Multi-scale causal discovery method and system based on collective behavior modeling

Abstract

The invention provides a multi-scale causal discovery method and system based on collective behavior modeling, and relates to the technical field of causal discovery, and the method comprises the steps: obtaining single-cell RNA sequencing data, the single-cell RNA sequencing data being a gene expression matrix of a plurality of cells; single-cell RNA sequencing data is input into a multi-scale causal model for reasoning, a multi-scale causal diagram is obtained, and the multi-scale causal diagram is used for describing the multi-scale causal relationship between genes and between genes and gene programs; wherein the multi-scale causal model takes a single gene as an individual variable, through collective behavior modeling, the single gene is aggregated into a collective variable representing a gene program, and a multi-scale collaborative learning strategy is adopted to collaboratively learn the multi-scale causal relationship between individuals and between individuals and collective. According to the method, a collective behavior modeling framework is innovatively constructed, key collective behaviors driving state changes can be effectively identified, and the causal discovery accuracy, rationality and robustness are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of causal discovery, and in particular to a multi-scale causal discovery method and system based on collective behavior modeling. Background Art

[0002] The core of causal discovery is to reveal the underlying mechanisms behind observed phenomena. In complex systems, distinguishing causal relationships from simple correlations is crucial for effectively designing interventions and formulating policies; however, traditional causal discovery methods mainly focus on analyzing the mutual influence between individuals in the system, often ignoring the collective behavior formed by the combination of multiple individuals.

[0003] In fact, in ecosystems, economic systems, and social networks, the collective effects of individual behaviors can often trigger nonlinear feedback effects, profoundly affecting the dynamic changes of the system; the complexity of this collective behavior makes it difficult for traditional causal discovery methods to accurately capture the dynamic characteristics within the system, especially in many real complex systems, where the interactions between individuals are not simple linear superpositions, but rather stem from complex network structures and multi-scale interactions, which makes collective behavior exhibit complex nonlinear characteristics. This nonlinear relationship indicates that small changes may trigger significant reactions within the system, and traditional methods are insufficient in identifying and modeling this complexity, which leads to omissions and deviations in the discovered causal relationships. In addition, the abstract nature of collective states and the local optimization strategies of existing score-based causal discovery algorithms further increase the difficulty of studying nonlinear effects, making the process of extracting effective causal information from data more complicated.

[0004] Therefore, existing causal discovery methods lack an in-depth understanding of collective behavior and the exploration of its nonlinear effects, and are unable to accurately reveal the causal mechanisms in complex systems. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a multi-scale causal discovery method and system based on collective behavior modeling, innovatively constructs a collective behavior modeling framework, which can effectively identify the key collective behaviors that drive state changes, and improves the accuracy, rationality and robustness of causal discovery.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions: A multi-scale causal discovery method based on collective behavior modeling, including: Acquire single-cell RNA sequencing data, wherein the single-cell RNA sequencing data is a gene expression matrix of a plurality of cells; The preprocessed single-cell RNA sequencing data is input into a multi-scale causal model for inference to obtain a multi-scale causal graph, wherein the multi-scale causal graph is used to describe the multi-scale causal relationship between genes and genes, and between genes and gene programs; The multi-scale causal model takes a single gene as an individual variable, aggregates it into a collective variable that characterizes the genetic program through collective behavior modeling, and adopts a multi-scale collaborative learning strategy to collaboratively learn the multi-scale causal relationship between individuals and individuals, and between individuals and collectives.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: A multi-scale causal discovery system based on collective behavior modeling, comprising: The data acquisition module is configured to: acquire single-cell RNA sequencing data, wherein the single-cell RNA sequencing data is a gene expression matrix of a plurality of cells; The causal discovery module is configured to: input the preprocessed single-cell RNA sequencing data into a multi-scale causal model for reasoning to obtain a multi-scale causal graph, wherein the multi-scale causal graph is used to describe the multi-scale causal relationship between genes and genes, and between genes and gene programs; The multi-scale causal model takes a single gene as an individual variable, aggregates it into a collective variable that characterizes the genetic program through collective behavior modeling, and adopts a multi-scale collaborative learning strategy to collaboratively learn the multi-scale causal relationship between individuals and individuals, and between individuals and collectives.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the multi-scale causal discovery method based on collective behavior modeling is implemented.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, a multi-scale causal discovery method based on collective behavior modeling is implemented.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the multi-scale causal discovery method based on collective behavior modeling.

[0011] Compared with the prior art, the present invention has the following beneficial effects: (1) This paper proposes a multi-scale causal discovery method based on attention mechanism and multi-scale collaborative learning. This method constructs a collective representation model, aggregates individual variables into collective variables, and adopts a two-stage optimization strategy to collaboratively learn the multi-scale causal relationship between individuals and collectives, thereby effectively revealing the multi-scale causal network structure in complex systems.

