A scalp microecological analysis method based on phenotype-age dynamic modeling, a storage medium and an equipment

Abstract

The application discloses a scalp microecological analysis method based on phenotype-age dynamic modeling, comprising the following steps: acquiring age and scalp phenotype data of a measured person, and microbiome sequencing data of a corresponding scalp sample; calculating scalp microbial relative abundance and constructing a matrix; calculating the correlation between age and each phenotype index; constructing a dynamic change model based on the key phenotype data and age data; dividing age into multiple continuous intervals based on the extracted features to form groups; and comparing and analyzing data between scalp microbiomes based on the groups. The method has the advantages of high universality, strong ease of use and strong comparability in scalp microecological analysis, is simple and reliable, easy to understand and use, convenient to operate, and can provide a more scientific and reasonable grouping strategy for scalp microecological genomics research based on age grouping, so that more accurate comparison and analysis can be obtained.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology, specifically relating to a scalp microecological analysis method based on phenotypic-age dynamic modeling. Background Technology

[0002] The human scalp is a complex ecosystem, home to a diverse community of bacteria, fungi, and other microorganisms. These microorganisms not only inhabit the scalp environment but also secrete specific components to defend against harmful microorganisms and maintain the stability of the scalp's microecology. Studies have shown that the scalp microbiota is closely related to scalp health and various disease states (such as scalp itching and dandruff). Scalp health depends on the balance of the microecology; once this balance is disrupted, it can trigger a series of scalp problems. Therefore, in-depth research into the relationship between scalp phenotype and the microecology is of great significance.

[0003] Currently, commonly used sequencing technologies in scalp microbiota research include 16S rDNA sequencing and shotgun metagenomic sequencing. After obtaining sequencing data, group analysis and result visualization are required. However, despite precise measurement and detailed recording of volunteers' age and scalp phenotype, the choice of grouping strategy still significantly impacts subsequent data comparison and analysis. Existing studies often use conventional age grouping methods, failing to fully consider the dynamic trends of different phenotypes with age. The grouping criteria are not strongly correlated with the microbiota itself, potentially leading to biases in microbiota analysis results between different age groups.

[0004] Therefore, this paper presents a scalp microecology analysis method based on phenotypic-age dynamic modeling. This method has important reference value for constructing scientific and reasonable age groups, effectively revealing the changing patterns of scalp microecology in different age groups, and thus supporting targeted scalp health research and the development of related care products. Summary of the Invention

[0005] This invention aims to address the above-mentioned technical problems by using bioinformatics technology to calculate the correlation between scalp phenotypes and scalp microecology. It constructs a model using the dynamic change trend of the most correlated phenotype with age, thereby enabling scientific and reasonable age grouping and subsequent scalp microecological analysis, including inter-group species distribution and alpha diversity analysis.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a scalp microecological analysis method based on phenotypic-age dynamic modeling, comprising the following steps: S1. Obtain the age and scalp phenotype data of the test subjects, as well as the microbiome sequencing data of the corresponding scalp samples; S2. Calculation and matrix construction of relative abundance of scalp microorganisms: After the microbiome sequencing data in step S1 is processed, the number of microbial reads at different taxonomic levels is normalized in the same sample to obtain the relative abundance matrix of scalp microorganisms. S3. Calculate the correlation between age and various phenotypic indicators: The age information of the test subjects obtained in step S1 is analyzed for correlation with the collected scalp phenotypic indicators to select at least one phenotypic with the highest correlation. S4. Based on the aforementioned key phenotypic data and age data, construct a dynamic change model: Plot a scatter plot of the most correlated phenotypic data in step S3 with age, fit it with multinomial regression, select the best model, extract at least one mathematical feature point or trend reversal feature from the best dynamic change model, and calculate its mathematical inflection point and / or stationary point. S5. Based on the extracted features, the age is divided into multiple continuous intervals to form groups: Based on the mathematical feature points extracted in step S4, and combined with the feature points and data trends, the overall shape and trend of the fitted curve within the effective age range are analyzed to form groups. S6. Based on the grouping, perform comparative analysis of scalp microbiome data: Based on the age grouping obtained in step S5, the relative abundance matrix of scalp microorganisms obtained in step S2 is used to conduct comparative analysis of scalp microbiome data among the groups.

