CN111128378A - Prediction method for evaluating development age of infant intestinal flora - Google Patents
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Abstract
The invention discloses a prediction method for evaluating the development age of infant intestinal flora, which comprises the following steps: acquiring intestinal flora data of infants; constructing a prediction model, namely a classification data model, by linear discriminant analysis and random forests on the basis of the intestinal flora data; inputting a sample to be tested into a prediction model for prediction, and outputting classification data to obtain a prediction result; obtaining the development age of the intestinal flora of the sample to be detected according to the prediction result; comparing the development age of the intestinal flora of the sample to be detected with the actual age, and judging whether the intestinal tracts of the infants are disordered or have development deviation; the invention adopts the combination of linear discriminant analysis and random forest to construct a prediction model, thereby greatly improving the accuracy, predicting the corresponding age through the prediction model, and then evaluating whether the flora is dysplastic through the comparison of the predicted age and the actual age.
Description
Technical Field
The invention relates to the research field of intestinal flora prediction, in particular to a prediction method for evaluating the development age of infant intestinal flora.
Background
In the prior art, few methods for detecting human intestinal microorganisms are used, for example, in the CN109448842A patent, linear discrimination is not used, the content of the patent is not directed to infants or judgment of newly added individual individuals, whether the intestinal microecology of the human intestinal is unbalanced or not is mainly evaluated, the age is not predicted, and the reference of the age is not used, and the prediction accuracy is less than 70%, for example, in the CN108345768A patent, the intestinal microecology is predicted to be the result of sub-health, and the sub-health may be aggravated at the same time, so as to cause diseases. The intestinal microecology is the most important and huge, especially special ecosystem of the organism. The large amount of microorganisms in the intestinal tract are constantly in dynamic equilibrium and relatively stable. Numerous factors influence this balance. The occurrence, development and treatment outcome of human sub-health are accompanied with the change or unbalance of intestinal microecological normal flora, thereby affecting the growth and development of infants. However, no method for predicting the development age of the intestinal flora of infants has been provided so far.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a prediction method for evaluating the development age of intestinal flora of infants, establishes a prediction model, and judges whether the development of the intestinal flora is disordered or not by predicting the age of the intestinal flora.
The purpose of the invention is realized by the following technical scheme:
a prediction method for assessing the development age of the gut flora of an infant, comprising the steps of:
acquiring intestinal flora data of infants as original data, and storing the intestinal flora data in a reference data set of a database;
preprocessing the intestinal flora data by linear discriminant analysis based on the intestinal flora data to obtain classified data, and constructing a prediction model by random forest training;
inputting the sample to be tested into a prediction model for prediction to obtain a prediction result, and obtaining the development age of the intestinal flora of the sample to be tested according to the prediction result;
and comparing the development age of the intestinal flora of the sample to be detected with the actual age, and judging whether the intestinal tracts of the infants are disordered or have development deviation.
Further, the acquiring of the intestinal flora data of the infant is specifically as follows: sequencing and analyzing by 16S amplicon sequencing technology, collecting the excreta of healthy infants of 1-48 months for testing, observing the infant condition and recording in a benchmark dataset of a database.
Further, the intestinal flora data is 525-dimensional 10 classification data with labels, wherein 525-dimensional means that a flora structure is composed of 525 strain classification units; the 10-category data includes 8 categories of 1-48 months and two categories consisting of young and middle aged.
Further, the building of the prediction model specifically includes:
preprocessing the 525-dimensional 10 classified data with labels by linear discriminant analysis based on the intestinal flora data and corresponding sampling age information, namely reducing dimensions to obtain low-dimensional data; and dividing the low-dimensional data into training data and testing data by adopting a random forest, setting the number of basic classifiers as K, and training to obtain a prediction model.
Further, the ratio of the training data to the test data is 7: 3; the number K of the basic classifiers is more than 100.
Further, the predicting is performed to obtain a prediction result, specifically:
determining the importance of each original feature of an original data set, namely the feature importance of an original flora, according to classification data, performing disorder arrangement operation on new features obtained by linear discriminant analysis conversion to obtain disorder arrangement features, classifying the disorder features by using a random forest again, and judging the importance of each disorder arrangement feature according to the difference between the precision of a prediction model obtained each time and the precision of an original model to obtain the disorder arrangement importance;
calculating a correlation coefficient between each original feature and each disorder arrangement feature, determining the correlation between the original features and the disorder arrangement features, and obtaining a Pearson correlation coefficient absolute value between the original features and the disorder arrangement features as a weight, wherein the feature importance of the original features is calculated as follows:
wherein, FiCharacteristic importance of the ith original species, pi,jIs the Pearson correlation coefficient between the ith original species and the jth new feature, fjThe importance of the j-th new feature is ranked out of order.