[0012] (2) The present invention uses the attention mechanism to dynamically calculate the contribution weight of individuals to the collective representation, making the collective behavior modeling explainable. At the same time, it supports the discovery of causal relationships from the individual to the collective scale, providing a theoretical basis for multi-scale causal analysis.

[0013] (3) The present invention introduces cross-scale consistency constraints in the optimization process to ensure that the causal structures of the three parts, namely individual-individual, individual-collective, and collective-individual, are coordinated with each other, avoid causal loops, and improve the rationality and robustness of causal discovery.

[0014] (4) The present invention adopts a parameterized causal extraction strategy to directly decode multi-scale causal relationships from the optimized model weights, which is suitable for multi-scale causal analysis in fields such as biomedicine and social sciences. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0016] Figure 1 This is a flow chart of the method of Example 1.

[0017] Figure 2 This is an example diagram of the multi-scale causal graph of Example 1. DETAILED DESCRIPTION The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0018] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0019] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0020] In response to the research challenges brought by the abstractness of collective states, the present invention proposes a novel multi-scale causal discovery framework, which integrates existing causal learning technologies and uses deep learning methods (such as attention mechanisms, etc.) to intelligently aggregate individual observation data, effectively identify key collective behaviors that drive changes in system states, and simulate their feedback influence mechanisms on individual behaviors through learned collective representations. At the same time, it accurately models the influence of individuals and their collective behaviors, effectively solving the technical problem that traditional methods are difficult to quantify collective states. In terms of causal discovery algorithms, the present invention proposes an innovative two-stage collaborative optimization strategy, which collaboratively optimizes the causal effects at the individual and collective levels, improves the robustness of multi-scale causal discovery, avoids the local optimal trap of traditional score optimization methods, maintains the consistency of cross-scale causal structures, and provides new methodological support for multi-scale causal analysis in complex systems.

[0021] Example 1 In one embodiment of the present disclosure, a multi-scale causal discovery method based on collective behavior modeling is provided, which innovatively constructs a collective behavior modeling framework, can effectively identify key collective behaviors that drive state changes, and improves the accuracy, rationality and robustness of causal discovery, including two major steps: Step 1: Obtain single-cell RNA sequencing data, where the single-cell RNA sequencing data is a gene expression matrix of several cells; Step 2: input the preprocessed single-cell RNA sequencing data into a multi-scale causal model for inference to obtain a multi-scale causal graph, which is used to describe the multi-scale causal relationship between genes and genes, and between genes and gene programs; The multi-scale causal model takes a single gene as an individual variable, aggregates it into a collective variable that characterizes the genetic program through collective behavior modeling, and adopts a multi-scale collaborative learning strategy to collaboratively learn the multi-scale causal relationship between individuals and individuals, and between individuals and collectives.

[0022] As an embodiment, the specific implementation process of the multi-scale causal discovery method disclosed in the present invention is described below by taking the mining of multi-scale causal mechanisms in gene regulatory networks as an example.

[0023] In the gene regulatory network, a gene program refers to a set of co-regulated genes that jointly perform specific biological functions through spatiotemporal expression patterns. Traditional methods (such as co-expression network analysis) can only identify the correlation between genes, but it is difficult to distinguish causal regulatory relationships, let alone reveal the potential gene program formed by the synergistic effect of multiple genes. This embodiment systematically explores the multi-scale causal mechanisms hidden in the gene regulatory network by modeling the micro-regulation between individual genes and the macro-causal effects at the gene program level. Figure 1 As shown, the specific steps are: S101. Data acquisition and preprocessing S1011. Data acquisition Specifically, single-cell RNA sequencing data (scRNA-seq) is obtained, which is a gene expression matrix of several genes in several cells. The gene expression matrix has cells as rows and genes as columns, and is used to represent the expression value of a single cell on a single gene. In this embodiment, the number of cells is 10,000 and the number of genes is 20,000.

[0024] In the following steps, each row of the gene expression matrix (i.e., the gene expression data of each cell) is taken as a sample, the expression value of a single gene is taken as the value of the individual variable, and the gene program is taken as the potential collective variable to infer and mine the multi-scale causal relationships between genes and genes, and between genes and gene programs.

[0025] S1012. Data preprocessing Specifically, after obtaining all cell samples, the samples are standardized using statistics such as mean and variance.

[0026] S102. Collective Behavior Modeling The deep learning model based on the attention mechanism is used to aggregate individual variables, visualize the abstract collective, and simulate collective behavior based on the impact of the aggregated data on other individuals. The specific implementation method is as follows: S1021, Individual Enhancement Representation Specifically, For the Individual variables, construct a multi-layer feedforward neural network, map it to the latent semantic space where the collective variables are located, and use it for subsequent prediction of collective representation to support multi-scale causal discovery, multi-layer feedforward neural network The formula is: (1) in, Individual variables The latent representation mapped to the semantic space where the collective variable is located, is the number of individual variables, is the number of potential collective variables, For Parameters of the constructed multi-layer feedforward neural network.