[0007] In some embodiments of the present invention, in step S1, the various phenotypic indicators of the scalp include, but are not limited to, any one or more of the following: oil content, moisture content, transepidermal water loss, pH value, scalp ultraviolet fluorescence density, number of hair roots, number of hair follicles, gray hair density, and number of gray hairs.

[0008] In some embodiments of the present invention, in step S2, different taxonomic levels include, but are not limited to, any one or more of the following: phylum level, family level, genus level, and species level.

[0009] In some embodiments of the present invention, in step S3, the correlation analysis is Spearman rank correlation analysis and / or Pearson correlation analysis.

[0010] In some embodiments of the present invention, in step S3, Spearman rank correlation analysis and / or Pearson correlation analysis are performed using the rcorr function in the Hmisc package of R software.

[0011] In some embodiments of the present invention, in step S4, the method for selecting the best model is: by comparing the goodness of fit of different polynomial degrees (e.g., R0).2 The optimal polynomial model is selected using methods such as AIC or BIC. The software used is R.

[0012] In some embodiments of the present invention, in step S6, the comparative analysis of scalp microbiome data includes, but is not limited to, any one or more of the following: average relative abundance calculation, α diversity calculation, and result visualization. The software used for analysis and plotting is R software.

[0013] In a second aspect, the present invention provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the scalp health assessment method as described in the present invention.

[0014] Thirdly, the present invention provides a computer device comprising: one or more processors, and a memory; wherein the memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the scalp health assessment method as described in the present invention.

[0015] The scalp microecology analysis method based on phenotypic-age dynamic modeling in this invention has the following advantages compared with existing microecology analysis methods: (1) Compared with the existing human tissue microecology analysis methods, this invention focuses on scalp samples, which can more specifically analyze the correlation between scalp phenotype and scalp microecology; (2) Most current related studies rely on experience or divide age into groups at equal intervals, ignoring the trend of phenotypic changes. Compared with other human tissue microecological analysis methods based on age grouping, this invention proposes a method based on phenotypic and age dynamic modeling, which can group age according to the trend of phenotypic changes with age, making the accuracy of subsequent inter-group microecological analysis higher and the biological significance clearer.

[0016] The scalp microecology analysis method based on phenotype-age dynamic modeling of the present invention has the advantages of high versatility, ease of use, and strong comparability in scalp microecology analysis. The method is simple, reliable, easy to understand and use, and convenient to operate. It can provide a more scientific and reasonable grouping strategy for age-based scalp microecology research, thereby obtaining more accurate comparative analysis. Attached Figure Description

[0017] Figure 1 This is a flowchart of the scalp microecology analysis method based on phenotypic-age dynamic modeling in an embodiment of the present invention.

[0018] Figure 2 This is a trend and fitting curve of the change in the amount of gray hair with age in an embodiment of the present invention.

[0019] Figure 3 This is a Venn diagram showing the distribution of scalp microorganisms in different groups in this invention.

[0020] Figure 4 This is a box plot showing the α-diversity of scalp microorganisms in different groups in an embodiment of the present invention. Detailed Implementation

[0021] To facilitate understanding of the present invention, a more complete description will be given below with reference to specific embodiments. Preferred embodiments of the invention are shown in the accompanying drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0023] Unless otherwise specified, the experimental methods used in the following examples and comparative examples are conventional methods, and the materials and reagents used are commercially available. Where specific software parameters or conditions are not specified in the examples, they are performed according to the parameters or conditions described in the literature in this field or according to the software manual.

[0024] refer to Figure 1 This invention provides a scalp microecological analysis method based on phenotype-age dynamic modeling, the main implementation of which includes the following steps: The study obtained the age and scalp phenotype information of the participants and sequenced the scalp samples; constructed a relative abundance matrix of scalp microorganisms; calculated the correlation between age and various phenotype data; constructed dynamic change models of several phenotypes with the highest correlation with age; extracted at least one mathematical feature point based on the best model, and determined age grouping by combining the feature point and the overall change trend; and conducted inter-group comparative analysis of scalp microecology based on the grouping.

[0025] (1) Obtain the age and scalp phenotype data of the test subjects, as well as the microbiome sequencing data of the corresponding scalp samples.

