A method and system for determining the degree of correlation between dark matter and known matter

Abstract

The present invention provides a method and system for determining the degree of association between dark matter and known matter, belonging to the field of environmental ecological science. The present invention constructs an overall network of dark matter and known matter and calculates the network stability, removes the influence of the null hypothesis on the network to obtain the corrected network stability, removes the influence of the overall network on the sub-networks of different components by the ratio method, and obtains the quantitative results of the network stability of the sub-network constructed by each component and dark matter with the environmental gradient. The present invention removes the influence of the overall network of known and dark matter, and can more accurately infer the changing trend of the network stability of each component with dark matter under different environments, providing reference information for the composition changes of all substances in the environment and the composition of dark matter.

Description

Technical Field

[0001] The present invention relates to the field of environmental ecological science, and relates to a method and system for determining the degree of correlation between dark matter and known matter. Background Art

[0002] Dark matter in nature refers to biological and non-biological matter that has not been identified, recognized, or classified, such as microbial (bacteria, fungi, archaea, viruses) dark matter, molecular (organic matter) dark matter, etc. These dark matter occupy a dominant position in the environment on Earth except for human activities, and often exhibit unique functions and ecological niches. However, since dark matter does not have any information in terms of composition and characteristics, it is often excluded by default in the traditional analysis process, making it impossible to accurately describe the real results.

[0003] At present, many scholars have also conducted some research on dark matter, laying an important theoretical foundation for people to open the door to the "dark world": for example, the emergence of genome analysis technology has expanded the research path for microbial dark matter that cannot be cultured alone; the widespread application of bioinformatics and the continuous growth of public data sets have promoted the understanding of microbial dark matter, and even some microbial dark matter with a large spectral distance often dominates in non-human environments, indicating that these dark matter may play a vital role in the earth system. In previous studies, researchers have been committed to exploring the properties of dark matter, but it is still unknown what kind of relationship these dark matter have with the matter we know, and what impact the disappearance of these dark matter will have on the known material system. Summary of the invention

[0004] 1. Problem to be solved

[0005] In response to the problems existing in the prior art, the present invention provides a method for determining the degree of correlation between dark matter and known matter. Based on network stability, the method can be used to determine the degree of correlation between dark matter and known matter in an ecosystem. The method is applicable to different types of biological and non-biological dark matter, and removes the influence of the overall network, making the relationship between dark matter and known matter of different compositions clearer.

[0006] The present invention provides a system based on the above method, which further reveals the relationship and interaction between currently unidentified organisms (substances) and other organisms (substances) in the ecosystem, provides methods and ideas for future research on dark matter, and has important research significance.

[0007] 2. Technical solution

[0008] In order to solve the above problems, the technical solution adopted by the present invention is as follows:

[0009] The present invention provides a method for determining the degree of correlation between dark matter and known matter, comprising the following steps:

[0010] receiving dark matter data and known matter data to form a first data sample, and selecting dark matter and known matter with a higher than set occurrence rate in the first data sample to form a second data sample;

[0011] Based on the second data sample, construct an overall network and calculate the stability of the overall network;

[0012] Based on the overall network or the second data sample, construct subnetworks of each known substance, and calculate the stability of each subnetwork respectively;

[0013] Calculate the overall network stability of the null hypothesis and the null hypothesis sub-network stability;

[0014] Calculate the average value of the original network stability ratio (sub-network / whole network) and the average value of the null hypothesis network stability ratio (sub-network / whole network), correct the stability of the whole network, and obtain the corrected value;

[0015] Based on the correction value, the degree of correlation between known matter and dark matter is determined.

[0016] Preferably, the second data sample is a frequency matrix of each species appearing in different sites, with all species as columns and sample sites as rows.

[0017] Preferably, based on the second data sample, the method for constructing the overall network is: using the sparcc() function of the SpiecEasi package in the R language software to construct the overall network.

