CN117275657A - Weight management effect prediction method based on intestinal fungus transplantation and application of genus - Google Patents
Weight management effect prediction method based on intestinal fungus transplantation and application of genus Download PDFInfo
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Abstract
The invention discloses a weight management effect prediction method based on intestinal fungus transplantation and application of fungus genus, wherein the prediction method comprises the following steps: acquiring the microbial abundance value and microbial differential bacteria information in intestinal flora sets of the detection population and the control population; quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discrimination method, and determining a flora influence factor; determining a flora offset of the differential microorganism genus based on the flora influence factor according to the differential situation between the microbial abundance value and the healthy microorganism genus; constructing a bacterial effectiveness scoring system according to the association relation between the bacterial colony influence factors and the bacterial colony offset; obtaining strains to be transplanted, inputting the strains to be transplanted into a strain effectiveness scoring system, and determining the transplanting effectiveness of the strains to be transplanted. The invention solves the problem that the effectiveness of the transplanted flora cannot be predicted before the intestinal flora is transplanted in the prior art, so that the flora transplanting efficiency is low.
Description
Technical Field
The invention relates to the technical field of flora transplantation, in particular to a weight management effect prediction method based on intestinal flora transplantation and application of fungus.
Background
The prevention and treatment of obesity has become a hot topic. At present, correction is mainly performed by dietetic therapy, exercise therapy, pharmaceutical intervention, surgical therapy and the like, but an effective and safer method is lacking. Intestinal flora plays an important role in the occurrence and development of obesity, and the flora and organism are interdependent, and the flora change caused by the change of the flora and the change of the intestinal environment of a human body can influence the processes of energy metabolism, nutrition absorption and the like of the organism, so that the occurrence and development of obesity are influenced. Currently, there is a large literature report on the treatment of obesity with intestinal flora transplantation (FMT). FMT refers to transplanting the intestinal flora of a healthy donor into the patient's intestinal tract to effect its treatment of obesity by reestablishing the normal functioning intestinal flora of the patient.
However, the current research shows that the effectiveness of the intestinal bacteria transplantation is low, and no method for improving the effectiveness of the intestinal bacteria transplantation exists at present, but the selection of donor flora plays a decisive role in improving the effectiveness of the transplantation, and if a prediction method of the effectiveness of the flora can be provided, the effectiveness of the intestinal bacteria transplantation can be greatly provided.
Disclosure of Invention
The invention aims to overcome the technical defects, provide a weight management effect prediction method based on intestinal flora transplantation and application of fungus genus, and solve the technical problem that the effectiveness of the transplanted fungus colony to a target receptor cannot be predicted before the intestinal fungus colony is transplanted in the prior art, so that the fungus colony transplantation efficiency is low.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for predicting a weight management effect based on intestinal fungus transplantation, comprising:
acquiring the microbial abundance value and microbial differential bacteria information in intestinal flora sets of the detection population and the control population;
quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discrimination method, and determining a flora influence factor;
determining a flora offset of the differential microorganism genus based on the flora influence factor according to the differential situation between the microbial abundance value and the healthy microorganism genus;
constructing a bacterial effectiveness scoring system according to the association relation between the bacterial colony influence factors and the bacterial colony offset;
obtaining strains to be transplanted, inputting the strains to be transplanted into a strain effectiveness scoring system, and determining the transplanting effectiveness of the strains to be transplanted.
In some embodiments, the microorganism abundance value is obtained by DNA concentration normalization and the microorganism differential bacteria is obtained by differential testing the microorganism abundance value by a preset non-parametric test method.
In some embodiments, the quantitatively analyzing the data quality of the microorganism abundance value by using a preset linear discriminant method, and determining the flora influence factor includes:
acquiring a mean value and a covariance matrix of a strain abundance data set of a detection crowd and acquiring a healthy mean value and a healthy covariance matrix of a strain abundance data set of a control crowd;
based on the mean and covariance matrix, projecting strain abundance data of the detection population, and determining detection data projection when the difference between classes is the largest and the difference in the classes is the smallest; based on the comparison mean and the health covariance matrix, projecting strain abundance data of the comparison population, and determining comparison data projection when the difference between classes is the largest and the difference in the classes is the smallest;
determining the average difference of the detected abundance according to the detection data projection; determining a contrast abundance mean difference according to the contrast data projection;
determining the flora influence factor according to the arithmetic average of the detection abundance mean difference and the control abundance mean difference.