[0027] S1022. Collective representation based on attention mechanism Specifically, Represents the potential representation matrix of all individual variables. The attention mechanism is used to further weight the potential representation, predict reliable collective representations for further studying collective behavior, and let the weight matrix of individual variables on collective variables be , the process of calculating the collective representation is expressed as:

[0028] (2)

[0029] in, is the calculated collective variable, are the key vector and value vector in the attention mechanism, is the corresponding linear transformation matrix in the attention mechanism.

[0030] In the subsequent optimization process, Can characterize individual variables The importance of collective representation also serves as the basis for subsequent multi-scale causal structure extraction.

[0031] S103. Multi-scale collaborative learning Individual variables and collective variables are used together to reconstruct as the training objective, cross-scale consistency constraints are introduced, and the augmented Lagrangian method is used to optimize the learning of the weight matrix of individual variables to individual variables and the weight matrix of collective variables to individual variables.

[0032] S1031, Multi-scale Reconstruction Specifically, is the weight matrix of individual variables to individual variables, is the weight matrix of collective variables to individual variables. Through these two weight matrices and collective variables , using Multilayer Perceptron (MLP) to analyze individual variables Reconstruction is expressed as: (3) in, are the multi-layer perceptron parameters to be trained, and ⊕ is the concatenation operation.

[0033] Multi-scale collaborative learning aims to minimize the reconstruction loss, so the loss function is defined as: (4) S1032, cross-scale consistency constraints In order to collaboratively learn multi-scale causal structures, this embodiment imposes cross-scale consistency constraints in multi-scale collaborative learning: (5) in, The individual relationship matrix considers the individual and their collective behaviors, and its physical meaning is the edges that different individual variables can directly affect or mediate through the collective.

[0034] On this basis, the optimization objective of multi-scale collaborative learning is expressed as:

[0035] (6) in, It is a loop-free constraint, which constrains individual variables not to affect themselves directly or indirectly. At the same time, it can be seen from formula (5) that this constraint will simultaneously constrain the influence of individuals on the collective, the influence of the collective on individuals, and the influence of individuals on individuals without loops.

[0036] S1033, two-stage optimization In order to find the multi-scale causal structure, multi-scale collaborative optimization learning is performed on all parameters, where all parameters include the weight matrix of individual variables to individual variables , the weight matrix of collective variables on individual variables , the weight matrix of individual variables on collective variables , Multilayer Perceptron Parameters and multilayer feedforward neural network parameters ; If formula (6) is directly used as the optimization target for optimization, the causal discovery result will be too dependent on the initial value of the model, and an effective causal structure cannot be found. Therefore, this embodiment uses a two-stage optimization algorithm to divide the optimization process into two scales, individual and collective, to optimize the model parameters respectively, effectively improving the accuracy of causal discovery. In the two stages, the augmented Lagrangian method is used to optimize the model parameters of different parts: (7) in, is the penalty coefficient, is the Lagrange multiplier. The constrained problem in equation (6) can be transformed into a series of unconstrained subproblems that can be solved by the gradient descent optimization strategy as follows: (8) (9) (10) When optimizing individual scales, , when optimizing the collective scale, , , , , It is Optimized in iterations , , ,This embodiment applies the L-BFGS-B algorithm to solve the optimization problem.

[0037] S104. Multi-scale causal relationship extraction Specifically, in order to obtain multi-scale causal relationships, this embodiment intends to extract the causal relationships at the individual to individual level, the causal relationships at the individual to collective level, and the causal relationships at the collective to individual level from the model parameters, and integrate the causal relationships of the above three parts to obtain the final multi-scale causal graph.

[0038] S1041, Individual-scale causal relationship extraction According to the set threshold , the weight matrix of individual variables learned previously for individual variables Binarization, to obtain the causal relationship between individuals: (11) in, When Individual variables and There is a causal relationship between individual variables, but not vice versa.

[0039] S1042, collective scale causal relationship extraction According to the set threshold , respectively, the weight matrix of individual variables to collective variables learned previously The weight matrix of collective variables on individual variables Binarization is performed to obtain the causal relationship from individual to collective and from collective to individual.

[0040] (12) (13) Based on the causal relationship between individuals, individuals to collective parts, and collectives to individuals, we construct Figure 2 The multi-scale causal diagram shown.