[0026] The study collected age information from several participants; used equipment to collect various scalp phenotypic data, including oil content, moisture content, transepidermal water loss, pH value, scalp UV fluorescence density, number of hair roots, number of hair follicles, gray hair density, and number of gray hairs; and sampled scalp microorganisms for microbiome sequencing.

[0027] (2) Calculation of relative abundance of scalp microorganisms and construction of matrix.

[0028] After the microbiome sequencing data from step (1) are processed, the number of microbial reads at different taxonomic levels is normalized for the same sample to obtain the relative abundance matrix of scalp microorganisms. Different taxonomic levels include, but are not limited to, phylum, family, genus, and species levels.

[0029] "Normalization" is a data standardization processing technique. Its specific implementation method is as follows: at any specified taxonomic level, the relative abundance data of various types of microorganisms detected are scaled proportionally so that the sum of the relative abundance of all microorganisms in each sample is equal to 1.

[0030] (3) Calculate the correlation between age and each phenotypic indicator.

[0031] The software was used to perform a correlation analysis between the age information of the test subjects obtained in step (1) and the various phenotypic indicators of the scalp collected (the Spearman correlation coefficient and / or Pearson correlation coefficient and significance were calculated) and at least one phenotypic with the highest correlation was selected.

[0032] (4) Based on key phenotypic data and age data, construct a dynamic change model.

[0033] Plot a scatter plot of the changing trends between several phenotypes with the highest correlation and age; compare the goodness of fit of each fitting model by fitting the curves using polynomial regression, and select the best polynomial fitting model; and extract at least one mathematical feature point or trend inflection feature from the best dynamic change model, and calculate its mathematical inflection point and / or stationary point.

[0034] (5) Based on the extracted features, the age is divided into multiple continuous intervals to form groups.

[0035] Based on the mathematical feature points extracted in step (4), and combined with the feature points and data trends, the overall shape and trend of the fitted curve within the effective age range are analyzed, and the age is divided into several consecutive groups.

[0036] (6) Comparative analysis of scalp microbiome data based on the grouping.

[0037] The α-diversity of scalp microbiota was calculated using software; and based on the age grouping, the distribution, average relative abundance, and α-diversity of scalp microbiota among different groups were compared and analyzed.

[0038] On the other hand, the present invention provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the scalp health assessment method as described in the present invention.

[0039] In another aspect, the present invention provides a computer device comprising: one or more processors, and a memory; the memory storing computer-readable instructions, which, when executed by the one or more processors, perform the steps of the scalp health assessment method as described in the present invention.

[0040] Example The scalp microecology analysis method based on phenotypic-age dynamic modeling provided in this invention includes the following steps: First, recruit a number of participants and collect their age and other information. Then, use relevant equipment to collect phenotypic data such as scalp oil content, moisture content, transepidermal water loss, pH value, scalp ultraviolet fluorescence density, number of hair roots, number of hair follicles, gray hair density, and number of gray hairs. Finally, use 16S rDNA sequencing technology to sequence the scalp microbial samples of the participants.

[0041] After the sequencing data is processed, the number of reads for each OTU (Operational Taxonomic Unit) is obtained. The OTUs are classified according to different taxonomic levels. At the same taxonomic level, the samples are normalized using Python or R software so that the sum of the relative abundance of each microorganism at that taxonomic level is 1. In this invention, a microbial relative abundance matrix at the genus level is constructed.

[0042] The Spearman rank correlation coefficient and / or Pearson correlation coefficient between scalp phenotypic indicators and age, and their significance, were calculated using the rcorr function in the Hmisc package of R software.

[0043] Several phenotypic indicators with the highest correlation were selected, and scatter plots of their changes with age were generated using the ggplot2 package in R. Multinomial regression was then performed using the stats package in R, and the goodness-of-fit R-squared of different models was calculated. 2 Select the best-fitting model, extract at least one mathematical feature point or trend reversal feature, and calculate its mathematical inflection point and / or stationary point.

[0044] Based on the mathematical characteristic points of the best-fit model and the overall trend of the fitted curve, several reasonable continuous groups are divided within the effective age range.

[0045] The α-diversity of each sample was calculated using the vegan package in R software. The ggplot2 package in R software was used to plot the results. Based on the age groups obtained above, an inter-group comparative analysis of the scalp microbiome structure was performed.