In some embodiments, the flora offset may be expressed by the following formula:
wherein v is the weighted offset of the abundance of the flora of the sample to be predicted and the abundance of the control flora, k is the number of species of the differential flora, O t A flora offset for a t-th type differential flora of the sample to be predicted; r is (r) t Is the t-class differential flora influence factor.
In some embodiments, the score of the bacterial effectiveness scoring system is obtained by the product of the bacterial population affecting factor and the bacterial population offset.
In some embodiments, the inputting the strain to be transplanted into the strain effectiveness scoring system, determining the transplanting effectiveness of the strain to be transplanted comprises:
obtaining the score of the strain to be transplanted based on the strain effectiveness scoring system;
and determining the efficiency of the strain to be transplanted according to the score.
In some embodiments, said determining the efficiency of said species to be transplanted based on said score comprises:
judging whether the score is larger than a preset effective score value or not;
if the strain is larger than the target effective strain, searching the strain to be transplanted.
In a second aspect, the present invention also provides a weight management effect prediction apparatus based on intestinal fungus transplantation, including:
the acquisition module is used for acquiring the microbial abundance value and the microbial differential genus information in the intestinal flora collection of the detection crowd and the control crowd;
the flora influence factor determining module is used for quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discrimination method to determine the flora influence factor;
the flora offset determining module is used for determining the flora offset of the microbial differential bacteria according to the difference condition between the microbial abundance value and the healthy bacteria based on the flora influence factor;
the scoring system construction module is used for constructing a bacterial effectiveness scoring system according to the association relation between the flora influence factors and the flora offset;
the prediction module is used for obtaining strains to be transplanted, inputting the strains to be transplanted into the strain effectiveness scoring system, and determining the transplanting effectiveness of the strains to be transplanted.
In a third aspect, the use of an effective genus identified according to the above method in the manufacture of a medicament for the treatment of obesity.
Further, the effective bacteria are Odoribacterium, bifidobacterium, christensenella, faecalibacterium and Holdemanella.
Compared with the prior art, the method, the device, the equipment and the storage medium for predicting the weight management effect based on the intestinal fungus transplantation provided by the invention have the advantages that firstly, the microorganism abundance value and the microorganism differential genus in the intestinal fungus population collection of the detection population and the control population are obtained; then, quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discriminant method, and determining a flora influence factor; thereby determining the difference in intestinal flora species composition between the detection population and the control population; then determining a flora offset of the differential microorganism genus based on the flora influence factor according to the difference between the abundance value of the microorganism and the healthy microorganism genus; according to the association relation between the flora influence factors and the flora offset, a bacterial effectiveness scoring system is constructed; finally, obtaining strains to be transplanted, inputting the strains to be transplanted into a strain effectiveness scoring system, and determining the effectiveness of the strains to be transplanted in treating obesity. The method for predicting the strain transplantation weight-losing method is simple and accurate, can predict the effectiveness of the strain in advance, and improves the effectiveness of strain transplantation.
Furthermore, the invention also determines the application of the effective bacteria in preparing the medicine for treating obesity by a weight management effect prediction method, wherein the effective bacteria comprise the Odoribacterium, the Bifidobacterium, the Christenseneella, the Faecalibacterium and the Holdemannella.
Drawings
FIG. 1 is a flowchart of an embodiment of a method for predicting weight management effects based on intestinal fungus transplantation according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of step S102 in the method for predicting body weight management effect based on intestinal fungus transplantation according to the present invention;
FIG. 3 is a schematic view of an embodiment of a weight management effect prediction device based on intestinal fungus transplantation according to the present invention;
FIG. 4 is a schematic diagram of an operating environment of an embodiment of an electronic device according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a weight management effect prediction method based on intestinal fungus transplantation, referring to fig. 1, comprising the following steps:
s101, acquiring a microorganism abundance value and microorganism differential bacteria information in intestinal flora sets of detection population and control population;
s102, quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discrimination method, and determining a flora influence factor;
s103, determining the flora offset of the differential microorganism genus according to the differential situation between the abundance value of the microorganism and the healthy microorganism genus based on the flora influence factor;
s104, constructing a bacterial effectiveness scoring system according to the association relation between the bacterial colony influence factors and the bacterial colony offset;
s105, obtaining strains to be transplanted, inputting the strains to be transplanted into the strain effectiveness scoring system, and determining the transplanting effectiveness of the strains to be transplanted.