Further, the development age of the intestinal flora of the sample to be detected is compared with the actual age, and whether the intestinal tracts of the infants are disordered or develop deviation is judged, specifically:
if the actual age deviation between the predicted age bracket and the tested target individual is less than N months, the age bracket is normal; if the deviation is more than N months, the flora is dysbiosis, and an intervention scheme needs to be further formulated according to actual conditions.
Further, N is 12.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method establishes a data set through amplicon sequencing data acquisition, adopts linear discriminant analysis and random forest establishment prediction models to support the discrimination of multiple age groups, has wide coverage range, improves the prediction accuracy, pays attention to the development condition of infant intestinal flora, can avoid the problems of a series of subsequent immunity, metabolism, nervous system and the like caused by flora disorder in advance, and has important significance for good care.
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FIG. 1 is a flowchart of a prediction method for evaluating the development age of intestinal flora of infants.
FIG. 2 is a diagram illustrating prediction accuracy in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Examples
A prediction method for estimating the development age of the intestinal flora of infants, as shown in fig. 1, comprising the following steps:
acquiring intestinal flora data of infants;
since the composition of the intestinal micro-organisms in the faeces is changing in real time and is influenced by many different short-term factors (e.g. antibiotic use, probiotic intake, disease state, etc.). Therefore, in order to establish a baseline data set covering the development age span of healthy infants, faeces of healthy infants were collected at 1, 6, 12, 18, 24, 30, 36, 48 months, who received no gut-related disorders (such as constipation or diarrhea) nor immune-activating disorders (such as cold and fever), and had not taken antibiotics and probiotic, prebiotic preparations within one month. When collecting samples, the collection personnel can observe and record the conditions of the infants. Placing the feces into three collecting tubes, storing in dry ice, rapidly returning to a laboratory, placing in a refrigerator at minus 80 ℃, and storing for 2 weeks at normal temperature if the feces are stored in a storage tube with normal temperature storage liquid; if the tube is empty, it must be placed in dry ice or other low temperature environment for no more than 24 hours and transferred to a low temperature freezer or subjected to DNA extraction as soon as possible. Carrying out DNA extraction on a sample, and then carrying out sample preparation; and (4) sequencing the amplicon by using a sequencer after the sample is prepared, and obtaining an amplicon sequencing result. 4. And analyzing the sequencing data of the amplicon to obtain the intestinal flora data.
The extracted DNA from the sample was checked for concentration using a Qubit instrument and the quality was observed by agarose gel electrophoresis. The V4 region of 16S rRNA was selected for amplicon sequencing (515F: 5 '-GTGCCAGCMGCCGCGGTAA-3' for the front primer and 806R:5 '-GGACTACHVGGGTWTCTAAT-3' for the back primer). The primer sequence has a linking sequence at the 3' end of Illumina and a sample recognition sequence with the length of 12 bp.
And respectively acquiring specific data sets of different samples according to the sample identification sequences by sequencing subordinate data. The data were double-ended stitched using FLASH software and low-quality fragments were removed. And the USEARCH method and the GreenGene database are used for removing chimera to improve the data purity. Finally, analysis of the entire flora structure was performed using the QIIME kit.
Constructing a prediction model through linear discriminant analysis and random forests on the basis of the intestinal flora data;
in order to process the classification problem and extract important characteristics, the data to be processed MINdepth-L7 is 525-dimensional 10-class data with a label, supervised preprocessing is carried out on the class data by adopting Linear Discriminant Analysis (LDA) in sequence to obtain class data, and a prediction model capable of carrying out multi-class on related data is obtained by training through a multi-class method of Random Forest (Random Forest).
In order to convert a high-dimensional data set into a more easily processed form, given data is reduced from 525 dimensions to 9 dimensions by using Linear Discriminant Analysis (LDA) in a supervised mode, so that a more efficient data expression form is obtained, and a machine learning model is further trained and predicted on the basis.
In order to complete the data training and prediction work, a classification method which is light and convenient to process missing values (MissingValue) and a random forest are adopted. In the random forest, due to the sparsity of the training data, the proportion of the training data to the test data is divided into 70% and 30%, and the number of the basic classifiers is set to be 200, so that a final prediction model is obtained.
Inputting a sample to be tested into a prediction model for prediction, and outputting classification data to obtain a prediction result; obtaining the development age of the intestinal flora of the sample to be detected according to the prediction result;
in order to further determine the Importance (FeatureImport) of each feature (flora) on the original data set according to the classification data, the new features obtained by 9 LDA conversions are respectively subjected to disorder arrangement operation (Permutation) to obtain disorder arrangement features, then the data obtained by each disorder arrangement operation is reclassified by a random forest again, and the Importance of each disorder arrangement feature is judged according to the difference between the precision of the classification model obtained each time and the precision of the original model, wherein the Importance of the features is called disorder arrangement Importance (Permutation Import).