[0018] Preferably, the formula for calculating the stability of the overall network is:

[0019]

[0020] y(x)=y i ~x i

[0021] R = ∫0 1 y(x)d(x)

[0022] in:

[0023] n is the number of dark matter species;

[0024] m is the number of known species;

[0025] i is the number of species extinct by dark matter;

[0026] b iis the corresponding number of extinctions of known substances when the number of extinct species of dark matter is i. The extinction number means that when a certain known substance has no correlation with the remaining dark matter, the known substance is considered extinct;

[0027] R is the stability of the network;

[0028] y(x) is the fitting curve expression of the remaining known matter that has not become extinct as the amount of dark matter extinction increases.

[0029] Preferably, the method for constructing a sub-network based on the overall network is: dividing the known substances into several categories according to set rules, screening known species of the same component from the overall network, and constructing a sub-network.

[0030] Preferably, the method for constructing a subnetwork based on the second data sample is: dividing the known substances into several categories according to set rules, and using the sparcc() function of the SpiecEasi package in the R language software to construct the subnetwork.

[0031] Preferably, the method for correcting stability is:

[0032] Calculate n overall network stability observation values ​​obs T , n sub-network stability observation values ​​obs C 、n null hypothesis values ​​of overall network stability null T And the null hypothesis value of the stability of n sub-networks is null C ;

[0033] Calculate the correction value R C ; Correction value R C The calculation formula is,

[0034]

[0035]

[0036]

[0037]

[0038] in,

[0039] is the average value of the original network stability ratio (sub-network / overall network);

[0040] is the average value of the null hypothesis network stability ratio (subnetwork / overall network);

[0041] σ ratio is the standard deviation of the stability ratio set of the original network and the null hypothesis network;

[0042] R C is the correction value.

[0043] Preferably, the null hypothesis is calculated as follows:

[0044] The null hypothesis of the overall network is obtained by randomly shuffling the values ​​of the overall network in rows and columns;

[0045] The null hypothesis stability of the overall network and the null hypothesis stability of the sub-network are calculated according to the stability calculation method.

[0046] Preferably, the method for determining the degree of correlation between known matter and dark matter based on the correction value is: the lower the correction value, the lower the network stability of the known matter, and the weaker the degree of correlation between the known matter and dark matter.

[0047] The present invention further provides a system for determining the degree of correlation between dark matter and known matter, which uses the above method to determine the degree of correlation between dark matter and known matter, including:

[0048] A sample forming unit configured to receive dark matter data and known matter data to form a first data sample, and select dark matter and known matter with a higher than set occurrence rate in the first data sample to form a second data sample;

[0049] An overall network construction unit, configured to construct an overall network based on the second data sample and calculate the stability of the overall network;

[0050] A subnetwork construction unit configured to construct a subnetwork of each known substance based on the second data sample or the overall network, and calculate the stability of each subnetwork respectively;

[0051] a stability calculation unit configured to calculate the overall network stability of the null hypothesis and the stability of the null hypothesis sub-network;

[0052] A correction unit, configured to calculate an average value of the original network stability ratio (sub-network / whole network) and an average value of the null hypothesis network stability ratio (sub-network / whole network), correct the stability of the whole network, and obtain a correction value;

[0053] The result output unit is configured to determine the degree of correlation between the known matter and the dark matter according to the correction value.

[0054] 3. Beneficial effects

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on network analysis and null hypothesis, the present invention constructs a binary network of dark matter and known matter, compares the differences in the interactions between dark matter and known matter under different environmental changes, and reveals the importance of dark matter to the entire system under different conditions. This method, through quantitative network analysis, reveals for the first time the role of dark matter in the ecosystem on known matter and the entire ecosystem; in addition, this method can also be extended to changes in ecosystem structure at different time and space scales. Applying this method to a wider range of groups and ecosystems can fully understand the role characteristics hidden in the dark matter world, which are crucial to explaining the changes in ecosystem structure and the impact of global changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a specific flow chart of the present invention;

[0057] Figure 2 An overview diagram of the calculation method for calculating organic dark matter using the present invention;

[0058] Figure 3 This is a stability diagram of different organic matter components and dark matter networks;

[0059] Figure 4 An overview diagram of the calculation method for calculating microbial dark matter using the present invention;

[0060] Figure 5 This is a diagram of the stability of different bacterial phyla and dark matter networks.