In the embodiment, firstly, acquiring the microorganism abundance value and the microorganism differential genus in the intestinal flora collection of the detection population and the control population; then, quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discriminant method, and determining a flora influence factor; thereby determining the difference in intestinal flora species composition between the detection population and the control population; then determining a flora offset of the differential microorganism genus based on the flora influence factor according to the difference between the abundance value of the microorganism and the healthy microorganism genus; according to the association relation between the flora influence factors and the flora offset, a bacterial effectiveness scoring system is constructed; finally, obtaining strains to be transplanted, inputting the strains to be transplanted into a strain effectiveness scoring system, and determining the effectiveness of the strains to be transplanted in treating obesity. The method for predicting the strain transplantation weight-losing method is simple and accurate, can predict the effectiveness of the strain in advance, and improves the effectiveness of strain transplantation.
In step S101, a certain fraction of the feces of the detection population and the feces of the control population are collected, and the microbial genomic DNA of the sample is extracted by a CTAB or SDS method, followed by PCR amplification. The amplification primers were library constructed using the TruSeq@DNA PCR-Free Sample Preparation Kit library kit and then sequenced on-machine using Illumina Miseq PE 250. Wherein the sequencing method is a second generation sequencing method or a third generation sequencing method including but not limited to. The means for sequencing is not particularly limited, and rapid and efficient sequencing can be achieved by sequencing through a second-generation or third-generation sequencing method. As a specific embodiment, the sequencing method is performed by at least one selected from the group consisting of Hiseq200, miseq (PE 300/PE 250), SOLiD, 454 and single molecule sequencing devices. Therefore, the high-throughput and deep sequencing characteristics of the sequencing devices can be utilized, so that the analysis of subsequent sequencing data, particularly the precision and accuracy in the process of statistical inspection, is facilitated.
Further, the double-ended sequencing data was denoised using the dada2 software package in the R language. After the error sequence is removed, the ASV feature sequence is classified by clustering with the Identity criterion set to 100%. 16S sequences were obtained from the SILVA database (release 138, SSURef_NR99), specific regions were cut out, redundant sequences were removed, and the majority of sequence supported taxol was retained. The database-based classifiers were obtained using the Qiime2 plugin feature-classifield-native-bayes, and then the Qiime2 plugin feature-classifield-sklearn was annotated with ASVs feature sequences at each classification level: the colony composition of each sample was counted in kingdom, phylum, class, order, family, genus, species. And finally, carrying out homogenization treatment on the data of each sample, and carrying out homogenization treatment by taking the minimum data amount in the sample as a standard to obtain the absolute abundance and the relative abundance of the sample. Specifically, "abundance" refers to a measure of the number of target microorganisms in a biological sample.
In a specific embodiment, the microorganism differential bacteria are analyzed by PCoA using wgcnstats and gglot 2 packages of R software. The R software was used for differential analysis between Beta diversity index sets, non-parametric testing, multi-response substitution process analysis (MRPP analysis). The Wilcoxon test was performed on species abundance for multiple groups using the R language.
It should be noted that, comparing the sequencing sequences of samples of different groups with the reference gene set; based on the comparison results, the relative abundance of each gene species and the relative abundance of functions in the nucleic acid samples of different groups are determined respectively, and statistics are performed on the relative abundance of each gene species and the relative abundance of functions in the nucleic acid samples of different groups. Finally, biological markers are determined that have significant differences in relative abundance between different groups of fecal samples, thereby effectively monitoring the effectiveness of strain transplantation by detecting the presence or absence of at least one of the aforementioned microorganisms.
In this embodiment, the detection population is an obese patient, the control population is a healthy patient, and the effectiveness of strain transplantation in the treatment of obesity is determined by extracting intestinal flora of the obese patient and the healthy patient respectively, and the effects of different strains on obesity are accurately screened and predicted.
In another specific embodiment, the detection population is a chronic enteritis patient, the control population is a healthy patient, and the effectiveness of bacterial species transplantation in the treatment of chronic enteritis is determined by respectively extracting intestinal flora of the chronic enteritis patient and intestinal flora of the healthy patient, and the treatment effect of different bacterial species on the chronic enteritis patient is accurately screened and predicted.
In another specific embodiment, the test population is cancer patients and the control population is healthy, and the effectiveness of the bacterial species transplantation for enhancing human immunity is determined by extracting intestinal flora of the cancer patients and healthy individuals.