Based on the out-of-order significance of the 9 new features, we wanted to calculate the significance of 525 original features (flora). Firstly, determining the correlation between the original features and the new features by calculating Pearson correlation coefficients between each original feature and 9 new features, and finally calculating the feature importance of the original features by taking the absolute values of the correlation coefficients between the original features and the new features as weights, wherein the feature importance of the original features is calculated as follows:
wherein, FiCharacteristic importance of the ith original species, pi,jIs the Pearson correlation coefficient between the ith original species and the jth new feature, fjRank importance for the jth new feature out of order;
and comparing the development age of the intestinal flora of the sample to be detected with the actual age, and judging whether the intestinal tracts of the infants are disordered or have development deviation. If the actual age deviation between the predicted age bracket and the sample of the test target individual is less than 12 months, the age bracket is normal; if the deviation is more than 12 months, the flora is dysbiosis, and an intervention scheme needs to be further formulated according to the actual situation.
The prediction results are shown in fig. 2, in which,
1 month (22 persons); b: 6 months (34 persons); c, 12 months (30 persons); d, 18 months (20 persons);
e:24 months (18); f:30 months (9 persons); g:36 months (13 persons); h, 48 months (16 persons);
adult (36-51 years old) (13); y adult (20-27 years old) (22 persons)
197 people in total
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (8)
1. A prediction method for assessing the development age of the gut flora of an infant, comprising the steps of:
acquiring intestinal flora data of infants as original data, and storing the intestinal flora data in a reference data set of a database;
preprocessing the intestinal flora data by linear discriminant analysis based on the intestinal flora data to obtain classified data, and constructing a prediction model by random forest training;
inputting the sample to be tested into a prediction model for prediction to obtain a prediction result, and obtaining the development age of the intestinal flora of the sample to be tested according to the prediction result;
and comparing the development age of the intestinal flora of the sample to be detected with the actual age, and judging whether the intestinal tracts of the infants are disordered or have development deviation.
2. The method of claim 1, wherein the obtaining of the infant gut flora data is as follows: sequencing and analyzing by 16S amplicon sequencing technology, collecting the excreta of healthy infants of 1-48 months for testing, observing the infant condition and recording in a benchmark dataset of a database.
3. The prediction method for evaluating the development age of the intestinal flora of the infant as claimed in claim 2, wherein the intestinal flora data is labeled 525-dimensional 10 classification data, wherein 525-dimensional means that the flora structure is composed of 525 bacterial classification units; the 10-category data includes 8 categories of 1-48 months and two categories consisting of young and middle aged.
4. The prediction method for assessing the development age of the infant intestinal flora according to claim 3, wherein the constructing of the prediction model specifically comprises:
preprocessing the 525-dimensional 10 classified data with labels by linear discriminant analysis based on the intestinal flora data and corresponding sampling age information, namely reducing dimensions to obtain low-dimensional data; and dividing the low-dimensional data into training data and testing data by adopting a random forest, setting the number of basic classifiers as K, and training to obtain a prediction model.
5. The prediction method for evaluating the development age of the infant intestinal flora according to claim 4, wherein the ratio of the training data to the test data is 7: 3; the number K of the basic classifiers is more than 100.
6. The prediction method for assessing the development age of the infant's intestinal flora according to claim 1, wherein the prediction is performed to obtain a prediction result, specifically:
determining the importance of each original feature of an original data set, namely the feature importance of an original flora, according to classification data, performing disorder arrangement operation on new features obtained by linear discriminant analysis conversion to obtain disorder arrangement features, classifying the disorder features by using a random forest again, and judging the importance of each disorder arrangement feature according to the difference between the precision of a prediction model obtained each time and the precision of an original model to obtain the disorder arrangement importance;
calculating a correlation coefficient between each original feature and each disorder arrangement feature, determining the correlation between the original features and the disorder arrangement features, and obtaining a Pearson correlation coefficient absolute value between the original features and the disorder arrangement features as a weight, wherein the feature importance of the original features is calculated as follows:
wherein, FiCharacteristic importance of the ith original species, pi,jBetween the ith original strain and the jth new characteristicPearson correlation coefficient of (f)jThe importance of the j-th new feature is ranked out of order.
7. The method according to claim 1, wherein the age of development of intestinal flora of the infant is determined by comparing the age of development of intestinal flora of the sample with the actual age of the infant, and the method comprises:
if the actual age deviation between the predicted age bracket and the tested target individual is less than N months, the age bracket is normal; if the deviation is more than N months, the flora is dysbiosis, and an intervention scheme needs to be further formulated according to actual conditions.
8. The prediction method for assessing the developmental age of gut flora in infants and young children according to claim 7, wherein N is 12.
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