[0046] Experimental results: 1. Phenotypic data and scalp microbial sample collection of the test subjects A number of participants were recruited, with a minimum age of 17 years and a maximum age of 67 years, and the age distribution was relatively even. The Sebumeter MB560 was used to detect scalp oil content, the Dermalab was used to detect scalp moisture content, the TM nano was used to detect transepidermal water loss, the pH meter was used to detect scalp pH, and Spocs were used to detect phenotypic data such as scalp UV fluorescence density, hair count, hair follicle count, gray hair density, and gray hair quantity. Microbial samples were collected from the participants' scalps using sterile swabs and 16S rDNA sequencing was performed.

[0047] 2. Construction of the relative abundance matrix of scalp microorganisms 16S rDNA sequencing identified 2744 bacterial genera. Sample normalization was performed using a Python script to obtain a relative abundance matrix of scalp microorganisms. Propionibacterium genus (…) Cutibacterium Staphylococcus spp. had the highest average relative abundance across all samples, at 42.09%; Staphylococcus The average relative abundance of *L.* among all samples was second only to *Propionibacterium*, at 37.61%; *L.* spp. ranked third. Lawsonella Its average relative abundance across all samples was 5.83%.

[0048] 3. Correlation calculation between scalp phenotypic indicators and age The Pearson correlation analysis between the above nine scalp phenotypic indicators and age was performed using the rcorr function in the Hmisc package of R software. The results are shown in Table 1. Significance is indicated by "***". p <0.01.

[0049] Table 1: Pearson correlation analysis results between scalp phenotypic indicators and age

[0050] As shown in Table 1, moisture content, transcutaneous moisture loss, gray hair density, and gray hair quantity are positively correlated with age; pH value, sebum content, ultraviolet fluorescence density, hair root count, and hair follicle count are negatively correlated with age. Among these, the gray hair quantity, gray hair density, ultraviolet fluorescence density, and sebum content have the highest absolute correlation values. Subsequently, a dynamic change model was constructed using the above four scalp phenotypic indicators (sebum content, ultraviolet fluorescence density, gray hair density, and gray hair quantity) that are most correlated with age.

[0051] 4. Multinomial regression fitting to construct the optimal dynamic change model. The stats package in R software was used to perform a polynomial fitting on the changes in oil content, ultraviolet fluorescence density, gray hair density, and gray hair quantity with age. The goodness of fit was determined by the coefficient of determination R. 2 An evaluation was conducted. The results are shown in Table 2.

[0052] Table 2: Optimal Dynamic Fit Model of Four Scalp Phenotypic Indices and Age

[0053] As shown in Table 2, the goodness of fit R 2 The value closest to 1 is the number of gray hairs, which is also the phenotypic indicator with the highest Pearson correlation coefficient with age. Its best polynomial degree is quadratic, and the polynomial fitting equation is: y = 22.822 - 1.697*x + 0.031*x 2 Therefore, the number of gray hairs was chosen as the optimal dynamic model for subsequent analysis.

[0054] Plot the fitted curve as follows Figure 2 Through mathematical calculations, the stationary point of the dynamic model polynomial equation for the change in the amount of gray hair with age was found to be 27 years old. This is the mathematical feature point in this embodiment.

[0055] 5. Determine age grouping based on feature points and overall shape Based on the stationed data, we further analyzed the overall shape of the fitted curve across the entire age range (17-67 years old).

[0056] pass Figure 2 Visual analysis revealed that, with the 27-year-old age mark as the center, the number of gray hairs remained relatively low across the 17-37 age range. At age 37, the rate of change in the curve steadily increased. Around age 50, the curve showed a significant upward trend, rising rapidly after age 50, suggesting a possible corresponding point in another important physiological change.

[0057] Therefore, based on the above mathematical characteristics and graphical trend analysis, the age groups were finally determined as follows: The first group (17-37 years old): Centered on the mathematical station (27 years old), covering the age groups on both sides where the changing trends are relatively consistent. In this embodiment, this group is named the youth period.

[0058] The second group (38-50 years old): starting at age 37, covers the transition period from the initial trend shift to the second major turning point. In this embodiment, this group is named the critical change period.

[0059] The third group (51-67 years old): Starting from age 50, this group corresponds to the age range after the second shift in the trend. In this embodiment, this group is named the aging period.

[0060] 6. Scalp microecological analysis was conducted according to the groups. Based on the age groupings mentioned above, the average relative abundance and α diversity of microorganisms in the three groups were calculated. Venn diagrams of microbial distribution and box plots of α diversity were plotted using the ggplot2 package in R software.