In some embodiments, referring to fig. 2, the quantitatively analyzing the data quality of the microorganism abundance value by using a preset linear discriminant method to determine a flora influence factor includes:
s201, acquiring a mean value and a covariance matrix of a strain abundance data set of a detection crowd and acquiring a healthy mean value and a healthy covariance matrix of a strain abundance data set of a control crowd;
s202, based on the mean value and the covariance matrix, projecting strain abundance data of the detection population, and determining detection data projection when the difference between classes is the largest and the difference in the classes is the smallest; based on the comparison mean and the health covariance matrix, projecting strain abundance data of the comparison population, and determining comparison data projection when the difference between classes is the largest and the difference in the classes is the smallest;
s203, determining the mean difference of the detected abundance according to the detection data projection; determining a contrast abundance mean difference according to the contrast data projection;
s204, determining the flora influence factor according to the arithmetic mean of the detection abundance mean difference and the control abundance mean difference.
In this embodiment, the data quality parameter of the flora abundance data is preferably an LDA value obtained by linear discriminant analysis; the calculation method comprises the following steps:
first, specific strain abundance data for the detection population and the control populationWhere i=1, 2, …, n 1 +n 2 ，n 1 To detect the sample content of the crowd, n 2 Sample content, x, of control population i For the strain abundance value of sample i, y i Class identifier, y, for sample i i ∈{C 1 ,C 2 }，C 1 For marking the detection crowd category, C 2 For marking the class of the control crowd, respectively obtaining the average mu of two classes of samples j And covariance matrix X j The following are provided:
then, respectively projecting the specific strain abundance data of the detection crowd and the control crowd to straight lines to obtain data projections when the difference between classes is as large as possible and the difference in the classes is as small as possible, namely, projecting the specific strain abundance data of the detection crowd and the control crowd
When the difference between classes is as large as possible and the difference within the classes is as small as possible, for the purpose of optimization, it is noted that:
wherein w is a linear vector, and the abundance value x of the strain is given to any sample i Its projection on the straight line w is w T x i ，Is the difference between classes, w T X j w is intra-category difference, j=c 1 ,C 2 ；
Defining an intra-class divergence matrix S w The following are provided:
defining an inter-class divergence matrix S b The following are provided:
the optimization objective is rewritten as:
for both classes, there is S b The direction of w is transversely parallel toSo make->Then there are:the method can obtain: />λ is the eigenvalue and w is the eigenvector, i.e. the projected straight line.
The data projection when the difference between the classes is as large as possible and the difference in the classes is as small as possible is obtained, specifically:
obtaining a characteristic value lambda when an optimization objective is reached * And feature vector w * Obtaining projection matrix lambda * w * For a specific strain abundance value x of a sample, the data projection x 'is x' = (lambda) * w * ) T x。
Thus, the obtained specific strain abundance data projectionCalculating the projection mean value difference delta' of the two types of data; according to the specific strain abundance data of the detection population and the control populationCalculating an abundance mean difference delta; the method comprises the following steps:
the arithmetic mean of the projection mean difference and the abundance mean difference is taken as the LDA value, i.e., lda= (δ+δ')/2.
It should be noted that, the LDA uses linear discriminant projection analysis to quantify the differences between the classes of the adopted flora abundance data, and we consider that the data with large differences between classes and small differences within classes have higher quality, so that the flora influence factors obtained by data calculation are more reliable, thereby reducing the uncertainty caused by the small sample data size as much as possible.
In a specific example, the following table shows the flora-affecting factors for the relevant target species of weight loss:
the weights of the bacteria of Odoribacter, bifidobacterium, christensenella, faecalibacterium, holdemanella, lachnoclostridium are obtained according to the LEfSe score chart:
| genus of bacteria | Weighting of |
| Bifidobacterium | 4.5 |
| Faecalibacterium | 4.4 |
| Holdemanella | 4.3 |
| Odoribacter | 4.2 |
| Christensenella | 4.1 |
| Lachnoclostridium | -4.2 |
In some embodiments, the flora offset may be expressed by the following formula:
wherein v is the weighted offset of the abundance of the flora of the sample to be predicted and the abundance of the control flora, k is the number of species of the differential flora, O t A flora offset for a t-th type differential flora of the sample to be predicted; r is (r) t Is the t-class differential flora influence factor.
In this embodiment, the flora offset is preferably obtained as follows:
when the abundance of the flora is greater than 95% of the abundance of the flora of a control population, the flora offset of the sample is 4 points; when the abundance of the flora is higher than the abundance of the flora in the control population of 90%, the flora offset of the sample is 3 points; when the abundance of the flora is higher than that of a control population with the flora abundance of more than 85%, the flora offset of the sample is 2 points; the sample has a flora shift of 1 score when the abundance of the flora is greater than 80% of the abundance of the flora in the control population.