[0061] In the above-mentioned Figures 1-5, the coordinates, symbols or other expressions expressed in English are all well known in the art and will not be described in detail in the present invention. DETAILED DESCRIPTION

[0062] The following detailed description of exemplary embodiments of the present invention refers to the accompanying drawings, which form a part of the description, and in which exemplary embodiments of the present invention that can be implemented are shown as examples. The following more detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but is only for illustration and does not limit the description of the characteristics and features of the present invention, so as to propose the best way to perform the present invention, and is sufficient to enable those skilled in the art to implement the present invention. However, it should be understood that various modifications and variations can be made without departing from the scope of the present invention as defined by the appended claims. The detailed description and the accompanying drawings should be considered only as illustrative, not restrictive, and if there are any such modifications and variations, they will all fall within the scope of the present invention described herein. In addition, the background technology is intended to illustrate the current status and significance of the research and development of the present technology, and is not intended to limit the present invention or the application field of the present application and the present invention.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0064] In the present invention, the dark matter refers to organisms (bacteria, archaea, fungi, plants, animals, etc.) and non-biological substances (organic matter) that have not been identified, recognized, or classified; the known matter refers to organisms (bacteria, archaea, fungi, plants, animals, etc.) and non-biological substances (organic matter) that have been identified, differentiated, and determined to be a certain component or category.

[0065] In the present invention, the occurrence rate refers to the frequency of occurrence of a species in N samples. For example, among 100 samples (in the present invention, as an example, a sample is a sampling site, or it can be a sample in other forms), species A appears in 30 samples, and the occurrence rate of species A can be considered to be 30%.

[0066] Furthermore, the present invention provides a method for determining the degree of correlation between dark matter and known matter, such as Figure 1 As shown, the following steps are included:

[0067] S100, receiving dark matter data and known matter data to form a first data sample, selecting dark matter and known matter with a higher than set occurrence rate (in the present invention, the set occurrence rate is preferably 30%) in the data sample to form a second data sample; wherein the second data sample refers to a frequency matrix of each species appearing in different sites formed with all species as columns (e.g., n+m columns, including n dark matter and m known matter) and sites as rows, for example, an environmental factor table, a species abundance table, a species attribute table, etc.;

[0068] S200, based on the second data sample, construct an overall network, and calculate the stability of the overall network; as a specific implementation method for constructing the overall network, the present invention constructs the overall network through the sparcc() function of the SpiecEasi package in the R language; for the stability in the present invention, the specific calculation method is:

[0069]

[0070] y(x)=y i ~x i

[0071] R = ∫0 1 y(x)d(x)

[0072] in:

[0073] n is the number of dark matter species;

[0074] m is the number of known species;

[0075] i is the number of species extinct by dark matter;

[0076] b i is the corresponding number of extinctions of known substances when the number of extinct species of dark matter is i. The extinction number means that when a certain known substance has no correlation with the remaining dark matter, the known substance is considered extinct;

[0077] R is the stability of the network;

[0078] y(x) is the fitting curve expression of the remaining known matter that has not been extinct as the amount of dark matter extinction increases;

[0079] S300, based on the second data sample or the overall network, construct subnetworks of each known substance, and calculate the stability of each subnetwork respectively; wherein, the subnetwork construction method is preferably to divide the known substances into several categories according to the set rules, screen the known species of the same component from the overall network, and construct the subnetwork; or directly based on the second data sample, use the sparcc() function of the SpiecEasi package in the R language software to construct the subnetwork; it is worth noting that the setting rules here vary according to different research objects, for example, for organic matter, the known substances can be divided into components such as lipids, proteins, lignin, etc. according to H / C and O / C, and microorganisms can be divided into Proteobacteria, Actinomycetes and other bacterial phyla according to the phylum classification level;

