaces manifold overlap calculation by calculating the differential rate of change of feature vectors between adjacent time nodes, thus limiting the cache throughput pressure on the central register. The distributed computing hardware infrastructure continuously records the operating parameters of multiple verification sample groups. The accuracy of transpolar metabolite feature map reconstruction is greater than or equal to 96.4%, the discrimination sensitivity of low-abundance trace negative ion metabolite signals reaches 94.8%, and the total data stream noise floor is below 3.2%. The central processing unit completes offline solidification and calibration of the parameter matrix.

[0045] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A bipolar single-cell metabolomics deep analysis system, characterized in that the system... include: The data dimensionality reduction and encapsulation unit is used to remove business labels from the original positive and negative ion high-dimensional tensor data stream and encapsulate it into data for algorithm processing. The associated affinity map matrix generation unit, connected to the data dimensionality reduction and encapsulation unit, is used to generate the associated affinity map matrix based on data processing according to the algorithm. The cross-gated in-situ counter-current reconstruction unit is connected to the associated affinity map matrix generation unit and the data dimensionality reduction and encapsulation unit, respectively. It is used to calculate the ratio of the difference between the peak intensities of the original positive and negative ion mass spectra in the current time series unit to the sum of the values ​​to establish the relative response equilibrium scale. The relative response balance scale is determined according to the following mathematical expression: Where R is the relative response balance scale, This represents the peak intensity of the original positive ion mass spectrum within the current time unit. The peak intensity of the original negative ion mass spectrum within the current time unit is used. When the absolute value of the relative response balance scale exceeds the determined threshold value of 0.12, adaptive degradation optimization is initiated. The step-by-step operator is called to increase the control boundary width of the feedforward dynamic compensation window to compensate for the time phase shift. At the same time, the local density of the manifold of the associated affinity map spectrum matrix within the current sampling period is scanned. The update weight matrix of the data truncation correction operator is allocated according to the local density gradient. The shearing weight of the original heterogeneous data stream is adjusted by updating the weight matrix to complete the active feedforward topology reconstruction and output the bipolar cooperative time series matrix. The metabolic network topology calculation unit is connected to the cross-gated in-situ hedging reconstruction unit and is used to perform metabolic network topology calculations based on the bipolar cooperative time series matrix.

2. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, When increasing the control boundary width of the feedforward dynamic compensation window, the cross-gated in-situ hedging reconfiguration unit performs the following processing: The control boundary width of the feedforward dynamic compensation window is limited to follow a horizontal linear rule, exhibiting discrete step evolution; the control boundary width is determined according to the following mathematical expression: ,in, The control boundary width of the feedforward dynamic compensation window. The static reference window width factor is 8μs. The linear adjustment weighting factor was set to 0.

25. The absolute value of the relative response balance scale is used; the optimization step is constrained by a fixed static reference window width coefficient of 8μs and a set linear adjustment weighting factor of 0.

25. A feedforward dynamic compensation window for compensating for mass transfer delay and timing phase shift is constructed in the internal storage space to limit the convergence threshold of the nonlinear feature decoupling operator.

3. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, When the data dimensionality reduction and encapsulation unit performs dimensionality reduction and encapsulation on the original positive and negative ion high-dimensional tensor data stream, it performs the following processing: when the original positive and negative ion high-dimensional tensor data stream is acquired at the data receiving port, the sample number and collection batch contained in the original positive and negative ion high-dimensional tensor data stream are stripped to remove the business tags; the original high-dimensional tensor after removing the business tags is recombined into a continuous single-cell temporal feature sparse matrix stream through multivariate linear model dimensionality reduction, and is output to the companion affinity map matrix generation unit as the algorithm processing data.

4. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, When generating the associated affinity map matrix, the associated affinity map matrix generation unit performs the following processing: extracting the isomeric molecular feature data streams from the positive ionization polarity property spectrum feature component set and the negative ionization polarity property spectrum feature component set in the algorithm processing data; Without an external clock synchronization signal, the spatial manifold overlap and distance measure between positive and negative ion mass spectrometry feature components in adjacent sampling periods are calculated to generate a co-existing affinity map matrix for eliminating time axis offset and nonlinear signal attenuation.

5. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, When the cross-gated in-situ hedging reconstruction unit adjusts the shear weights of the original heterogeneous data stream by updating the weight matrix, it performs the following processing: identifying the nonlinear decay region of the response caused by the difference in ionization efficiency; The update weight matrix of the correction operator is truncated based on the density gradient of the response nonlinear decay region, so that the shear weight in the high-density region is increased and the shear weight in the low-density region is decreased.

6. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, The metabolic network topology calculation unit includes a topology decoupling module, which receives a bipolar coordinating time series matrix and maps the decoupled positive and negative polarity coordinating feature components to a single-cell metabolic pathway network model to calculate and output a metabolic network topology diagram without intensity coverage interference.

7. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, The system also includes a historical evolution evaluation unit, which is used to predict the long-term trend of the relative response balance scale to evaluate the system's operational stability. This unit performs the following processing: continuously recording the values ​​of the relative response balance scale within historical sampling periods to construct a long-term time series reflecting the evolution of the ion mass spectrometry channel state over time; calculating the first-order time derivative of the long-term time series to quantify the response characteristic shift rate of the bipolar data acquisition channel; and generating and outputting a calibration warning signal indicating that the data channel deviates from its steady state when the response characteristic shift rate is continuously not lower than a set variation threshold. The set variation threshold is twice the preset steady-state reference shift rate.

8. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, The system also includes a microfluidic chip substrate with a co-chip dual-channel concurrent sampling structure, which is used to simultaneously capture intracellular positive ionization polarity component data streams and negative ionization polarity component data streams; the data dimensionality reduction packaging unit is connected to the data transmission port of the co-chip dual-channel concurrent sampling structure, wherein the data acquisition alternation period of the system is set to 10μs to 50μs.

9. The same-slice bipolar single-cell metabolomics deep analysis system according to claim 1, characterized in that, The system also includes a global adaptive calibration unit, which is connected to the data dimensionality reduction and encapsulation unit and the metabolic network topology calculation unit, respectively. After completing a single metabolic network topology calculation, the unit feeds back the topology convergence residual to the data dimensionality reduction and encapsulation unit to dynamically update the baseline feature weights in the dimensionality reduction of the multivariate linear model and achieve closed-loop error compensation. Specifically, the topology convergence residual is the quality balance residual of each pathway node in the single-cell metabolic pathway network model calculated by the graph convolution information flow algorithm. The global adaptive calibration unit feeds back the quality balance residual as the closed-loop error compensation amount to the data dimensionality reduction and encapsulation unit to dynamically update the baseline feature weights in the dimensionality reduction of the multivariate linear model.

10. A method for in-depth analysis of bipolar single-cell metabolomics on the same slice, which is used in the in-depth analysis system of bipolar single-cell metabolomics on the same slice as described in claim 1, characterized in that, Includes the following steps: Step S101: The data dimensionality reduction and encapsulation unit removes the business tags from the original positive and negative ion high-dimensional tensor data stream and encapsulates it into algorithm processing data. During dimensionality reduction and encapsulation, the original high-dimensional tensor after removing the business tags is input into the dimensionality reduction operation module to calculate the covariance matrix of the ion intensity of each feature channel in adjacent scanning cycles to extract the principal component feature vectors. Sparse redundant components with feature values ​​lower than the preset background noise baseline variance are removed. The original high-dimensional tensor is linearly projected and transformed using the retained core principal component load matrix to reorganize it into a continuous single-cell temporal feature sparse matrix stream. Step S102: The associated affinity map matrix generation unit maps the positive and negative polarity components and generates the associated affinity map matrix by processing the data according to the algorithm and through the multi-head spatial cross attention mechanism. Step S103: The cross-gated in-situ counter-current reconstruction unit calculates the ratio of the peak intensity difference to the sum of the original positive and negative ion mass spectra within the current time unit to establish the relative response balance scale. The relative response balance scale is determined according to the following mathematical expression: Where R is the relative response balance scale, This represents the peak intensity of the original positive ion mass spectrum within the current time unit. This represents the peak intensity of the original negative ion mass spectrum within the current time unit. Step S104, on the relative response balance scale absolute value When the threshold value of 0.12 is exceeded, the cross-gated in-situ offset reconstruction unit initiates adaptive degradation optimization, and calls the step-type stepping operator to increase the control boundary width of the feedforward dynamic compensation window to compensate for the timing phase offset. The control boundary width is determined according to the following mathematical expression: ,in, The control boundary width of the feedforward dynamic compensation window. The static reference window width factor is 8μs. The linear adjustment weighting factor was set to 0.

25. The absolute value of the relative response balance scale; Step S105: The cross-gated in-situ hedging reconstruction unit scans the manifold local density of the associated affinity map matrix in the current sampling period, allocates the update weight matrix of the data truncation correction operator according to the local density gradient, adjusts the shearing weight of the original heterogeneous data stream through the update weight matrix to complete the active feedforward topology reconstruction, and outputs the bipolar cooperative time series matrix. Step S106: The metabolic network topology calculation unit performs metabolic network topology calculation based on the bipolar cooperative time series matrix.

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