In some embodiments, the score of the bacterial effectiveness scoring system is obtained by the product of the bacterial population affecting factor and the bacterial population offset.
In this example, the effectiveness of the sample strain is characterized by the population affecting factor and the population offset, and the effectiveness of the strain sample can be characterized qualitatively.
In some embodiments, the inputting the strain to be transplanted into the strain effectiveness scoring system, determining the transplanting effectiveness of the strain to be transplanted comprises:
obtaining the score of the strain to be transplanted based on the strain effectiveness scoring system;
and determining the efficiency of the strain to be transplanted according to the score.
In the embodiment, the effectiveness of the strain for the target body transplantation is determined through the quantification of the score, so that the efficiency of the strain transplantation is greatly improved.
The samples of the detection population and the intestinal bacteria transplantation treatment group are subjected to microorganism sequencing, donor flora and fecal samples before and after target body transplantation are analyzed in batches, and the intestinal flora effective for the target body is determined based on high-throughput sequencing data.
Based on the above-mentioned weight management effect prediction method based on intestinal fungus transplantation, the embodiment of the present invention further provides a weight management effect prediction device 300 based on intestinal fungus transplantation, referring to fig. 3, where the weight management effect prediction device 300 based on intestinal fungus transplantation includes an obtaining module 310, a flora influence factor determining module 320, a flora offset determining module 330, a scoring system constructing module 340 and a predicting module 350.
An acquisition module 310, configured to acquire a microorganism abundance value and microorganism differential genus information in the intestinal flora collection of the detection population and the control population;
the flora influence factor determining module 320 is configured to quantitatively analyze the data quality of the abundance value of the microorganism by using a preset linear discriminant method, and determine a flora influence factor;
a flora offset determining module 330, configured to determine a flora offset of the differential microorganism genus according to a difference between the abundance of the microorganism and the healthy microorganism genus based on the flora influence factor;
the scoring system construction module 340 is configured to construct a bacterial effectiveness scoring system according to the association relationship between the bacterial population influence factor and the bacterial population offset;
the prediction module 350 is configured to obtain a strain to be transplanted, input the strain to be transplanted into the strain effectiveness scoring system, and determine the transplanting effectiveness of the strain to be transplanted.
As shown in fig. 4, the present invention further provides an electronic device based on the method for predicting the weight management effect based on the intestinal fungus transplantation, where the electronic device may be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server, and other computing devices. The electronic device includes a processor 410, a memory 420, and a display 430. Fig. 4 shows only some of the components of the electronic device, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead.
The memory 420 may in some embodiments be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 420 may also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like. Further, the memory 420 may also include both internal storage units and external storage devices of the electronic device. The memory 420 is used for storing application software installed on the electronic device and various data, such as program codes for installing the electronic device. The memory 420 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 420 stores an intestinal transplant-based weight management effect prediction program 440, and the intestinal transplant-based weight management effect prediction program 440 may be executed by the processor 410, thereby implementing the intestinal transplant-based weight management effect prediction method according to the embodiments of the present application.
The processor 410 may in some embodiments be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 420, for example, performing weight management effect prediction methods based on intestinal fungus transplantation, etc.
The display 430 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 430 is used for displaying information at the weight management effect prediction device based on intestinal fungus transplantation and for displaying a visual user interface. The components 410-430 of the electronic device communicate with each other over a system bus.
Of course, those skilled in the art will appreciate that implementing all or part of the above-described methods may be implemented by a computer program for instructing relevant hardware (e.g., a processor, a controller, etc.), where the program may be stored in a computer-readable storage medium, and where the program may include the steps of the above-described method embodiments when executed. The storage medium may be a memory, a magnetic disk, an optical disk, or the like.
The invention relates to application of effective bacteria determined by the method in preparing a medicament for treating obesity.
Further, the effective bacteria are Odoribacterium, bifidobacterium, christensenella, faecalibacterium and Holdemanella.
In the embodiment, the effective genus is determined by a weight management effect prediction method based on intestinal fungus transplantation, and the method is applied to obesity treatment, so that the aim of efficiently solving the obesity problem by utilizing intestinal fungus transplantation is fulfilled.