[0041] Example 2 In one embodiment of the present disclosure, a multi-scale causal discovery system based on collective behavior modeling is provided, comprising: The data acquisition module is configured to: acquire single-cell RNA sequencing data, wherein the single-cell RNA sequencing data is a gene expression matrix of a plurality of cells; The causal discovery module is configured to: input the preprocessed single-cell RNA sequencing data into a multi-scale causal model for reasoning to obtain a multi-scale causal graph, wherein the multi-scale causal graph is used to describe the multi-scale causal relationship between genes and genes, and between genes and gene programs; The multi-scale causal model takes a single gene as an individual variable, aggregates it into a collective variable that characterizes the genetic program through collective behavior modeling, and adopts a multi-scale collaborative learning strategy to collaboratively learn the multi-scale causal relationship between individuals and individuals, and between individuals and collectives.

[0042] Example 3 In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the multi-scale causal discovery method based on collective behavior modeling.

[0043] Example 4 In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the multi-scale causal discovery method based on collective behavior modeling is implemented.

[0044] Example 5 In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the multi-scale causal discovery method based on collective behavior modeling.

[0045] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A multi-scale causal discovery method based on collective behavior modeling, characterized in that: include: Acquire single-cell RNA sequencing data, wherein the single-cell RNA sequencing data is a gene expression matrix of a plurality of cells; The preprocessed single-cell RNA sequencing data is input into a multi-scale causal model for reasoning to obtain a multi-scale causal graph, wherein the multi-scale causal graph is used to describe the multi-scale causal relationship between genes and genes, and between genes and gene programs; The multi-scale causal model takes a single gene as an individual variable, aggregates it into a collective variable that characterizes the genetic program through collective behavior modeling, and adopts a multi-scale collaborative learning strategy to collaboratively learn the multi-scale causal relationship between individuals and individuals, and between individuals and collectives.

2. A multi-scale causal discovery method based on collective behavior modeling as claimed in claim 1, characterized in that: The gene expression matrix, with cells as rows and genes as columns, is used to characterize the expression value of a single gene in a single cell; The preprocessing is to take each row of data in the gene expression matrix as a sample and perform standardization processing on all samples.

3. The multi-scale causal discovery method based on collective behavior modeling as claimed in claim 1, characterized in that: The multi-scale causal model includes a collective behavior modeling module, which utilizes a deep learning model based on an attention mechanism to aggregate individual variables to obtain collective variables, and dynamically calculates a weight matrix of individual variables to collective variables.

4. The multi-scale causal discovery method based on collective behavior modeling as claimed in claim 1, characterized in that: The multi-scale causal model also includes a multi-scale collaborative learning module, which uses individual variables and collective variables to jointly reconstruct as a training goal, introduces cross-scale consistency constraints, and adopts an augmented Lagrangian method to optimize the learning of the weight matrix of individual variables to individual variables and the weight matrix of collective variables to individual variables.

5. A multi-scale causal discovery method based on collective behavior modeling as claimed in claim 4, characterized in that: The reconstruction is used as the training goal, and the cross-scale consistency constraint is introduced, which can be expressed as follows: in, Individual variables and reconstructed individual variables, To consider the individual relationship matrix of individuals and their collective behaviors, They are the weight matrix of individual variables to individual variables, the weight matrix of collective variables to individual variables, and the weight matrix of individual variables to collective variables. It is an acyclic constraint, which means that individual variables cannot directly or indirectly affect themselves. is the number of individual variables.

6. The multi-scale causal discovery method based on collective behavior modeling as claimed in claim 1, characterized in that: The multi-scale causal model also includes a multi-scale causal relationship extraction module, which extracts the causal relationship from individual to individual level, the causal relationship from individual to collective level, and the causal relationship from collective to individual level based on the weight matrix of individual variables to individual variables, the weight matrix of collective variables to individual variables, and the weight matrix of individual variables to collective variables, respectively, to form a final multi-scale causal graph.

7. A multi-scale causal discovery system based on collective behavior modeling, characterized in that: include: The data acquisition module is configured to: acquire single-cell RNA sequencing data, wherein the single-cell RNA sequencing data is a gene expression matrix of a plurality of cells; The causal discovery module is configured to: input the preprocessed single-cell RNA sequencing data into a multi-scale causal model for reasoning to obtain a multi-scale causal graph, wherein the multi-scale causal graph is used to describe the multi-scale causal relationship between genes and genes, and between genes and gene programs; The multi-scale causal model takes a single gene as an individual variable, aggregates it into a collective variable that characterizes the genetic program through collective behavior modeling, and adopts a multi-scale collaborative learning strategy to collaboratively learn the multi-scale causal relationship between individuals and individuals, and between individuals and collectives.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-scale causal discovery method based on collective behavior modeling described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, a multi-scale causal discovery method based on collective behavior modeling as described in any one of claims 1-6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes a multi-scale causal discovery method based on collective behavior modeling as described in any one of claims 1-6.

Images