[0080] S400, calculating the overall network stability of the null hypothesis and the null hypothesis sub-network stability; specifically, the calculation method of the null hypothesis is,

[0081] By randomly shuffling the values ​​of the overall network in rows and columns, the overall network null hypothesis B is obtained;

[0082] Calculate the null hypothesis stability of the overall network and the null hypothesis stability of the sub-network according to the stability calculation method;

[0083] S500, calculating the average value of the original network stability ratio (sub-network / whole network) and the average value of the null hypothesis network stability ratio (sub-network / whole network), correcting the stability of the whole network to obtain a corrected value;

[0084] S600, determining the correlation between the known matter and the dark matter according to the correction value; the correction method is:

[0085] Calculate n overall network stability observation values ​​obs T , n sub-network stability observation values ​​obs C 、n null hypothesis values ​​of overall network stability null T And the null hypothesis value of the stability of n sub-networks is null C ;

[0086] Calculate the correction value R C ; Correction value R C The calculation formula is,

[0087]

[0088]

[0089]

[0090]

[0091] in,

[0092] is the average value of the original network stability ratio (sub-network / overall network);

[0093] is the average value of the null hypothesis network stability ratio (subnetwork / overall network);

[0094] σ ratio is the standard deviation of the stability ratio set of the original network and the null hypothesis network;

[0095] R C is the correction value.

[0096] In the present invention, the method for determining the degree of correlation between known matter and dark matter based on the correction value is: the lower the correction value, the lower the network stability of the known matter, and the weaker the degree of correlation between the known matter and the dark matter.

[0097] Based on the above method, the present invention further provides a system for determining the degree of correlation between dark matter and known matter, comprising:

[0098] A sample forming unit configured to receive dark matter data and known matter data to form a first data sample, and select dark matter and known matter with a higher than set occurrence rate in the first data sample to form a second data sample;

[0099] An overall network construction unit, configured to construct an overall network based on the second data sample and calculate the stability of the overall network;

[0100] A subnetwork construction unit configured to construct a subnetwork of each known substance based on the second data sample or the overall network, and calculate the stability of each subnetwork respectively;

[0101] a stability calculation unit configured to calculate the overall network stability of the null hypothesis and the stability of the null hypothesis sub-network;

[0102] A correction unit, configured to calculate an average value of the original network stability ratio (sub-network / whole network) and an average value of the null hypothesis network stability ratio (sub-network / whole network), and correct the stability of the whole network to obtain a correction value;

[0103] The result output unit is configured to determine the degree of correlation between the known matter and the dark matter according to the correction value, and the output form of the result output unit can be in the form of a chart or other forms.

[0104] The method and system of the present invention are based on network stability and can be used to quantitatively evaluate the relationship between dark matter and known matter in an ecosystem. They are applicable to different types of biological and non-biological dark matter, and remove the influence of the overall network, making the relationship between dark matter and known matter of different components clearer. At the same time, it further reveals the relationship and interaction between currently unidentified organisms (matter) and other organisms (matter) in the ecosystem, providing methods and ideas for future research on dark matter, which has important research significance.

[0105] The present invention is described in detail below through specific examples.

[0106] Example 1

[0107] In this embodiment, the dissolved organic matter in sediments under different nutrient gradient culture conditions is taken as the main research object, wherein the components that have not been identified, recognized, and differentiated are defined as dark matter, and the substances that have been identified, recognized, and differentiated are known substances.

[0108] The method of the present invention is used to study the stability of dark matter and known matter networks, including the following steps:

[0109] Step 1. Obtain a relative abundance table of dark matter and known substances under certain conditions; in this embodiment, the relative abundance table of dark matter and known substances refers to the ratio of the abundance of each dark matter to the known substance to the total abundance of the community; since there is no specific abundance index for organic matter, the peak value of organic matter is used as an abundance index to measure the content of different organic matter in the sample. In order to increase the accuracy of network analysis as much as possible, the present invention randomly screened some organic matter with a higher incidence (greater than 30%). In order to increase the comparability between different networks, the sum of the richness of dark matter and known substances in each environmental gradient is set to 400.