The medicament for treating obesity based on the effective genus can be in a liquid, solid or granular state, or can be in oral administration, injection or other forms, so long as the effective genus can be ensured to act on intestinal tracts, and the purpose of treating obesity by utilizing the genus can be realized.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any other corresponding changes and modifications made in accordance with the technical idea of the present invention shall be included in the scope of the claims of the present invention.
Claims (10)
1. A method for predicting weight management effects based on intestinal fungus transplantation, comprising:
acquiring the microbial abundance value and microbial differential bacteria information in intestinal flora sets of the detection population and the control population;
quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discrimination method, and determining a flora influence factor;
determining a flora offset of the differential microorganism genus based on the flora influence factor according to the differential situation between the microbial abundance value and the healthy microorganism genus;
constructing a bacterial effectiveness scoring system according to the association relation between the bacterial colony influence factors and the bacterial colony offset;
obtaining strains to be transplanted, inputting the strains to be transplanted into a strain effectiveness scoring system, and determining the transplanting effectiveness of the strains to be transplanted.
2. The method for predicting weight management effects based on intestinal fungus transplantation according to claim 1, wherein said microbial abundance values are obtained by normalizing DNA concentration, and said microbial abundance values are differentially examined by a preset nonparametric examination method.
3. The method for predicting weight management effects based on intestinal fungus transplantation according to claim 1, wherein the quantitative analysis of the data quality of the abundance of microorganisms by using a preset linear discriminant method, determining a flora influencing factor, comprises:
acquiring a mean value and a covariance matrix of a strain abundance data set of a detection crowd and acquiring a healthy mean value and a healthy covariance matrix of a strain abundance data set of a control crowd;
based on the mean and covariance matrix, projecting strain abundance data of the detection population, and determining detection data projection when the difference between classes is the largest and the difference in the classes is the smallest; based on the comparison mean and the health covariance matrix, projecting strain abundance data of the comparison population, and determining comparison data projection when the difference between classes is the largest and the difference in the classes is the smallest;
determining the average difference of the detected abundance according to the detection data projection; determining a contrast abundance mean difference according to the contrast data projection;
determining the flora influence factor according to the arithmetic average of the detection abundance mean difference and the control abundance mean difference.
4. The method for predicting weight management effects based on intestinal fungus transplantation according to claim 1, wherein said flora shift is expressed by the following formula:
wherein v is the weighted offset of the abundance of the flora of the sample to be predicted and the abundance of the control flora, k is the number of species of the differential flora, O t A flora offset for a t-th type differential flora of the sample to be predicted; r is (r) t Is the t-class differential flora influence factor.
5. The method for predicting weight management effects of an intestinal fungus graft according to claim 1, wherein the score of the bacterial species effectiveness scoring system is obtained by multiplying the bacterial population affecting factor by the bacterial population offset.
6. The method for predicting weight management effects based on enterobacteria transplantation according to claim 5, wherein said inputting the strain to be transplanted into the strain effectiveness scoring system, determining the transplantation effectiveness of the strain to be transplanted, comprises:
obtaining the score of the strain to be transplanted based on the strain effectiveness scoring system;
and determining the efficiency of the strain to be transplanted according to the score.
7. The method for predicting weight management effects of enterobacteria-based transplantation according to claim 5, wherein said determining the efficiency of the species to be transplanted based on the score comprises:
judging whether the score is larger than a preset effective score value or not;
if the strain is larger than the target effective strain, searching the strain to be transplanted.
8. A weight management effect prediction device based on intestinal fungus transplantation, comprising:
the acquisition module is used for acquiring the microbial abundance value and the microbial differential genus information in the intestinal flora collection of the detection crowd and the control crowd;
the flora influence factor determining module is used for quantitatively analyzing the data quality of the microorganism abundance value by adopting a preset linear discrimination method to determine the flora influence factor;
the flora offset determining module is used for determining the flora offset of the microbial differential bacteria according to the difference condition between the microbial abundance value and the healthy bacteria based on the flora influence factor;
the scoring system construction module is used for constructing a bacterial effectiveness scoring system according to the association relation between the flora influence factors and the flora offset;
the prediction module is used for obtaining strains to be transplanted, inputting the strains to be transplanted into the strain effectiveness scoring system, and determining the transplanting effectiveness of the strains to be transplanted.
9. Use of an effective genus identified according to the method of claims 1-7 for the manufacture of a medicament for the treatment of obesity.
10. The use according to claim 9, wherein the effective bacteria are odobacterium, bifidobacterium, christenseneella, faecalibbacterium and Holdemanella.
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