[0061] The Venn diagram showing the distribution of the three groups of microorganisms is shown below. Figure 3 The results showed that the three groups shared a total of 1,850 genera of microorganisms; the aging period (51-67 years old, named "Y51_67" in the following chart) had the most genera of microorganisms, and the aging period also had the most unique microorganisms.

[0062] The average relative abundance of scalp microorganisms in the three groups is shown in Table 3. The results showed that the average relative abundance of Propionibacterium spp. was significantly higher in the critical period of change (38-50 years old, named "Y38_50" in the following tables) than in the youth period (17-37 years old, named "Y17_37" in the following tables) and the aging period. The average relative abundance of Staphylococcus spp. gradually decreased with age, and a significant decrease occurred in the critical period of change. The average relative abundance of Lawsonia spp. gradually decreased with age.

[0063] Table 3: Average relative abundance of scalp microorganisms

[0064] Box plots of scalp microbial alpha diversity (Chao1 index) for the three groups are shown below. Figure 4 As shown, the results indicate that the Chao1 index gradually increases with age, indicating increased α-diversity. Furthermore, the Chao1 index of the microbiome in the aging period was significantly higher than that in the previous two age groups, and there were significant differences among the three groups. p <2e-16).

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A scalp microecological analysis method based on phenotypic-age dynamic modeling, characterized in that... Includes the following steps: S1. Obtain the age and scalp phenotype data of the test subjects, as well as the microbiome sequencing data of the corresponding scalp samples; S2. Calculation and matrix construction of relative abundance of scalp microorganisms: After the microbiome sequencing data in step S1 is processed, the number of microbial reads at different taxonomic levels is normalized in the same sample to obtain the relative abundance matrix of scalp microorganisms. S3. Calculate the correlation between age and various phenotypic indicators: The age information of the test subjects obtained in step S1 is analyzed for correlation with the collected scalp phenotypic indicators to select at least one phenotypic with the highest correlation. S4. Based on the aforementioned key phenotypic data and age data, construct a dynamic change model: Plot a scatter plot of the most correlated phenotypic data in step S3 with age, fit it with multinomial regression, select the best model, extract at least one mathematical feature point or trend reversal feature from the best dynamic change model, and calculate its mathematical inflection point and / or stationary point. S5. Based on the extracted features, the age is divided into multiple continuous intervals to form groups: Based on the mathematical feature points extracted in step S4, and combined with the feature points and data trends, the overall shape and trend of the fitted curve within the effective age range are analyzed to form groups. S6. Based on the grouping, perform comparative analysis of scalp microbiome data: Based on the age grouping obtained in step S5, the relative abundance matrix of scalp microorganisms obtained in step S2 is used to conduct comparative analysis of scalp microbiome data among the groups.

2. The method according to claim 1, characterized in that, In step S1, the scalp phenotypic indicators include any one or more of the following: oil content, moisture content, transepidermal water loss, pH value, scalp ultraviolet fluorescence density, number of hair roots, number of hair follicles, gray hair density, and number of gray hairs.

3. The method according to claim 1, characterized in that, In step S2, different taxonomic levels include any one or more of the following: phylum, family, genus, and species.

4. The method according to claim 1, characterized in that, In step S3, the correlation analysis is Spearman rank correlation analysis and / or Pearson correlation analysis.

5. The method according to claim 4, characterized in that, In step S3, Spearman rank correlation analysis and / or Pearson correlation analysis are performed using the rcorr function in the Hmisc package of R software.

6. The method according to claim 1, characterized in that, In step S4, the method for selecting the best model is to select the best polynomial model by comparing the goodness of fit of different polynomial degrees.

7. The method according to claim 1, characterized in that, In step S6, the comparative analysis of scalp microbiome data includes any one or more of the following: mean relative abundance calculation, α diversity calculation, and result visualization.

8. The method according to claim 6 or 7, characterized in that, The software used for analysis and plotting is R.

9. A storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the scalp health assessment method as described in any one of claims 1 to 8.

10. A computer device comprising: One or more processors, and a memory; the memory storing computer-readable instructions that, when executed by one or more processors, perform the steps of the scalp health assessment method as described in any one of claims 1 to 8.

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