[0110] Step 2, calculate the overall network stability, sub-network stability, and the overall network null hypothesis stability and sub-network null hypothesis stability; in this embodiment, the known substances are divided into four categories: lipids, proteins, lignin, and unsaturated carbohydrates, and extracted from the overall network as sub-network construction rules; the null hypothesis is obtained by randomly shuffling the original network correlation coefficient values ​​in rows and columns.

[0111] Step 3: Repeat steps 1-2 for about 100 (i.e., n=100) times, and calibrate the network parameter values. In order to reduce the error caused by random sampling, the relative abundance table of dark matter and known matter under each environmental gradient will be extracted 100 times. Accordingly, the overall network, subnetwork, and the overall network null hypothesis and subnetwork null hypothesis will also be calculated 100 times to obtain the corrected stability.

[0112] Based on the above steps and methods, the topological properties of the dissolved organic matter symbiosis network in the lake environment were quantitatively analyzed using the relative abundance of dissolved organic matter, the organic matter attribute table, and environmental data ( Figure 2 ), and the obtained stability is corrected by the null hypothesis test. The correction results are as follows Figure 3 As shown. Figure 3 It can be seen that the median of the corrected stability of lipids and proteins (i.e., the thick line in the figure) is significantly lower than that of lignin and unsaturated carbohydrates, that is, whether in China or Norway, lipids and proteins show lower stability than lignin and unsaturated carbohydrates. The lower the stability, the weaker the correlation between dark matter and known substances, which may be determined by its easy degradation by microorganisms.

[0113] At the same time, the figure also shows the significant differences between different substances, among which one star means p < 0.05, two stars means p < 0.01, three stars means p < 0.001, and NS. (No Significance) means no statistical difference. By comparing the significance of the differences between different groups, we can see that the stability values ​​of proteins and lipids are the lowest, and they are significantly different from other groups, indicating that the network stability formed by proteins and lipids and dark matter is the lowest, that is, the degree of correlation between these two substances and dark matter is the weakest.

[0114] Example 2

[0115] The basic content of this embodiment is the same as that of embodiment 1, except that: in this embodiment, the research object is a bacterial community, and the present invention is used to calculate the correlation characteristics of microbial dark matter and known substances (such as Figure 4 The output is as follows Figure 5 As shown. Figure 5It can be seen that the median stability of Proteobacteria and Actinobacteria is relatively low, indicating that compared with Firmicutes and Bacteroidetes, the network formed by Proteobacteria, Actinobacteria and dark matter is more easily affected under the condition of gradual extinction of dark matter, that is, the lower the stability, the weaker the correlation between dark matter and Proteobacteria and Actinobacteria.

[0116] More specifically, although exemplary embodiments of the present invention have been described herein, the present invention is not limited to these embodiments, but includes any and all embodiments that are recognizable by those skilled in the art based on the foregoing detailed description, such as combinations between the various embodiments, adaptive changes and / or replacements. The limitations in the claims may be interpreted broadly based on the language used in the claims and are not limited to the examples described in the foregoing detailed description or during the implementation of the application, which examples should be considered non-exclusive. Any steps listed in any method or process claim may be performed in any order and are not limited to the order presented in the claim. Therefore, the scope of the present invention should be determined solely by the attached claims and their legal equivalents, rather than by the description and examples given above.

Claims

1. A method for determining the degree of correlation between dark matter and known matter, characterized in that: The steps include: receiving dark matter data and known matter data to form a first data sample, and selecting dark matter and known matter with a higher than set occurrence rate in the first data sample to form a second data sample; Based on the second data sample, construct an overall network and calculate the stability of the overall network; Based on the overall network or the second data sample, construct subnetworks of each known substance, and calculate the stability of each subnetwork respectively; Calculate the overall network stability of the null hypothesis and the null hypothesis sub-network stability; Calculating the average value of the original network stability ratio (sub-network / whole network) and the average value of the null hypothesis network stability ratio (sub-network / whole network), and correcting the stability of the whole network to obtain a corrected value; Based on the correction value, the degree of correlation between known matter and dark matter is determined.

2. A method for determining the degree of correlation between dark matter and known matter according to claim 1, characterized in that: The second data sample is a frequency matrix of each species appearing in different samples, formed by taking all species as columns and samples as rows.

3. A method for determining the degree of correlation between dark matter and known matter according to claim 1, characterized in that: Based on the second data sample, the method for constructing the overall network is: using the sparcc() function of the SpiecEasi package in the R language software to construct the overall network.

4. The method for determining the degree of correlation between dark matter and known matter according to claim 1, characterized in that: The formula for calculating the stability of the overall network is: y(x)=y i ~x i in: n is the number of dark matter species; m is the number of known species; i is the number of species extinct by dark matter; b i is the corresponding number of extinctions of known substances when the number of dark matter extinction species is i. The extinction number means that when a certain known substance has no correlation with the remaining dark matter, the known substance is considered extinct; R is the stability of the network; y(x) is the fitting curve expression of the remaining known matter that has not become extinct as the amount of dark matter extinction increases.

5. The method for determining the degree of correlation between dark matter and known matter according to claim 1, characterized in that: The method for constructing a sub-network based on the overall network is: dividing known substances into several categories according to set rules, screening known species of the same component from the overall network, and constructing a sub-network.

6. A method for determining the degree of correlation between dark matter and known matter according to claim 1, characterized in that: The method for constructing a subnetwork based on the second data sample is: dividing the known substances into several categories according to the set rules, and using the sparcc() function of the SpiecEasi package in the R language software to construct the subnetwork.

7. The method for determining the degree of correlation between dark matter and known matter according to claim 1, characterized in that: The method for correcting the stability is: Calculate n overall network stability observation values ​​obs T , n sub-network stability observation values ​​obs C 、n null hypothesis values ​​of overall network stability null T And the null hypothesis value of the stability of n sub-networks is null C ; Calculate the correction value R C ; The correction value R C The calculation formula is, in, is the average value of the original network stability ratio (sub-network / overall network); is the average value of the null hypothesis network stability ratio (subnetwork / overall network); σ ratio is the standard deviation of the stability ratio set of the original network and the null hypothesis network; R C is the correction value.

8. The method for determining the degree of correlation between dark matter and known matter according to claim 2, characterized in that: The calculation method of the null hypothesis is, The null hypothesis of the overall network is obtained by randomly shuffling the values ​​of the overall network in rows and columns; The null hypothesis stability of the whole network and the null hypothesis stability of the sub-network are calculated according to the stability calculation method.

9. The method for determining the degree of correlation between dark matter and known matter according to claim 2, characterized in that: According to the correction value, a method for determining the degree of correlation between the known matter and the dark matter is: the lower the correction value, the lower the network stability of the known matter, and the weaker the degree of correlation between the known matter and the dark matter.

10. A system for determining the degree of correlation between dark matter and known matter, using the method according to any one of claims 1 to 9 to determine the degree of correlation between dark matter and known matter, characterized in that: include: A sample forming unit configured to receive dark matter data and known matter data to form a first data sample, and select dark matter and known matter with a higher than set occurrence rate in the first data sample to form a second data sample; An overall network construction unit, configured to construct an overall network based on the second data sample and calculate the stability of the overall network; A subnetwork construction unit configured to construct a subnetwork of each known substance based on the second data sample or the overall network, and calculate the stability of each subnetwork respectively; a stability calculation unit configured to calculate the overall network stability of the null hypothesis and the stability of the null hypothesis sub-network; A correction unit, configured to calculate an average value of the original network stability ratio (sub-network / whole network) and an average value of the null hypothesis network stability ratio (sub-network / whole network), and correct the stability of the whole network to obtain a correction value; A result output unit is configured to determine the degree of correlation between the known matter and the dark matter according to the correction value.

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